diff --git a/README.md b/README.md
index 0a60b5a8..07a1b692 100644
--- a/README.md
+++ b/README.md
@@ -41,7 +41,7 @@ The default page of the repo is on `dev` branch. To install the dev version, ple
 This project
 
 - is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to change.
-- requires PyStan as a system dependency. PyStan is licensed under [GPLv3](https://www.gnu.org/licenses/gpl-3.0.html), which is a free, copyleft license for software.
+- requires [cmdstanpy](https://mc-stan.org/cmdstanpy/) as one of the core dependencies for Bayesian sampling.
 
 # Orbit: A Python Package for Bayesian Forecasting
 
diff --git a/docs/about.rst b/docs/about.rst
index 73d79ef6..3f7af02b 100644
--- a/docs/about.rst
+++ b/docs/about.rst
@@ -20,8 +20,8 @@ It also supports the following sampling methods for model estimation:
 -  Stochastic Variational Inference (SVI) as a hybrid-sampling method on approximate
    distribution
    
-Under the hood, the package is leveraging probabilistic program such as `pyro <https://pyro.ai/>`__ and `PyStan 2.0
-<https://pystan2.readthedocs.io/en/latest/>`__.
+Under the hood, the package is leveraging probabilistic program such as `pyro <https://pyro.ai/>`__ and `cmdstanpy
+<https://mc-stan.org/cmdstanpy/>`__.
 
 
 Citation
diff --git a/docs/conf.py b/docs/conf.py
index 4313f822..a62e3d83 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -50,7 +50,7 @@
 ]
 
 # autodoc_mock_imports = [
-#     'pandas', 'torch', 'pystan', 'tqdm', 'matplotlib.pyplot'
+#     'pandas', 'torch', 'cmdstanpy', 'tqdm', 'matplotlib.pyplot'
 #     'pyro', 'matplotlib', 'seaborn', 'scipy', 'sklearn']
 
 # Add any paths that contain templates here, relative to this directory.
diff --git a/docs/tutorials/build_your_own_model.ipynb b/docs/tutorials/build_your_own_model.ipynb
index e3a8e4d4..df27470b 100644
--- a/docs/tutorials/build_your_own_model.ipynb
+++ b/docs/tutorials/build_your_own_model.ipynb
@@ -42,11 +42,11 @@
     "\n",
     "### Model\n",
     "\n",
-    "**Model** is an object defined by a class inherited from `BaseTemplate` a.k.a **Model Template** in the diagram below.  It mainly turns the logic of `fit()` and `predict()` concrete by supplying the `fitter` as a file (**PyStan**) or a callable class (**Pyro**) and the internal `predict()` method. This object defines the overall inputs, model structure, parameters and likelihoods.\n",
+    "**Model** is an object defined by a class inherited from `BaseTemplate` a.k.a **Model Template** in the diagram below.  It mainly turns the logic of `fit()` and `predict()` concrete by supplying the `fitter` as a file (**CmdStanPy**) or a callable class (**Pyro**) and the internal `predict()` method. This object defines the overall inputs, model structure, parameters and likelihoods.\n",
     "\n",
     "### Estimator\n",
     "\n",
-    "Meanwhile, there are different APIs implement slightly different ways of sampling and optimization (for **MAP**). `orbit` is designed to support various APIs such as **PyStan** and **Pyro** (hopefully PyMC3, Numpyro in the future!). The logic separating the call of different APIs with different interface is done by the **Estimator** class which is further inherited in `PyroEstimator` and `StanEstimator`.\n"
+    "Meanwhile, there are different APIs implement slightly different ways of sampling and optimization (for **MAP**). `orbit` is designed to support various APIs such as **CmdStanPy** and **Pyro** (hopefully PyMC3, Numpyro in the future!). The logic separating the call of different APIs with different interface is done by the **Estimator** class which is further inherited in `PyroEstimator` and `StanEstimator`.\n"
    ]
   },
   {
@@ -496,7 +496,7 @@
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x2a5da37c0>"
+       "<orbit.forecaster.svi.SVIForecaster at 0x17f0f6070>"
       ]
      },
      "execution_count": 11,
@@ -531,25 +531,25 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:10:27 - orbit - INFO - Using SVI (Pyro) with steps: 501, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
-      "2024-01-11 22:10:28 - orbit - INFO - step    0 loss = 27333, scale = 0.077497\n",
+      "2024-01-21 13:47:27 - orbit - INFO - Using SVI (Pyro) with steps: 501, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
+      "2024-01-21 13:47:27 - orbit - INFO - step    0 loss = 27333, scale = 0.077497\n",
       "INFO:orbit:step    0 loss = 27333, scale = 0.077497\n",
-      "2024-01-11 22:10:29 - orbit - INFO - step  100 loss = 12594, scale = 0.00924\n",
+      "2024-01-21 13:47:29 - orbit - INFO - step  100 loss = 12594, scale = 0.00924\n",
       "INFO:orbit:step  100 loss = 12594, scale = 0.00924\n",
-      "2024-01-11 22:10:31 - orbit - INFO - step  200 loss = 12596, scale = 0.0094562\n",
+      "2024-01-21 13:47:31 - orbit - INFO - step  200 loss = 12596, scale = 0.0094562\n",
       "INFO:orbit:step  200 loss = 12596, scale = 0.0094562\n",
-      "2024-01-11 22:10:33 - orbit - INFO - step  300 loss = 12591, scale = 0.0092175\n",
+      "2024-01-21 13:47:32 - orbit - INFO - step  300 loss = 12591, scale = 0.0092175\n",
       "INFO:orbit:step  300 loss = 12591, scale = 0.0092175\n",
-      "2024-01-11 22:10:35 - orbit - INFO - step  400 loss = 12594, scale = 0.0095741\n",
+      "2024-01-21 13:47:34 - orbit - INFO - step  400 loss = 12594, scale = 0.0095741\n",
       "INFO:orbit:step  400 loss = 12594, scale = 0.0095741\n",
-      "2024-01-11 22:10:36 - orbit - INFO - step  500 loss = 12591, scale = 0.0095602\n",
+      "2024-01-21 13:47:36 - orbit - INFO - step  500 loss = 12591, scale = 0.0095602\n",
       "INFO:orbit:step  500 loss = 12591, scale = 0.0095602\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x2a5da37c0>"
+       "<orbit.forecaster.svi.SVIForecaster at 0x17f0f6070>"
       ]
      },
      "execution_count": 12,
diff --git a/docs/tutorials/decompose_prediction.ipynb b/docs/tutorials/decompose_prediction.ipynb
index ee92a315..06b97737 100644
--- a/docs/tutorials/decompose_prediction.ipynb
+++ b/docs/tutorials/decompose_prediction.ipynb
@@ -111,13 +111,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:10:53 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
+      "2024-01-21 14:39:49 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x1058475b0>"
+       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x108f9ca60>"
       ]
      },
      "execution_count": 4,
diff --git a/docs/tutorials/dlt.ipynb b/docs/tutorials/dlt.ipynb
index ba36ec12..026d1f42 100644
--- a/docs/tutorials/dlt.ipynb
+++ b/docs/tutorials/dlt.ipynb
@@ -201,7 +201,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:11:09 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
+      "2024-01-21 13:46:06 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n"
      ]
     },
     {
@@ -218,8 +218,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 795 ms, sys: 1.27 s, total: 2.06 s\n",
-      "Wall time: 481 ms\n"
+      "CPU times: user 613 ms, sys: 1.34 s, total: 1.96 s\n",
+      "Wall time: 729 ms\n"
      ]
     }
    ],
@@ -252,13 +252,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:11:10 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
+      "2024-01-21 13:46:06 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x174876ca0>"
+       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x175098400>"
       ]
      },
      "execution_count": 5,
@@ -333,7 +333,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:11:12 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
+      "2024-01-21 13:46:08 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n"
      ]
     },
     {
@@ -350,8 +350,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.01 s, sys: 956 ms, total: 1.97 s\n",
-      "Wall time: 501 ms\n"
+      "CPU times: user 593 ms, sys: 1.38 s, total: 1.97 s\n",
+      "Wall time: 468 ms\n"
      ]
     }
    ],
@@ -403,15 +403,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:11:12 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
+      "2024-01-21 13:46:09 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 280 ms, sys: 110 ms, total: 391 ms\n",
-      "Wall time: 351 ms\n"
+      "CPU times: user 324 ms, sys: 268 ms, total: 592 ms\n",
+      "Wall time: 344 ms\n"
      ]
     },
     {
@@ -479,7 +479,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:11:13 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
+      "2024-01-21 13:46:10 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n"
      ]
     },
     {
@@ -496,8 +496,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.14 s, sys: 941 ms, total: 2.08 s\n",
-      "Wall time: 468 ms\n"
+      "CPU times: user 735 ms, sys: 1.51 s, total: 2.24 s\n",
+      "Wall time: 478 ms\n"
      ]
     }
    ],
@@ -552,7 +552,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:11:14 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
+      "2024-01-21 13:46:10 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
      ]
     },
     {
@@ -684,11 +684,13 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "### High Dimensional and  Fourier Series Regression\n",
     "\n",
-    "In case of high dimensional regression, users can consider fixing the smoothness with a relatively small levels smoothing values e.g. setting `level_sm_input=0.01`. This is particularly useful in modeling higher frequency time-series such as daily and hourly data using regression on Fourier series. Check out the `examples/` folder for more details."
+    "In case of high dimensional regression, users can consider fixing the smoothness with a relatively small levels smoothing values e.g. setting `level_sm_input=0.01`. This is particularly useful in modeling higher frequency time-series such as daily and hourly data using regression on Fourier series. Check out the `examples/` folder for the details."
    ]
   }
  ],
@@ -722,6 +724,11 @@
    "toc_position": {},
    "toc_section_display": true,
    "toc_window_display": true
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "091dbfa208428afaf5ec1bce184d496f586fd9ce3f39e55cfff16a9adc2d3379"
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/ets_lgt_dlt_missing_response.ipynb b/docs/tutorials/ets_lgt_dlt_missing_response.ipynb
index b2365ec5..2d986700 100644
--- a/docs/tutorials/ets_lgt_dlt_missing_response.ipynb
+++ b/docs/tutorials/ets_lgt_dlt_missing_response.ipynb
@@ -297,13 +297,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:12:15 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
+      "2024-01-21 14:38:49 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "675e617a68644e69bc19461f72afcb76",
+       "model_id": "7127326a60cb455a92d2f29cc00a7a3d",
        "version_major": 2,
        "version_minor": 0
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@@ -317,7 +317,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e61ef67e9c1349a391fa6d7dd8049409",
+       "model_id": "788443932419449f9e398df74e8180ab",
        "version_major": 2,
        "version_minor": 0
       },
@@ -331,7 +331,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4b4e1e5d00f0412fb2e3db4fe0f7f877",
+       "model_id": "f75584f0370742288f15096a09a23ee2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -345,7 +345,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a0676da571094a85b5d2f7028395da0a",
+       "model_id": "ee67293605f9471c876b59bd14d63cc0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -397,13 +397,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:12:15 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
+      "2024-01-21 14:38:50 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "62892bd875c846049d52ee7c19c7ec4f",
+       "model_id": "91631a6519be4f9eb191e0655fbac846",
        "version_major": 2,
        "version_minor": 0
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@@ -417,7 +417,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f95443129e8f4ad6b519cde63f110bb7",
+       "model_id": "5117ba9e278f4c93915daa1cade2411e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -431,7 +431,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "42da72d1bdf44ade9718c58a0f720fec",
+       "model_id": "e047da76e6a3451dbfb3895656ccc731",
        "version_major": 2,
        "version_minor": 0
       },
@@ -445,7 +445,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6fa4d95140674159917f351e5cef602e",
+       "model_id": "da8adf7c21ea478ebc9471902d193535",
        "version_major": 2,
        "version_minor": 0
       },
@@ -497,13 +497,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:12:19 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
+      "2024-01-21 14:38:53 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
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+       "model_id": "c51672ad186240cc80ddffdcddff9c3d",
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@@ -517,7 +517,7 @@
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+       "model_id": "956c2e23d6514668bf16bceac75fbbc8",
        "version_major": 2,
        "version_minor": 0
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@@ -531,7 +531,7 @@
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      "data": {
       "application/vnd.jupyter.widget-view+json": {
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+       "model_id": "aa820a1c6a57417cbe4ba807f7d1e063",
        "version_major": 2,
        "version_minor": 0
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@@ -545,7 +545,7 @@
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       "application/vnd.jupyter.widget-view+json": {
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        "version_major": 2,
        "version_minor": 0
       },
@@ -811,7 +811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-12-16T20:11:05.798766Z",
diff --git a/docs/tutorials/ktr1.ipynb b/docs/tutorials/ktr1.ipynb
index 5cd17c09..33848352 100644
--- a/docs/tutorials/ktr1.ipynb
+++ b/docs/tutorials/ktr1.ipynb
@@ -352,24 +352,24 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:13:38 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:13:39 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
+      "2024-01-21 14:13:46 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 14:13:47 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
       "/Users/towinazure/opt/miniconda3/envs/orbit39/lib/python3.9/site-packages/torch/__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/tensor/python_tensor.cpp:453.)\n",
       "  _C._set_default_tensor_type(t)\n",
-      "2024-01-11 22:13:39 - orbit - INFO - step    0 loss = -1946.6, scale = 0.093118\n",
+      "2024-01-21 14:13:47 - orbit - INFO - step    0 loss = -1946.6, scale = 0.093118\n",
       "INFO:orbit:step    0 loss = -1946.6, scale = 0.093118\n",
-      "2024-01-11 22:13:41 - orbit - INFO - step  100 loss = -3131.7, scale = 0.01002\n",
+      "2024-01-21 14:13:49 - orbit - INFO - step  100 loss = -3131.7, scale = 0.01002\n",
       "INFO:orbit:step  100 loss = -3131.7, scale = 0.01002\n",
-      "2024-01-11 22:13:43 - orbit - INFO - step  200 loss = -3119.9, scale = 0.0097664\n",
+      "2024-01-21 14:13:52 - orbit - INFO - step  200 loss = -3119.9, scale = 0.0097664\n",
       "INFO:orbit:step  200 loss = -3119.9, scale = 0.0097664\n",
-      "2024-01-11 22:13:46 - orbit - INFO - step  300 loss = -3132.6, scale = 0.0097929\n",
+      "2024-01-21 14:13:54 - orbit - INFO - step  300 loss = -3132.6, scale = 0.0097929\n",
       "INFO:orbit:step  300 loss = -3132.6, scale = 0.0097929\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x2a63f7f70>"
+       "<orbit.forecaster.svi.SVIForecaster at 0x2a7430e80>"
       ]
      },
      "execution_count": 7,
@@ -627,24 +627,24 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:13:49 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:13:49 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
+      "2024-01-21 14:13:58 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "INFO:orbit:Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 14:13:59 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
       "INFO:orbit:Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
-      "2024-01-11 22:13:49 - orbit - INFO - step    0 loss = -2190.8, scale = 0.093667\n",
+      "2024-01-21 14:13:59 - orbit - INFO - step    0 loss = -2190.8, scale = 0.093667\n",
       "INFO:orbit:step    0 loss = -2190.8, scale = 0.093667\n",
-      "2024-01-11 22:13:51 - orbit - INFO - step  100 loss = -4356.8, scale = 0.0069845\n",
+      "2024-01-21 14:14:01 - orbit - INFO - step  100 loss = -4356.8, scale = 0.0069845\n",
       "INFO:orbit:step  100 loss = -4356.8, scale = 0.0069845\n",
-      "2024-01-11 22:13:53 - orbit - INFO - step  200 loss = -4349.7, scale = 0.0073678\n",
+      "2024-01-21 14:14:03 - orbit - INFO - step  200 loss = -4349.7, scale = 0.0073678\n",
       "INFO:orbit:step  200 loss = -4349.7, scale = 0.0073678\n",
-      "2024-01-11 22:13:55 - orbit - INFO - step  300 loss = -4272.4, scale = 0.0069981\n",
+      "2024-01-21 14:14:05 - orbit - INFO - step  300 loss = -4272.4, scale = 0.0069981\n",
       "INFO:orbit:step  300 loss = -4272.4, scale = 0.0069981\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x340692220>"
+       "<orbit.forecaster.svi.SVIForecaster at 0x34057b040>"
       ]
      },
      "execution_count": 13,
diff --git a/docs/tutorials/ktr2.ipynb b/docs/tutorials/ktr2.ipynb
index 9f566d4c..556b00fe 100644
--- a/docs/tutorials/ktr2.ipynb
+++ b/docs/tutorials/ktr2.ipynb
@@ -687,17 +687,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:14:43 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:14:43 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
+      "2024-01-21 14:13:52 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 14:13:52 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
       "/Users/towinazure/opt/miniconda3/envs/orbit39/lib/python3.9/site-packages/torch/__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/tensor/python_tensor.cpp:453.)\n",
       "  _C._set_default_tensor_type(t)\n",
-      "2024-01-11 22:14:43 - orbit - INFO - step    0 loss = 3107.8, scale = 0.091353\n",
+      "2024-01-21 14:13:53 - orbit - INFO - step    0 loss = 3107.8, scale = 0.091353\n",
       "INFO:orbit:step    0 loss = 3107.8, scale = 0.091353\n",
-      "2024-01-11 22:14:44 - orbit - INFO - step  100 loss = 304.98, scale = 0.049503\n",
+      "2024-01-21 14:13:53 - orbit - INFO - step  100 loss = 304.98, scale = 0.049503\n",
       "INFO:orbit:step  100 loss = 304.98, scale = 0.049503\n",
-      "2024-01-11 22:14:45 - orbit - INFO - step  200 loss = 313.02, scale = 0.052519\n",
+      "2024-01-21 14:13:54 - orbit - INFO - step  200 loss = 313.02, scale = 0.052519\n",
       "INFO:orbit:step  200 loss = 313.02, scale = 0.052519\n",
-      "2024-01-11 22:14:45 - orbit - INFO - step  300 loss = 316.29, scale = 0.052299\n",
+      "2024-01-21 14:13:55 - orbit - INFO - step  300 loss = 316.29, scale = 0.052299\n",
       "INFO:orbit:step  300 loss = 316.29, scale = 0.052299\n"
      ]
     },
@@ -1039,24 +1039,24 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:14:46 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:14:46 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
+      "2024-01-21 14:13:56 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "INFO:orbit:Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 14:13:56 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
       "INFO:orbit:Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
-      "2024-01-11 22:14:46 - orbit - INFO - step    0 loss = 828.02, scale = 0.10882\n",
+      "2024-01-21 14:13:56 - orbit - INFO - step    0 loss = 828.02, scale = 0.10882\n",
       "INFO:orbit:step    0 loss = 828.02, scale = 0.10882\n",
-      "2024-01-11 22:14:46 - orbit - INFO - step  100 loss = 340.58, scale = 0.87797\n",
+      "2024-01-21 14:13:56 - orbit - INFO - step  100 loss = 340.58, scale = 0.87797\n",
       "INFO:orbit:step  100 loss = 340.58, scale = 0.87797\n",
-      "2024-01-11 22:14:47 - orbit - INFO - step  200 loss = 266.67, scale = 0.37411\n",
+      "2024-01-21 14:13:57 - orbit - INFO - step  200 loss = 266.67, scale = 0.37411\n",
       "INFO:orbit:step  200 loss = 266.67, scale = 0.37411\n",
-      "2024-01-11 22:14:47 - orbit - INFO - step  300 loss = 261.21, scale = 0.43775\n",
+      "2024-01-21 14:13:57 - orbit - INFO - step  300 loss = 261.21, scale = 0.43775\n",
       "INFO:orbit:step  300 loss = 261.21, scale = 0.43775\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x2aaabc820>"
+       "<orbit.forecaster.svi.SVIForecaster at 0x2ac72a580>"
       ]
      },
      "execution_count": 14,
diff --git a/docs/tutorials/ktr3.ipynb b/docs/tutorials/ktr3.ipynb
index 50ae7f33..b5807f16 100644
--- a/docs/tutorials/ktr3.ipynb
+++ b/docs/tutorials/ktr3.ipynb
@@ -260,17 +260,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:15:14 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:15:14 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
+      "2024-01-21 14:25:27 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 14:25:27 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
       "/Users/towinazure/opt/miniconda3/envs/orbit39/lib/python3.9/site-packages/torch/__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/tensor/python_tensor.cpp:453.)\n",
       "  _C._set_default_tensor_type(t)\n",
-      "2024-01-11 22:15:14 - orbit - INFO - step    0 loss = 176.47, scale = 0.083093\n",
+      "2024-01-21 14:25:27 - orbit - INFO - step    0 loss = 176.47, scale = 0.083093\n",
       "INFO:orbit:step    0 loss = 176.47, scale = 0.083093\n",
-      "2024-01-11 22:15:15 - orbit - INFO - step  100 loss = 113.08, scale = 0.046374\n",
+      "2024-01-21 14:25:28 - orbit - INFO - step  100 loss = 113.08, scale = 0.046374\n",
       "INFO:orbit:step  100 loss = 113.08, scale = 0.046374\n",
-      "2024-01-11 22:15:16 - orbit - INFO - step  200 loss = 113.14, scale = 0.046119\n",
+      "2024-01-21 14:25:29 - orbit - INFO - step  200 loss = 113.14, scale = 0.046119\n",
       "INFO:orbit:step  200 loss = 113.14, scale = 0.046119\n",
-      "2024-01-11 22:15:17 - orbit - INFO - step  300 loss = 113.21, scale = 0.046233\n",
+      "2024-01-21 14:25:30 - orbit - INFO - step  300 loss = 113.21, scale = 0.046233\n",
       "INFO:orbit:step  300 loss = 113.21, scale = 0.046233\n"
      ]
     },
@@ -338,17 +338,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:15:17 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:15:17 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
+      "2024-01-21 14:25:30 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "INFO:orbit:Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 14:25:30 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
       "INFO:orbit:Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
-      "2024-01-11 22:15:17 - orbit - INFO - step    0 loss = 145.65, scale = 0.088976\n",
+      "2024-01-21 14:25:30 - orbit - INFO - step    0 loss = 145.65, scale = 0.088976\n",
       "INFO:orbit:step    0 loss = 145.65, scale = 0.088976\n",
-      "2024-01-11 22:15:18 - orbit - INFO - step  100 loss = -5.2369, scale = 0.036939\n",
+      "2024-01-21 14:25:31 - orbit - INFO - step  100 loss = -5.2369, scale = 0.036939\n",
       "INFO:orbit:step  100 loss = -5.2369, scale = 0.036939\n",
-      "2024-01-11 22:15:19 - orbit - INFO - step  200 loss = -5.3791, scale = 0.036969\n",
+      "2024-01-21 14:25:32 - orbit - INFO - step  200 loss = -5.3791, scale = 0.036969\n",
       "INFO:orbit:step  200 loss = -5.3791, scale = 0.036969\n",
-      "2024-01-11 22:15:20 - orbit - INFO - step  300 loss = -5.5677, scale = 0.037689\n",
+      "2024-01-21 14:25:33 - orbit - INFO - step  300 loss = -5.5677, scale = 0.037689\n",
       "INFO:orbit:step  300 loss = -5.5677, scale = 0.037689\n"
      ]
     },
@@ -445,24 +445,24 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:15:20 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:15:20 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
+      "2024-01-21 14:25:34 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "INFO:orbit:Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 14:25:34 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
       "INFO:orbit:Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
-      "2024-01-11 22:15:20 - orbit - INFO - step    0 loss = 99.354, scale = 0.096314\n",
+      "2024-01-21 14:25:34 - orbit - INFO - step    0 loss = 99.354, scale = 0.096314\n",
       "INFO:orbit:step    0 loss = 99.354, scale = 0.096314\n",
-      "2024-01-11 22:15:21 - orbit - INFO - step  100 loss = -440.9, scale = 0.027049\n",
+      "2024-01-21 14:25:35 - orbit - INFO - step  100 loss = -440.9, scale = 0.027049\n",
       "INFO:orbit:step  100 loss = -440.9, scale = 0.027049\n",
-      "2024-01-11 22:15:22 - orbit - INFO - step  200 loss = -446.03, scale = 0.028019\n",
+      "2024-01-21 14:25:36 - orbit - INFO - step  200 loss = -446.03, scale = 0.028019\n",
       "INFO:orbit:step  200 loss = -446.03, scale = 0.028019\n",
-      "2024-01-11 22:15:23 - orbit - INFO - step  300 loss = -447.23, scale = 0.029201\n",
+      "2024-01-21 14:25:37 - orbit - INFO - step  300 loss = -447.23, scale = 0.029201\n",
       "INFO:orbit:step  300 loss = -447.23, scale = 0.029201\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x2abfcbd90>"
+       "<orbit.forecaster.svi.SVIForecaster at 0x2ac2b1250>"
       ]
      },
      "execution_count": 11,
diff --git a/docs/tutorials/ktr4.ipynb b/docs/tutorials/ktr4.ipynb
index e51930a4..bb50c173 100644
--- a/docs/tutorials/ktr4.ipynb
+++ b/docs/tutorials/ktr4.ipynb
@@ -305,24 +305,24 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:15:21 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:15:21 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
+      "2024-01-21 14:25:31 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 14:25:31 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
       "/Users/towinazure/opt/miniconda3/envs/orbit39/lib/python3.9/site-packages/torch/__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/tensor/python_tensor.cpp:453.)\n",
       "  _C._set_default_tensor_type(t)\n",
-      "2024-01-11 22:15:21 - orbit - INFO - step    0 loss = -3.5592, scale = 0.085307\n",
+      "2024-01-21 14:25:32 - orbit - INFO - step    0 loss = -3.5592, scale = 0.085307\n",
       "INFO:orbit:step    0 loss = -3.5592, scale = 0.085307\n",
-      "2024-01-11 22:15:22 - orbit - INFO - step  100 loss = -228.72, scale = 0.036569\n",
+      "2024-01-21 14:25:32 - orbit - INFO - step  100 loss = -228.72, scale = 0.036569\n",
       "INFO:orbit:step  100 loss = -228.72, scale = 0.036569\n",
-      "2024-01-11 22:15:23 - orbit - INFO - step  200 loss = -228.83, scale = 0.037738\n",
+      "2024-01-21 14:25:33 - orbit - INFO - step  200 loss = -228.83, scale = 0.037738\n",
       "INFO:orbit:step  200 loss = -228.83, scale = 0.037738\n",
-      "2024-01-11 22:15:24 - orbit - INFO - step  300 loss = -229.75, scale = 0.037871\n",
+      "2024-01-21 14:25:34 - orbit - INFO - step  300 loss = -229.75, scale = 0.037871\n",
       "INFO:orbit:step  300 loss = -229.75, scale = 0.037871\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x2a8de1b20>"
+       "<orbit.forecaster.svi.SVIForecaster at 0x2a79010a0>"
       ]
      },
      "execution_count": 5,
@@ -431,24 +431,24 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:15:24 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:15:24 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
+      "2024-01-21 14:25:35 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "INFO:orbit:Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 14:25:35 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
       "INFO:orbit:Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
-      "2024-01-11 22:15:24 - orbit - INFO - step    0 loss = 9.7371, scale = 0.10482\n",
+      "2024-01-21 14:25:35 - orbit - INFO - step    0 loss = 9.7371, scale = 0.10482\n",
       "INFO:orbit:step    0 loss = 9.7371, scale = 0.10482\n",
-      "2024-01-11 22:15:25 - orbit - INFO - step  100 loss = -231.22, scale = 0.41649\n",
+      "2024-01-21 14:25:36 - orbit - INFO - step  100 loss = -231.22, scale = 0.41649\n",
       "INFO:orbit:step  100 loss = -231.22, scale = 0.41649\n",
-      "2024-01-11 22:15:26 - orbit - INFO - step  200 loss = -230.89, scale = 0.426\n",
+      "2024-01-21 14:25:36 - orbit - INFO - step  200 loss = -230.89, scale = 0.426\n",
       "INFO:orbit:step  200 loss = -230.89, scale = 0.426\n",
-      "2024-01-11 22:15:26 - orbit - INFO - step  300 loss = -229.25, scale = 0.4185\n",
+      "2024-01-21 14:25:37 - orbit - INFO - step  300 loss = -229.25, scale = 0.4185\n",
       "INFO:orbit:step  300 loss = -229.25, scale = 0.4185\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x2ad2bddf0>"
+       "<orbit.forecaster.svi.SVIForecaster at 0x2aaefec70>"
       ]
      },
      "execution_count": 8,
@@ -623,24 +623,24 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:15:27 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:15:27 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
+      "2024-01-21 14:25:38 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "INFO:orbit:Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 14:25:38 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
       "INFO:orbit:Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
-      "2024-01-11 22:15:27 - orbit - INFO - step    0 loss = -5741.9, scale = 0.094521\n",
+      "2024-01-21 14:25:38 - orbit - INFO - step    0 loss = -5741.9, scale = 0.094521\n",
       "INFO:orbit:step    0 loss = -5741.9, scale = 0.094521\n",
-      "2024-01-11 22:15:29 - orbit - INFO - step  100 loss = -7140.3, scale = 0.31416\n",
+      "2024-01-21 14:25:39 - orbit - INFO - step  100 loss = -7140.3, scale = 0.31416\n",
       "INFO:orbit:step  100 loss = -7140.3, scale = 0.31416\n",
-      "2024-01-11 22:15:31 - orbit - INFO - step  200 loss = -7140, scale = 0.31681\n",
+      "2024-01-21 14:25:41 - orbit - INFO - step  200 loss = -7140, scale = 0.31681\n",
       "INFO:orbit:step  200 loss = -7140, scale = 0.31681\n",
-      "2024-01-11 22:15:33 - orbit - INFO - step  300 loss = -7140.6, scale = 0.3307\n",
+      "2024-01-21 14:25:43 - orbit - INFO - step  300 loss = -7140.6, scale = 0.3307\n",
       "INFO:orbit:step  300 loss = -7140.6, scale = 0.3307\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x2ae506400>"
+       "<orbit.forecaster.svi.SVIForecaster at 0x2ac1539d0>"
       ]
      },
      "execution_count": 14,
diff --git a/docs/tutorials/lgt.ipynb b/docs/tutorials/lgt.ipynb
index 59bcb0a7..d213cf1c 100644
--- a/docs/tutorials/lgt.ipynb
+++ b/docs/tutorials/lgt.ipynb
@@ -185,7 +185,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:15:29 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
+      "2024-01-21 14:01:47 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n"
      ]
     }
    ],
@@ -213,14 +213,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 10.5 ms, sys: 4.92 ms, total: 15.4 ms\n",
-      "Wall time: 112 ms\n"
+      "CPU times: user 12.9 ms, sys: 6.6 ms, total: 19.5 ms\n",
+      "Wall time: 116 ms\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.map.MAPForecaster at 0x106986b50>"
+       "<orbit.forecaster.map.MAPForecaster at 0x2aa460df0>"
       ]
      },
      "execution_count": 5,
@@ -333,21 +333,21 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:15:29 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
+      "2024-01-21 14:01:47 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 126 ms, sys: 158 ms, total: 283 ms\n",
-      "Wall time: 4.49 s\n"
+      "CPU times: user 113 ms, sys: 94 ms, total: 207 ms\n",
+      "Wall time: 4.16 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x1761c3760>"
+       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x1726d6160>"
       ]
      },
      "execution_count": 9,
@@ -562,21 +562,21 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:15:34 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
+      "2024-01-21 14:01:52 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 84.6 ms, sys: 22 ms, total: 107 ms\n",
-      "Wall time: 4.18 s\n"
+      "CPU times: user 90.2 ms, sys: 74.3 ms, total: 165 ms\n",
+      "Wall time: 4.14 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x2a6843700>"
+       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x2aa7062b0>"
       ]
      },
      "execution_count": 15,
diff --git a/docs/tutorials/model_diagnostics.ipynb b/docs/tutorials/model_diagnostics.ipynb
index 0bb20f81..6723b775 100644
--- a/docs/tutorials/model_diagnostics.ipynb
+++ b/docs/tutorials/model_diagnostics.ipynb
@@ -290,13 +290,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:15:59 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 500 and samples(per chain): 500.\n"
+      "2024-01-21 14:01:25 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 500 and samples(per chain): 500.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x2978734f0>"
+       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x29d732610>"
       ]
      },
      "execution_count": 7,
@@ -471,7 +471,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:16:09 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 500 and samples(per chain): 500.\n"
+      "2024-01-21 14:01:34 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 500 and samples(per chain): 500.\n"
      ]
     }
    ],
diff --git a/docs/tutorials/model_estimations_predictions.ipynb b/docs/tutorials/model_estimations_predictions.ipynb
index 895c97a0..449401b7 100644
--- a/docs/tutorials/model_estimations_predictions.ipynb
+++ b/docs/tutorials/model_estimations_predictions.ipynb
@@ -17,7 +17,7 @@
     "2. Markov Chain Monte Carlo (MCMC)\n",
     "3. Stochastic Variational Inference (SVI)\n",
     "\n",
-    "This session will cover the first two: **MAP** and **MCMC** which mainly uses [PyStan2.0](https://pystan2.readthedocs.io/en/latest/) at the back end. Users can simply can leverage the args `estimator` to pick the method (`stan-map` and `stan-mcmc`). The details will be covered by the sections below. The SVI method is calling [Pyro](https://pyro.ai/) by specifying `estimator='pyro-svi'`. However, it is covered by a separate session."
+    "This session will cover the first two: **MAP** and **MCMC** which mainly uses [CmdStanPy](https://mc-stan.org/cmdstanpy/) at the back end. Users can simply can leverage the args `estimator` to pick the method (`stan-map` and `stan-mcmc`). The details will be covered by the sections below. The SVI method is calling [Pyro](https://pyro.ai/) by specifying `estimator='pyro-svi'`. However, it is covered by a separate session."
    ]
   },
   {
@@ -112,14 +112,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:16:04 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
+      "2024-01-21 13:58:33 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 10.5 ms, sys: 8.45 ms, total: 18.9 ms\n",
+      "CPU times: user 10.3 ms, sys: 12.7 ms, total: 23 ms\n",
       "Wall time: 30.4 ms\n"
      ]
     }
@@ -183,7 +183,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:16:05 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
+      "2024-01-21 13:58:34 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n"
      ]
     },
     {
@@ -304,15 +304,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:16:05 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 100 and samples(per chain): 100.\n"
+      "2024-01-21 13:58:34 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 100 and samples(per chain): 100.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 157 ms, sys: 35 ms, total: 192 ms\n",
-      "Wall time: 605 ms\n"
+      "CPU times: user 152 ms, sys: 23.7 ms, total: 175 ms\n",
+      "Wall time: 589 ms\n"
      ]
     }
    ],
@@ -482,15 +482,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:16:06 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
+      "2024-01-21 13:58:35 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 208 ms, sys: 83.6 ms, total: 292 ms\n",
-      "Wall time: 776 ms\n"
+      "CPU times: user 205 ms, sys: 89.7 ms, total: 294 ms\n",
+      "Wall time: 766 ms\n"
      ]
     }
    ],
diff --git a/docs/tutorials/other_utilities.ipynb b/docs/tutorials/other_utilities.ipynb
index c8b511a7..e36462c4 100644
--- a/docs/tutorials/other_utilities.ipynb
+++ b/docs/tutorials/other_utilities.ipynb
@@ -119,31 +119,31 @@
        "      <th>0</th>\n",
        "      <td>x0</td>\n",
        "      <td>2020-01-31</td>\n",
-       "      <td>0.154111</td>\n",
+       "      <td>0.646241</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>x0</td>\n",
        "      <td>2020-02-29</td>\n",
-       "      <td>1.496860</td>\n",
+       "      <td>-0.207489</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>x0</td>\n",
        "      <td>2020-03-31</td>\n",
-       "      <td>0.107068</td>\n",
+       "      <td>0.141275</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>x0</td>\n",
        "      <td>2020-04-30</td>\n",
-       "      <td>0.611659</td>\n",
+       "      <td>-0.492888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>x0</td>\n",
        "      <td>2020-05-31</td>\n",
-       "      <td>2.453136</td>\n",
+       "      <td>1.287505</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -151,11 +151,11 @@
       ],
       "text/plain": [
        "  key         dt         x\n",
-       "0  x0 2020-01-31  0.154111\n",
-       "1  x0 2020-02-29  1.496860\n",
-       "2  x0 2020-03-31  0.107068\n",
-       "3  x0 2020-04-30  0.611659\n",
-       "4  x0 2020-05-31  2.453136"
+       "0  x0 2020-01-31  0.646241\n",
+       "1  x0 2020-02-29 -0.207489\n",
+       "2  x0 2020-03-31  0.141275\n",
+       "3  x0 2020-04-30 -0.492888\n",
+       "4  x0 2020-05-31  1.287505"
       ]
      },
      "execution_count": 3,
@@ -234,31 +234,31 @@
        "      <th>0</th>\n",
        "      <td>x0</td>\n",
        "      <td>2020-01-31</td>\n",
-       "      <td>0.154111</td>\n",
+       "      <td>0.646241</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>x0</td>\n",
        "      <td>2020-02-29</td>\n",
-       "      <td>1.496860</td>\n",
+       "      <td>-0.207489</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>x0</td>\n",
        "      <td>2020-03-31</td>\n",
-       "      <td>0.107068</td>\n",
+       "      <td>0.141275</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>x0</td>\n",
        "      <td>2020-04-30</td>\n",
-       "      <td>0.611659</td>\n",
+       "      <td>-0.492888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>x0</td>\n",
        "      <td>2020-05-31</td>\n",
-       "      <td>2.453136</td>\n",
+       "      <td>1.287505</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -266,11 +266,11 @@
       ],
       "text/plain": [
        "  key         dt         x\n",
-       "0  x0 2020-01-31  0.154111\n",
-       "1  x0 2020-02-29  1.496860\n",
-       "2  x0 2020-03-31  0.107068\n",
-       "3  x0 2020-04-30  0.611659\n",
-       "4  x0 2020-05-31  2.453136"
+       "0  x0 2020-01-31  0.646241\n",
+       "1  x0 2020-02-29 -0.207489\n",
+       "2  x0 2020-03-31  0.141275\n",
+       "3  x0 2020-04-30 -0.492888\n",
+       "4  x0 2020-05-31  1.287505"
       ]
      },
      "execution_count": 4,
diff --git a/docs/tutorials/pyro_basic.ipynb b/docs/tutorials/pyro_basic.ipynb
index d756db23..929193f4 100644
--- a/docs/tutorials/pyro_basic.ipynb
+++ b/docs/tutorials/pyro_basic.ipynb
@@ -160,14 +160,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:16:17 - orbit - INFO - Using SVI (Pyro) with steps: 101, samples: 300, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
+      "2024-01-21 13:53:57 - orbit - INFO - Using SVI (Pyro) with steps: 101, samples: 300, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
       "/Users/towinazure/opt/miniconda3/envs/orbit39/lib/python3.9/site-packages/torch/__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/tensor/python_tensor.cpp:453.)\n",
       "  _C._set_default_tensor_type(t)\n",
-      "2024-01-11 22:16:18 - orbit - INFO - step    0 loss = 658.91, scale = 0.11635\n",
+      "2024-01-21 13:53:57 - orbit - INFO - step    0 loss = 658.91, scale = 0.11635\n",
       "INFO:orbit:step    0 loss = 658.91, scale = 0.11635\n",
-      "2024-01-11 22:16:21 - orbit - INFO - step   50 loss = -432, scale = 0.48623\n",
+      "2024-01-21 13:54:01 - orbit - INFO - step   50 loss = -432, scale = 0.48623\n",
       "INFO:orbit:step   50 loss = -432, scale = 0.48623\n",
-      "2024-01-11 22:16:24 - orbit - INFO - step  100 loss = -444.07, scale = 0.34976\n",
+      "2024-01-21 13:54:04 - orbit - INFO - step  100 loss = -444.07, scale = 0.34976\n",
       "INFO:orbit:step  100 loss = -444.07, scale = 0.34976\n"
      ]
     },
@@ -175,14 +175,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 6.38 s, sys: 609 ms, total: 6.99 s\n",
-      "Wall time: 6.66 s\n"
+      "CPU times: user 6.9 s, sys: 652 ms, total: 7.55 s\n",
+      "Wall time: 7.62 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x2a72c6df0>"
+       "<orbit.forecaster.svi.SVIForecaster at 0x2a9885fd0>"
       ]
      },
      "execution_count": 6,
diff --git a/docs/tutorials/quick_start.ipynb b/docs/tutorials/quick_start.ipynb
index 0ec2d527..d89e6ba6 100644
--- a/docs/tutorials/quick_start.ipynb
+++ b/docs/tutorials/quick_start.ipynb
@@ -275,7 +275,7 @@
    "metadata": {},
    "source": [
     "`Orbit` aims to provide an intuitive **initialize-fit-predict** interface for working with forecasting tasks. Under the hood, it utilizes probabilistic modeling API such as\n",
-    "`PyStan` and `Pyro`. We first illustrate a Bayesian implementation of Rob Hyndman's ETS (which stands for Error, Trend, and Seasonality) Model [(Hyndman et. al, 2008)](http://www.exponentialsmoothing.net/home) using `PyStan`."
+    "`CmdStanPy` and `Pyro`. We first illustrate a Bayesian implementation of Rob Hyndman's ETS (which stands for Error, Trend, and Seasonality) Model [(Hyndman et. al, 2008)](http://www.exponentialsmoothing.net/home) using `CmdStanPy`."
    ]
   },
   {
@@ -313,21 +313,21 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:09:21 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
+      "2024-01-21 13:34:09 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 50.8 ms, sys: 24.1 ms, total: 74.9 ms\n",
-      "Wall time: 657 ms\n"
+      "CPU times: user 54.9 ms, sys: 30.4 ms, total: 85.3 ms\n",
+      "Wall time: 1.27 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x16ddca820>"
+       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x2870b7820>"
       ]
      },
      "execution_count": 7,
diff --git a/docs/tutorials/regression_penalty.ipynb b/docs/tutorials/regression_penalty.ipynb
index 9a635d57..ae6efe22 100644
--- a/docs/tutorials/regression_penalty.ipynb
+++ b/docs/tutorials/regression_penalty.ipynb
@@ -258,13 +258,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:16:28 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 1000 and samples(per chain): 25.\n"
+      "2024-01-21 13:53:50 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 1000 and samples(per chain): 25.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b65fc9c79094404d8602ad02bd8a80a3",
+       "model_id": "59ad6c2a73234495b29fd54aa75bcf41",
        "version_major": 2,
        "version_minor": 0
       },
@@ -278,7 +278,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "888cf9c33e8c43629956085d7f76835a",
+       "model_id": "6e5433ae14fb496a9ee3be48e7e5a60c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -292,7 +292,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9fa1abb26974426692a2f4c62aefecb9",
+       "model_id": "15d0142b36794d2d9dc752ce18da776f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -306,7 +306,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "511da444e06a4017830a4b8ce70f4998",
+       "model_id": "6f092429f0434e73a7b78c1fd92499e2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -322,14 +322,14 @@
      "output_type": "stream",
      "text": [
       "                                                                                                                                                                                                                                                                                                                                \n",
-      "CPU times: user 47.3 ms, sys: 39.3 ms, total: 86.6 ms\n",
-      "Wall time: 1.04 s\n"
+      "CPU times: user 48.8 ms, sys: 33.8 ms, total: 82.7 ms\n",
+      "Wall time: 1.06 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x29dcecac0>"
+       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x29d3d3e80>"
       ]
      },
      "execution_count": 8,
@@ -416,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-08-20T23:41:29.092925Z",
@@ -428,13 +428,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:16:30 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 1000 and samples(per chain): 25.\n"
+      "2024-01-21 13:53:52 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 1000 and samples(per chain): 25.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cbdee9d1d0084c7db6a6994eaa5d7710",
+       "model_id": "79c1098aefd645bfaef6892fc5466fed",
        "version_major": 2,
        "version_minor": 0
       },
@@ -448,7 +448,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "498b31cc6ca249c49d9b7b9fa5f841b2",
+       "model_id": "38f1dde0db2947ddbee09b1647099688",
        "version_major": 2,
        "version_minor": 0
       },
@@ -462,7 +462,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "60d6396432534331b40ae7ca574c1cbe",
+       "model_id": "f5e5c5fb204940c9969f9b54d0215115",
        "version_major": 2,
        "version_minor": 0
       },
@@ -476,7 +476,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b430f9ec718d43429ec99fc71de3d1fd",
+       "model_id": "e2dc1124b2ee4230981500d3171fbed0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -492,14 +492,14 @@
      "output_type": "stream",
      "text": [
       "                                                                                                                                                                                                                                                                                                                                \n",
-      "CPU times: user 58.1 ms, sys: 72.2 ms, total: 130 ms\n",
-      "Wall time: 1.63 s\n"
+      "CPU times: user 64.2 ms, sys: 98 ms, total: 162 ms\n",
+      "Wall time: 1.69 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x29debcf40>"
+       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x29e5b1e80>"
       ]
      },
      "execution_count": 10,
@@ -526,7 +526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-08-20T23:41:29.312900Z",
@@ -561,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-08-20T23:41:29.317298Z",
@@ -595,7 +595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-08-20T23:41:29.329485Z",
@@ -626,7 +626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-08-20T23:41:29.336311Z",
@@ -661,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-08-20T23:41:29.341796Z",
@@ -676,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-08-20T23:41:29.355537Z",
@@ -717,7 +717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-08-20T23:41:41.014941Z",
@@ -729,13 +729,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:16:32 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 2000 and samples(per chain): 25.\n"
+      "2024-01-21 13:53:53 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 2000 and samples(per chain): 25.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3073254596bd49b4a8bf18ab4b804236",
+       "model_id": "9ad371973c03449284aa296e3c9660a2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -749,7 +749,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ca78d15268434abaa6cf69e117b74542",
+       "model_id": "53350c01bcaf4c79ade5a431b9bafec0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -763,7 +763,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eeb96dadee694e37b626ad90c5ccb384",
+       "model_id": "1fd675804bb1484fa36d24ba1295de50",
        "version_major": 2,
        "version_minor": 0
       },
@@ -777,7 +777,7 @@
     {
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e3354119fee644f79f8be93ce2971edc",
+       "model_id": "46d32c36126b413491fe07671548972e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -798,7 +798,7 @@
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x29df91f40>"
+       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x29e686850>"
       ]
      },
      "execution_count": 17,
@@ -821,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-08-20T23:41:41.275538Z",
@@ -864,7 +864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-08-20T23:41:48.887761Z",
@@ -876,13 +876,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:16:36 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 2000 and samples(per chain): 25.\n"
+      "2024-01-21 13:53:58 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 2000 and samples(per chain): 25.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "181bd3041bc44478a22390f91f07ca78",
+       "model_id": "39805c343798477daaa4accd7da2e1a8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -896,7 +896,7 @@
     {
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       "application/vnd.jupyter.widget-view+json": {
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        "version_major": 2,
        "version_minor": 0
       },
@@ -910,7 +910,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e3da12a299d54a7ab050e1249d2fb27d",
+       "model_id": "84fb1bd204e24740b0da911ab590d1da",
        "version_major": 2,
        "version_minor": 0
       },
@@ -924,7 +924,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fe4bb60bea87408989201b6241b4604a",
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        "version_major": 2,
        "version_minor": 0
       },
@@ -945,7 +945,7 @@
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x29dfcc910>"
+       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x29e748e80>"
       ]
      },
      "execution_count": 19,
@@ -969,7 +969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-08-20T23:41:49.105834Z",
@@ -1003,7 +1003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-08-20T23:41:49.110576Z",
diff --git a/docs/tutorials/regression_prior.ipynb b/docs/tutorials/regression_prior.ipynb
index e33c39eb..a3c9c748 100644
--- a/docs/tutorials/regression_prior.ipynb
+++ b/docs/tutorials/regression_prior.ipynb
@@ -38,8 +38,7 @@
     "import orbit\n",
     "from orbit.utils.dataset import load_iclaims\n",
     "from orbit.models import DLT\n",
-    "from orbit.diagnostics.plot import plot_predicted_data\n",
-    "from orbit.constants.palette import OrbitPalette"
+    "from orbit.diagnostics.plot import plot_predicted_data"
    ]
   },
   {
@@ -325,72 +324,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:17:07 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 1000 and samples(per chain): 25.\n"
+      "2024-01-21 13:53:22 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 1000 and samples(per chain): 25.\n"
      ]
     },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bc2a3ff9239e45dea4e4390b8db1f416",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 1 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4675698fb4ec4f82b5d10dbd5be04419",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 2 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "58df3c72986c4ce285e19753dd98f6ce",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 3 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f0c6050d0c1c4c649ef14958964ba285",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 4 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                                                                                                                                                                                                                                                                                                                                \n",
-      "CPU times: user 208 ms, sys: 56.6 ms, total: 265 ms\n",
-      "Wall time: 5.98 s\n"
+      "CPU times: user 136 ms, sys: 31.3 ms, total: 167 ms\n",
+      "Wall time: 5.86 s\n"
      ]
     }
    ],
@@ -402,6 +344,7 @@
     "    seasonality=52,\n",
     "    seed=8888,\n",
     "    num_warmup=4000,\n",
+    "    stan_mcmc_args={'show_progress': False}\n",
     ")\n",
     "dlt.fit(df=train_df)\n",
     "predicted_df = dlt.predict(df=test_df)"
@@ -451,72 +394,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:17:13 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 1000 and samples(per chain): 25.\n"
+      "2024-01-21 13:53:28 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 1000 and samples(per chain): 25.\n"
      ]
     },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "57888460aad8481384a72a5fd6032ecb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 1 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4ee0dba838ed40509c2ba909378da631",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 2 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "db0b093e54dc4a2a857e59b882ffb8c6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 3 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "68ac1b1da6474ed8b429bd3917988a56",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 4 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                                                                                                                                                                                                                                                                                                                                \n",
-      "CPU times: user 181 ms, sys: 33.2 ms, total: 214 ms\n",
-      "Wall time: 6.08 s\n"
+      "CPU times: user 143 ms, sys: 23.3 ms, total: 167 ms\n",
+      "Wall time: 6.25 s\n"
      ]
     }
    ],
@@ -529,6 +415,7 @@
     "    seasonality=52,\n",
     "    seed=8888,\n",
     "    num_warmup=4000,\n",
+    "    stan_mcmc_args={'show_progress': False}\n",
     ")\n",
     "dlt_reg.fit(df=train_df)\n",
     "predicted_df_reg = dlt_reg.predict(test_df)"
@@ -680,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-04-07T18:49:21.970562Z",
@@ -692,64 +579,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:17:19 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 1000 and samples(per chain): 250.\n"
+      "2024-01-21 13:53:34 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 1000 and samples(per chain): 250.\n"
      ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "497574110cc4487e9ccf4ff7f9df2bcf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 1 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1d9f630a7d164c0c9d45d6f94af4bb2e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 2 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1d53d578ecde4f76867e882e2c4b3ab3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 3 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d8c44b06b50141558fc86dc8321b5b8e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 4 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -763,6 +594,7 @@
     "    num_sample=1000,\n",
     "    estimator='stan-mcmc',\n",
     "    seed=2022,\n",
+    "    stan_mcmc_args={'show_progress': False}\n",
     ")\n",
     "dlt_reg_adjust.fit(df=train_df)\n",
     "predicted_df_reg_adjust = dlt_reg_adjust.predict(test_df)"
@@ -770,14 +602,108 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-04-07T18:49:21.986924Z",
      "start_time": "2022-04-07T18:49:21.972519Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>regressor</th>\n",
+       "      <th>regressor_sign</th>\n",
+       "      <th>coefficient</th>\n",
+       "      <th>coefficient_lower</th>\n",
+       "      <th>coefficient_upper</th>\n",
+       "      <th>Pr(coef &gt;= 0)</th>\n",
+       "      <th>Pr(coef &lt; 0)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>trend.unemploy</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>0.126584</td>\n",
+       "      <td>0.075630</td>\n",
+       "      <td>0.198016</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>vix</td>\n",
+       "      <td>Positive</td>\n",
+       "      <td>0.019553</td>\n",
+       "      <td>0.002202</td>\n",
+       "      <td>0.054368</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sp500</td>\n",
+       "      <td>Negative</td>\n",
+       "      <td>-0.032251</td>\n",
+       "      <td>-0.087838</td>\n",
+       "      <td>-0.002386</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>trend.job</td>\n",
+       "      <td>Regular</td>\n",
+       "      <td>-0.011294</td>\n",
+       "      <td>-0.086100</td>\n",
+       "      <td>0.058422</td>\n",
+       "      <td>0.394</td>\n",
+       "      <td>0.606</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        regressor regressor_sign  coefficient  coefficient_lower  \\\n",
+       "0  trend.unemploy       Positive     0.126584           0.075630   \n",
+       "1             vix       Positive     0.019553           0.002202   \n",
+       "2           sp500       Negative    -0.032251          -0.087838   \n",
+       "3       trend.job        Regular    -0.011294          -0.086100   \n",
+       "\n",
+       "   coefficient_upper  Pr(coef >= 0)  Pr(coef < 0)  \n",
+       "0           0.198016          1.000         0.000  \n",
+       "1           0.054368          1.000         0.000  \n",
+       "2          -0.002386          0.000         1.000  \n",
+       "3           0.058422          0.394         0.606  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "dlt_reg_adjust.get_regression_coefs()"
    ]
@@ -791,14 +717,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-04-07T18:49:21.994703Z",
      "start_time": "2022-04-07T18:49:21.988876Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------Mean Absolute Error Summary----------------\n",
+      "Naive Model: 0.255\n",
+      "Regression Model: 0.242\n",
+      "Refined Regression Model: 0.096\n"
+     ]
+    }
+   ],
    "source": [
     "def mae(x, y):\n",
     "    return np.mean(np.abs(x - y))\n",
diff --git a/docs/tutorials/residual_diagnostic.ipynb b/docs/tutorials/residual_diagnostic.ipynb
index 348cccb5..3c2c0f00 100644
--- a/docs/tutorials/residual_diagnostic.ipynb
+++ b/docs/tutorials/residual_diagnostic.ipynb
@@ -254,7 +254,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:30:39 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
+      "2024-01-21 13:50:54 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n"
      ]
     }
    ],
diff --git a/docs/tutorials/set_random_seed.ipynb b/docs/tutorials/set_random_seed.ipynb
index d3d7b3d4..938b8265 100644
--- a/docs/tutorials/set_random_seed.ipynb
+++ b/docs/tutorials/set_random_seed.ipynb
@@ -220,8 +220,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:17:53 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:17:53 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
+      "2024-01-21 13:51:04 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 13:51:04 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n"
      ]
     }
    ],
@@ -296,8 +296,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:17:53 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:17:53 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
+      "2024-01-21 13:51:05 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 13:51:05 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n"
      ]
     }
    ],
@@ -351,8 +351,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:17:53 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n",
-      "2024-01-11 22:17:58 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
+      "2024-01-21 13:51:05 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n",
+      "2024-01-21 13:51:10 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
      ]
     }
    ],
diff --git a/docs/tutorials/utilities_simulation.ipynb b/docs/tutorials/utilities_simulation.ipynb
index 29025110..baa39b34 100644
--- a/docs/tutorials/utilities_simulation.ipynb
+++ b/docs/tutorials/utilities_simulation.ipynb
@@ -123,7 +123,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/docs/tutorials/wbic.ipynb b/docs/tutorials/wbic.ipynb
index e81f6ff2..390e87cf 100644
--- a/docs/tutorials/wbic.ipynb
+++ b/docs/tutorials/wbic.ipynb
@@ -137,18 +137,18 @@
      "text": [
       "(365, 12)\n",
       "          y        x1        x2        x3        x4        x5        x6  \\\n",
-      "0  4.426242  0.172792  0.000000  0.165219 -0.000000 -0.498404  0.999915   \n",
-      "1  5.580432  0.452678  0.223187 -0.000000  0.290559 -0.848345 -0.497851   \n",
-      "2  5.031773  0.182286  0.147066  0.014211  0.273356  1.191872 -0.824569   \n",
-      "3  3.264027 -0.368227 -0.081455 -0.241060  0.299423 -1.207208  1.777036   \n",
-      "4  5.246511  0.019861 -0.146228 -0.390954 -0.128596 -0.679806  0.624098   \n",
+      "0  4.426242  0.172792  0.000000  0.165219 -0.000000 -0.479769  0.240312   \n",
+      "1  5.580432  0.452678  0.223187 -0.000000  0.290559 -0.970166 -1.296340   \n",
+      "2  5.031773  0.182286  0.147066  0.014211  0.273356 -0.403479 -0.382158   \n",
+      "3  3.264027 -0.368227 -0.081455 -0.241060  0.299423 -0.093035  0.447098   \n",
+      "4  5.246511  0.019861 -0.146228 -0.390954 -0.128596  0.902981 -0.622541   \n",
       "\n",
       "         x7        x8        x9       x10       date  \n",
-      "0 -0.051517 -0.539590 -0.947974  0.722555 2016-01-10  \n",
-      "1 -1.462154  1.038089  0.160666  0.765847 2016-01-17  \n",
-      "2  1.090851  0.195800  1.985481  1.443468 2016-01-24  \n",
-      "3 -0.151839 -0.495828  0.157189 -0.135686 2016-01-31  \n",
-      "4  0.334603 -0.053634 -1.879043 -0.576510 2016-02-07  \n"
+      "0  0.781391 -0.104744 -0.790966  0.245078 2016-01-10  \n",
+      "1  0.841649  0.024343  0.694507 -0.141609 2016-01-17  \n",
+      "2 -0.090071  2.223643 -0.060319 -1.234368 2016-01-24  \n",
+      "3 -0.736534  1.631389 -0.426342  0.446477 2016-01-31  \n",
+      "4 -3.756670  0.095822 -0.084870 -0.057588 2016-02-07  \n"
      ]
     }
    ],
@@ -185,8 +185,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:18:17 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n",
-      "2024-01-11 22:18:29 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
+      "2024-01-21 13:47:51 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n",
+      "2024-01-21 13:48:03 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
      ]
     },
     {
@@ -201,7 +201,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:18:41 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
+      "2024-01-21 13:48:16 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
      ]
     },
     {
@@ -216,7 +216,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:18:53 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
+      "2024-01-21 13:48:28 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
      ]
     },
     {
@@ -231,7 +231,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:19:05 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
+      "2024-01-21 13:48:40 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
      ]
     },
     {
@@ -246,14 +246,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:19:17 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
+      "2024-01-21 13:48:52 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WBIC value with 5 regressors: 1057.433\n",
+      "WBIC value with 5 regressors: 1060.191\n",
       "------------------------------------------------------------------\n"
      ]
     },
@@ -261,14 +261,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:19:30 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
+      "2024-01-21 13:49:05 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WBIC value with 6 regressors: 1062.740\n",
+      "WBIC value with 6 regressors: 1066.616\n",
       "------------------------------------------------------------------\n"
      ]
     },
@@ -276,14 +276,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:19:43 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
+      "2024-01-21 13:49:18 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WBIC value with 7 regressors: 1068.295\n",
+      "WBIC value with 7 regressors: 1072.969\n",
       "------------------------------------------------------------------\n"
      ]
     },
@@ -291,14 +291,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:19:55 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
+      "2024-01-21 13:49:30 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WBIC value with 8 regressors: 1075.207\n",
+      "WBIC value with 8 regressors: 1079.379\n",
       "------------------------------------------------------------------\n"
      ]
     },
@@ -306,19 +306,19 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:20:07 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
+      "2024-01-21 13:49:42 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WBIC value with 9 regressors: 1081.496\n",
+      "WBIC value with 9 regressors: 1084.309\n",
       "------------------------------------------------------------------\n",
-      "WBIC value with 10 regressors: 1088.894\n",
+      "WBIC value with 10 regressors: 1090.869\n",
       "------------------------------------------------------------------\n",
-      "CPU times: user 22 s, sys: 917 ms, total: 23 s\n",
-      "Wall time: 2min 1s\n"
+      "CPU times: user 22.3 s, sys: 1.01 s, total: 23.3 s\n",
+      "Wall time: 2min 2s\n"
      ]
     }
    ],
@@ -356,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-03-25T01:06:50.978628Z",
@@ -368,13 +368,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:20:19 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 225 and samples(per chain): 25.\n"
+      "2024-01-21 13:49:54 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 225 and samples(per chain): 25.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "522e65cce7a94ca99eb41735e71608a5",
+       "model_id": "a954b6fb9e8848ea9cc192a72dae3231",
        "version_major": 2,
        "version_minor": 0
       },
@@ -388,7 +388,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b44b7228eda945f482fe2913b0dfacac",
+       "model_id": "4662a81384a442408ec6e72b0e03e54d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -402,7 +402,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8a21a6f3d1af449aae1ef38d67dc417f",
+       "model_id": "22672e568fc74db0aeecce08e2224ca3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -416,7 +416,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c94d27efa7244e279923f78c1fe322c7",
+       "model_id": "874c4dee313a4d65883a3e099bcbfe78",
        "version_major": 2,
        "version_minor": 0
       },
@@ -438,7 +438,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-11 22:20:20 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n"
+      "2024-01-21 13:49:56 - orbit - INFO - Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n"
      ]
     },
     {
@@ -446,7 +446,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "WBIC value for LGT model (stan MCMC): 1139.816\n"
+      "WBIC value for LGT model (stan MCMC): 1143.854\n"
      ]
     },
     {
@@ -455,89 +455,10 @@
      "text": [
       "/Users/towinazure/opt/miniconda3/envs/orbit39/lib/python3.9/site-packages/torch/__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/tensor/python_tensor.cpp:453.)\n",
       "  _C._set_default_tensor_type(t)\n",
-      "2024-01-11 22:20:20 - orbit - INFO - step    0 loss = 315.62, scale = 0.1151\n",
-      "INFO:orbit:step    0 loss = 315.62, scale = 0.1151\n",
-      "2024-01-11 22:20:29 - orbit - INFO - step  100 loss = 116.52, scale = 0.5065\n",
-      "INFO:orbit:step  100 loss = 116.52, scale = 0.5065\n",
-      "2024-01-11 22:20:38 - orbit - INFO - step  200 loss = 116.37, scale = 0.50234\n",
-      "INFO:orbit:step  200 loss = 116.37, scale = 0.50234\n",
-      "2024-01-11 22:20:46 - orbit - INFO - step  300 loss = 116.57, scale = 0.51314\n",
-      "INFO:orbit:step  300 loss = 116.57, scale = 0.51314\n",
-      "2024-01-11 22:20:46 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 225 and samples(per chain): 25.\n",
-      "INFO:orbit:Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 225 and samples(per chain): 25.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WBIC value for LGT model (pyro SVI): 1126.263\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6b4bfc94774f4f118f18cd40cf160e42",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 1 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "89a1c78d0c5947e1b1b69f10773880b3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 2 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e178ade4cb404ca68d64082cfe36d236",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 3 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "82a327f70dc649a181107aede55e557b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 4 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                                                                                                                                                                                                \n",
-      "WBIC value for ETS model: 1197.855\n",
-      "CPU times: user 25.1 s, sys: 3.51 s, total: 28.6 s\n",
-      "Wall time: 27.8 s\n"
+      "2024-01-21 13:49:56 - orbit - INFO - step    0 loss = 299.39, scale = 0.11485\n",
+      "INFO:orbit:step    0 loss = 299.39, scale = 0.11485\n",
+      "2024-01-21 13:50:05 - orbit - INFO - step  100 loss = 116.84, scale = 0.5094\n",
+      "INFO:orbit:step  100 loss = 116.84, scale = 0.5094\n"
      ]
     }
    ],
@@ -586,25 +507,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-03-25T01:06:51.187435Z",
      "start_time": "2022-03-25T01:06:50.982429Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 960x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "labels = [\"DLT_{}\".format(x) for x in range(1, NUM_OF_REGRESSORS + 1)] + ['LGT_MCMC', 'LGT_SVI','ETS']\n",
     "fig, ax = plt.subplots(1, 1,figsize=(12, 6), dpi=80)\n",
@@ -638,81 +548,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-03-25T01:06:52.190878Z",
      "start_time": "2022-03-25T01:06:51.192123Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-01-11 22:20:47 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:20:47 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:20:47 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:20:47 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:20:47 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:20:47 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BIC value with 1 regressors: 1247.444\n",
-      "------------------------------------------------------------------\n",
-      "BIC value with 2 regressors: 1191.892\n",
-      "------------------------------------------------------------------\n",
-      "BIC value with 3 regressors: 1139.408\n",
-      "------------------------------------------------------------------\n",
-      "BIC value with 4 regressors: 1081.432\n",
-      "------------------------------------------------------------------\n",
-      "BIC value with 5 regressors: 1079.344\n",
-      "------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-01-11 22:20:47 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:20:47 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:20:47 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "2024-01-11 22:20:47 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BIC value with 6 regressors: 1078.939\n",
-      "------------------------------------------------------------------\n",
-      "BIC value with 7 regressors: 1078.603\n",
-      "------------------------------------------------------------------\n",
-      "BIC value with 8 regressors: 1078.128\n",
-      "------------------------------------------------------------------\n",
-      "BIC value with 9 regressors: 1077.860\n",
-      "------------------------------------------------------------------\n",
-      "BIC value with 10 regressors: 1187.327\n",
-      "------------------------------------------------------------------\n",
-      "CPU times: user 189 ms, sys: 263 ms, total: 452 ms\n",
-      "Wall time: 551 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "BIC_ls = []\n",
@@ -744,25 +587,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-03-25T01:06:52.363849Z",
      "start_time": "2022-03-25T01:06:52.193136Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 960x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "labels = [\"DLT_{}\".format(x) for x in range(1, NUM_OF_REGRESSORS + 1)]\n",
     "fig, ax = plt.subplots(1, 1,figsize=(12, 6), dpi=80)\n",
diff --git a/examples/archive/Backtest_External_Models.ipynb b/examples/archive/Backtest_External_Models.ipynb
deleted file mode 100644
index e03cd5fd..00000000
--- a/examples/archive/Backtest_External_Models.ipynb
+++ /dev/null
@@ -1,392 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Backtest External Models\n",
-    "\n",
-    "We continue to illustrate how to use `Backtest` object to gauge performance of external models. `Backtest` is designed to work for any model objects which have a `fit` method and a `predict` method, by writing some call back functions. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-21T00:41:14.475488Z",
-     "start_time": "2020-05-21T00:41:13.248605Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'orbit.backtest.backtest'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-dbff8bc8c153>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ggplot'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0morbit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbacktest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbacktest\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTimeSeriesSplitter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBacktest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0morbit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msmape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwmape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'orbit.backtest.backtest'"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "plt.style.use('ggplot')\n",
-    "\n",
-    "from orbit.backtest.backtest import TimeSeriesSplitter, Backtest\n",
-    "from orbit.utils.metrics import mape, smape, wmape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-21T00:41:14.508925Z",
-     "start_time": "2020-05-21T00:41:14.479633Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data_path = \"../examples/data/iclaims_example.csv\"\n",
-    "raw_data = pd.read_csv(data_path, parse_dates=['week'])\n",
-    "\n",
-    "## log transformation\n",
-    "data = raw_data.copy()\n",
-    "# data[['claims', 'trend.unemploy', 'trend.filling', 'trend.job']] = \\\n",
-    "#     data[['claims', 'trend.unemploy', 'trend.filling', 'trend.job']].apply(np.log, axis=1)\n",
-    "\n",
-    "print(data.shape)\n",
-    "data.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sklearn model object - Random Forest"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Declare a `TimeSeriesSplitter` and `Backtest` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-21T00:41:14.522107Z",
-     "start_time": "2020-05-21T00:41:14.514918Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "splitter = TimeSeriesSplitter(data, min_train_len=200, incremental_len=20, forecast_len=20, n_splits=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-21T00:41:14.529507Z",
-     "start_time": "2020-05-21T00:41:14.524893Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bt = Backtest(splitter=splitter)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we instantiate an sklearn model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-21T00:41:14.649714Z",
-     "start_time": "2020-05-21T00:41:14.532596Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "\n",
-    "mod = RandomForestRegressor(n_estimators = 50)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create callback functions for the `RandomForestRegressor` model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-21T00:41:14.659602Z",
-     "start_time": "2020-05-21T00:41:14.652303Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def fit_callback_sklearn(model, train_df, response_col, regressor_col):\n",
-    "    y = train_df[response_col]\n",
-    "    X = train_df[regressor_col]\n",
-    "    model.fit(X, y)\n",
-    "    return\n",
-    "\n",
-    "def predict_callback_sklearn(model, test_df, response_col, regressor_col):\n",
-    "    X = test_df[regressor_col]\n",
-    "    pred = model.predict(X)\n",
-    "\n",
-    "    return pd.DataFrame(pred, columns=['prediction'])\n",
-    "\n",
-    "# passed into fit_callback_sklearn()\n",
-    "fit_predict_args = {\n",
-    "    'response_col': 'claims',\n",
-    "    'regressor_col': ['trend.unemploy', 'trend.filling', 'trend.job']\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-21T00:41:15.077511Z",
-     "start_time": "2020-05-21T00:41:14.665774Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bt.fit_score(\n",
-    "    mod,\n",
-    "    response_col='claims',\n",
-    "    predicted_col='prediction',\n",
-    "    fit_callback=fit_callback_sklearn,\n",
-    "    predict_callback=predict_callback_sklearn,\n",
-    "    fit_args=fit_predict_args,\n",
-    "    predict_args=fit_predict_args\n",
-    ")\n",
-    "\n",
-    "bt.get_predictions().head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-21T00:41:15.093810Z",
-     "start_time": "2020-05-21T00:41:15.080842Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bt.get_scores()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prophet model object"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-21T00:41:15.282504Z",
-     "start_time": "2020-05-21T00:41:15.096407Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from fbprophet import Prophet\n",
-    "import inspect"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-21T00:41:15.296284Z",
-     "start_time": "2020-05-21T00:41:15.285396Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def model_callback_prophet(model, **kwargs):\n",
-    "    object_type = type(model)\n",
-    "    new_instance = object_type(**kwargs)\n",
-    "    \n",
-    "    return new_instance\n",
-    "\n",
-    "def fit_callbacks_prophet(model, train_df, date_col, response_col, regressor_col):\n",
-    "    \n",
-    "    train_df = train_df.rename(columns={date_col: \"ds\", response_col: \"y\"})\n",
-    "    if regressor_col is not None:\n",
-    "        for regressor in regressor_col:\n",
-    "            model.add_regressor(regressor) \n",
-    "    model.fit(train_df)\n",
-    "    \n",
-    "    return \n",
-    "\n",
-    "def pred_callbacks_prophet(model, test_df, date_col, response_col, regressor_col):\n",
-    "    test_df = test_df.rename(columns={date_col: \"ds\", response_col: \"y\"})\n",
-    "    \n",
-    "    predictions = model.predict(test_df)\n",
-    "    predictions.rename(columns={'yhat': 'prediction', 'ds': date_col}, inplace=True)\n",
-    "    predictions=predictions[[date_col, 'prediction']]\n",
-    "\n",
-    "    return predictions\n",
-    "\n",
-    "fit_predict_args = {\n",
-    "    'response_col': 'claims',\n",
-    "    'date_col': 'week',\n",
-    "    'regressor_col': ['trend.unemploy', 'trend.filling', 'trend.job']\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-21T00:41:16.620601Z",
-     "start_time": "2020-05-21T00:41:15.300514Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "mod = Prophet()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note: if some error pops out below (related to the pickle issue), it could be eliminated by setting `save_model=False` or upgrading your python to 3.7."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-21T00:41:24.177741Z",
-     "start_time": "2020-05-21T00:41:16.623103Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bt.fit_score(\n",
-    "    mod,\n",
-    "    response_col='claims',\n",
-    "    predicted_col='prediction',\n",
-    "    fit_callback=fit_callbacks_prophet,\n",
-    "    predict_callback=pred_callbacks_prophet,\n",
-    "    model_callback=model_callback_prophet,\n",
-    "    fit_args=fit_predict_args,\n",
-    "    predict_args=fit_predict_args,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-21T00:41:24.203016Z",
-     "start_time": "2020-05-21T00:41:24.180445Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bt.get_predictions().head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-21T00:41:24.219715Z",
-     "start_time": "2020-05-21T00:41:24.205707Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bt.get_scores()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "orbit",
-   "language": "python",
-   "name": "orbit"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "306.391px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/archive/Backtest_Misc.ipynb b/examples/archive/Backtest_Misc.ipynb
deleted file mode 100644
index 1af0eb28..00000000
--- a/examples/archive/Backtest_Misc.ipynb
+++ /dev/null
@@ -1,319 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Backtest Misc\n",
-    "\n",
-    "Examples of backtesting for \n",
-    "* multiple series \n",
-    "* multiple models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-15T09:34:40.863630Z",
-     "start_time": "2020-05-15T09:34:38.688018Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'orbit.lgt'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-1c6ff01ad6cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ggplot'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0morbit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlgt\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLGT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0morbit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdlt\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDLT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0morbit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbacktest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbacktest\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTimeSeriesSplitter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBacktest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'orbit.lgt'"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "plt.style.use('ggplot')\n",
-    "\n",
-    "from orbit.lgt import LGT\n",
-    "from orbit.dlt import DLT\n",
-    "from orbit.backtest.backtest import TimeSeriesSplitter, Backtest\n",
-    "from orbit.utils.metrics import mape, smape, wmape\n",
-    "from orbit.backtest.functions import run_multi_series_backtest"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-15T09:34:41.221011Z",
-     "start_time": "2020-05-15T09:34:40.865141Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data_path = \"../examples/data/m4_weekly.csv\"\n",
-    "data = pd.read_csv(data_path, parse_dates=['date'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"Total keys: {}\".format(len(data['key'].unique())))\n",
-    "data.head(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Settings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-15T09:34:46.969800Z",
-     "start_time": "2020-05-15T09:34:46.965355Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# data settings\n",
-    "date_col = \"date\"\n",
-    "response_col = \"value\"\n",
-    "key_col = \"key\"\n",
-    "seasonality = 52\n",
-    "\n",
-    "# backtest settings\n",
-    "min_train_len = 120\n",
-    "forecast_len = 13\n",
-    "incremental_len = 5\n",
-    "n_splits = 1\n",
-    "window_type = \"expanding\"\n",
-    "seed = 2019"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-15T09:36:38.348355Z",
-     "start_time": "2020-05-15T09:36:38.344088Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "lgt_map = LGT(\n",
-    "    response_col=response_col,\n",
-    "    date_col=date_col,\n",
-    "    seasonality=seasonality,\n",
-    "    seed=seed,\n",
-    "    infer_method='map',\n",
-    "    predict_method='map',\n",
-    "    is_multiplicative=True\n",
-    ")\n",
-    "\n",
-    "dlt_map = DLT(\n",
-    "    response_col=response_col,\n",
-    "    date_col=date_col,\n",
-    "    seasonality=seasonality,\n",
-    "    seed=seed,\n",
-    "    infer_method='map',\n",
-    "    predict_method='map',\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-15T09:36:41.310569Z",
-     "start_time": "2020-05-15T09:36:41.307838Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bt_models = {\n",
-    "    'LGT-MAP': lgt_map,\n",
-    "    'DLT-MAP': dlt_map\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Backtest Multiple Series"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-15T09:35:17.441768Z",
-     "start_time": "2020-05-15T09:35:11.213915Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bt_result, bt_scores = run_multi_series_backtest(\n",
-    "    data=data, \n",
-    "    response_col=response_col, \n",
-    "    key_col=key_col, \n",
-    "    date_col=date_col,\n",
-    "    model=lgt_map,\n",
-    "    min_train_len=min_train_len, \n",
-    "    incremental_len=incremental_len, \n",
-    "    forecast_len=forecast_len, \n",
-    "    predicted_col='prediction', \n",
-    "    n_splits=n_splits,\n",
-    "    window_type = window_type\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-15T09:35:17.451240Z",
-     "start_time": "2020-05-15T09:35:17.443990Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bt_scores.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Backtest Multiple Models (and Multiple Series)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-15T09:35:35.724253Z",
-     "start_time": "2020-05-15T09:35:25.577981Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bt_result_list = []\n",
-    "bt_scores_list = []\n",
-    "for mod_name, mod in bt_models.items():\n",
-    "    bt_result, bt_scores = run_multi_series_backtest(\n",
-    "        data=data, \n",
-    "        response_col=response_col, \n",
-    "        key_col=key_col, \n",
-    "        date_col=date_col,\n",
-    "        model=lgt_map,\n",
-    "        min_train_len=min_train_len, \n",
-    "        incremental_len=incremental_len, \n",
-    "        forecast_len=forecast_len, \n",
-    "        predicted_col='prediction', \n",
-    "        n_splits=n_splits,\n",
-    "        window_type = window_type\n",
-    "    )\n",
-    "    bt_result['model'] = mod_name\n",
-    "    bt_scores['model'] = mod_name\n",
-    "    bt_result_list.append(bt_result)\n",
-    "    bt_scores_list.append(bt_scores)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-15T09:35:35.736480Z",
-     "start_time": "2020-05-15T09:35:35.726482Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bt_result = pd.concat(bt_result_list, axis=0)\n",
-    "bt_scores = pd.concat(bt_scores_list, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-05-15T09:35:35.748251Z",
-     "start_time": "2020-05-15T09:35:35.738962Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bt_scores.head()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "orbit",
-   "language": "python",
-   "name": "orbit"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "306.391px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/archive/Backtest_Tune_DLT.ipynb b/examples/archive/Backtest_Tune_DLT.ipynb
deleted file mode 100644
index f4ca214c..00000000
--- a/examples/archive/Backtest_Tune_DLT.ipynb
+++ /dev/null
@@ -1,366 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<b><font size=4> Tune damped factor in DLT model with backtest engine\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this demo, we want to show how to use the utility `tune_damped_factor` to tune the hyper-parameter `damped_factor_fixed` in **orbit DLT** model. \n",
-    "\n",
-    "Note: only use this when you want a fixed damped facotr value. Otherwise, one can specify `damped_factor_min` and `damped_factor_max` to do the sampling."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-01-28T00:59:41.870758Z",
-     "start_time": "2020-01-28T00:59:39.165690Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from orbit.dlt import DLT\n",
-    "from orbit.backtest.backtest import TimeSeriesSplitter, Backtest\n",
-    "from orbit.backtest.multibacktest import tune_damped_factor\n",
-    "from orbit.utils.metrics import mape, smape, wmape\n",
-    "\n",
-    "%matplotlib inline\n",
-    "plt.style.use('ggplot')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-01-28T00:59:41.934244Z",
-     "start_time": "2020-01-28T00:59:41.908241Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(443, 5)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>651215</td>\n",
-       "      <td>1.183973</td>\n",
-       "      <td>0.720140</td>\n",
-       "      <td>1.119669</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>825891</td>\n",
-       "      <td>1.183973</td>\n",
-       "      <td>0.814896</td>\n",
-       "      <td>1.178599</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>659173</td>\n",
-       "      <td>1.203382</td>\n",
-       "      <td>0.739091</td>\n",
-       "      <td>1.119669</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>507651</td>\n",
-       "      <td>1.164564</td>\n",
-       "      <td>0.814896</td>\n",
-       "      <td>1.107883</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>538617</td>\n",
-       "      <td>1.086926</td>\n",
-       "      <td>0.776993</td>\n",
-       "      <td>1.072525</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week  claims  trend.unemploy  trend.filling  trend.job\n",
-       "0 2010-01-03  651215        1.183973       0.720140   1.119669\n",
-       "1 2010-01-10  825891        1.183973       0.814896   1.178599\n",
-       "2 2010-01-17  659173        1.203382       0.739091   1.119669\n",
-       "3 2010-01-24  507651        1.164564       0.814896   1.107883\n",
-       "4 2010-01-31  538617        1.086926       0.776993   1.072525"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_path = '../examples/data/iclaims_example.csv'\n",
-    "raw_data = pd.read_csv(data_path, parse_dates=['week'])\n",
-    "data = raw_data.copy()\n",
-    "print(data.shape)\n",
-    "data.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a TimeSeriesSplitter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "min_train_len = 380\n",
-    "forecast_len = 20\n",
-    "incremental_len = 20\n",
-    "\n",
-    "splitter = TimeSeriesSplitter(\n",
-    "    data, min_train_len, incremental_len, forecast_len, \n",
-    "    window_type='expanding', date_col='week')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a DLT model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mod = DLT(\n",
-    "    date_col = 'week',\n",
-    "    response_col = 'claims',\n",
-    "    regressor_col = ['trend.unemploy', 'trend.filling', 'trend.job'],\n",
-    "    seasonality = 52,\n",
-    "    seed = 8888,\n",
-    "    predict_method = 'map',\n",
-    "    infer_method = 'map',\n",
-    "    is_multiplicative = True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conduct a damped factor grid search"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Doing backtesting for damped factor 0.8\n",
-      "Doing backtesting for damped factor 0.9\n",
-      "Doing backtesting for damped factor 0.99\n"
-     ]
-    }
-   ],
-   "source": [
-    "res = tune_damped_factor(model = mod, \n",
-    "                         splitter = splitter,\n",
-    "                         damped_factor_grid = [0.8, 0.9, 0.99],\n",
-    "                         predicted_col = 'prediction',\n",
-    "                         metrics = {\"smape\": smape, \"mape\": mape})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we sort the results in terms of OOT `smape` value. In terms of this, `dampted_factor_fixed = 0.2` is recommended."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>damped factor</th>\n",
-       "      <th>train_smape</th>\n",
-       "      <th>train_mape</th>\n",
-       "      <th>test_smape</th>\n",
-       "      <th>test_mape</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.99</td>\n",
-       "      <td>0.040752</td>\n",
-       "      <td>0.040566</td>\n",
-       "      <td>0.045307</td>\n",
-       "      <td>0.044323</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.90</td>\n",
-       "      <td>0.040723</td>\n",
-       "      <td>0.040533</td>\n",
-       "      <td>0.046178</td>\n",
-       "      <td>0.045098</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.80</td>\n",
-       "      <td>0.041619</td>\n",
-       "      <td>0.041508</td>\n",
-       "      <td>0.050071</td>\n",
-       "      <td>0.048783</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   damped factor  train_smape  train_mape  test_smape  test_mape\n",
-       "2           0.99     0.040752    0.040566    0.045307   0.044323\n",
-       "1           0.90     0.040723    0.040533    0.046178   0.045098\n",
-       "0           0.80     0.041619    0.041508    0.050071   0.048783"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res.sort_values('test_smape')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.7"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "306.391px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "toc-autonumbering": false,
-  "toc-showcode": false
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/archive/Regression_Advance1.ipynb b/examples/archive/Regression_Advance1.ipynb
deleted file mode 100644
index ca3ee039..00000000
--- a/examples/archive/Regression_Advance1.ipynb
+++ /dev/null
@@ -1,1200 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Regressorion with Orbit - Advance I\n",
-    "\n",
-    "In this demo, we want to demonstartate further how to config orbit to perform regressions with time-series analysis.  The config including different regularizations and we use a simulated data set to compare against ground truth. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__IMPORTANT:__ This notebook only works under python 3.6 due to a bug related to matplotlib."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:20:43.392659Z",
-     "start_time": "2020-09-01T22:20:41.373447Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import scipy\n",
-    "import gc\n",
-    "\n",
-    "import statsmodels.api as sm\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "import pystan\n",
-    "\n",
-    "from orbit.models.lgt import LGTMAP, LGTAggregated, LGTFull\n",
-    "from orbit.models.dlt import DLTMAP, DLTAggregated, DLTFull\n",
-    "\n",
-    "from orbit.diagnostics.plot import plot_posterior_params\n",
-    "from orbit.constants.palette import QualitativePalette\n",
-    "from orbit.utils.simulation import make_ts_multiplicative"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:20:43.398369Z",
-     "start_time": "2020-09-01T22:20:43.394625Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "np version: 1.19.0\n",
-      "scipy version: 1.2.0\n",
-      "pystan version: 2.19.1.1\n"
-     ]
-    }
-   ],
-   "source": [
-    "# randomization is using numpy with this version\n",
-    "print(\"np version: {}\".format(np.__version__))\n",
-    "print(\"scipy version: {}\".format(scipy.__version__))\n",
-    "print(\"pystan version: {}\".format(pystan.__version__))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simulation of Regression with Trend"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a vanila problem with observation(t) = trend(t) + regression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:20:58.691963Z",
-     "start_time": "2020-09-01T22:20:58.685443Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.15602066,  0.05231785,  0.16325487, -0.11182046,  0.0027203 ])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# To scale regressor values in a nicer way\n",
-    "REG_BASE = 1000\n",
-    "SEED = 2020\n",
-    "NUM_OF_REGRESSORS = 5\n",
-    "COEFS= np.random.default_rng(SEED).normal(.03, .1, NUM_OF_REGRESSORS)\n",
-    "COEFS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:19:23.840328Z",
-     "start_time": "2020-09-01T22:19:23.827449Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.15602066,  0.05231785,  0.16325487, -0.11182046,  0.0027203 ])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_df, trend, seas, coefs = make_ts_multiplicative(\n",
-    "    series_len=200, seasonality=52, coefs=COEFS,\n",
-    "    regressor_log_loc=0.0, regressor_log_scale=0.2, noise_to_signal_ratio=1.0,\n",
-    "    regression_sparsity=0.5, obs_val_base=1000, regresspr_val_base=REG_BASE, trend_type='rw',\n",
-    "    seas_scale=.05, response_col='response', seed=SEED\n",
-    ")\n",
-    "coefs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:19:25.435497Z",
-     "start_time": "2020-09-01T22:19:25.421773Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>response</th>\n",
-       "      <th>regressor_1</th>\n",
-       "      <th>regressor_2</th>\n",
-       "      <th>regressor_3</th>\n",
-       "      <th>regressor_4</th>\n",
-       "      <th>regressor_5</th>\n",
-       "      <th>date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1865.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1842.0</td>\n",
-       "      <td>2548.0</td>\n",
-       "      <td>1047.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2016-01-10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1680.0</td>\n",
-       "      <td>1755.0</td>\n",
-       "      <td>1858.0</td>\n",
-       "      <td>1871.0</td>\n",
-       "      <td>912.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2016-01-17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2390.0</td>\n",
-       "      <td>1429.0</td>\n",
-       "      <td>2055.0</td>\n",
-       "      <td>1936.0</td>\n",
-       "      <td>1974.0</td>\n",
-       "      <td>1765.0</td>\n",
-       "      <td>2016-01-24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>984.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1272.0</td>\n",
-       "      <td>1469.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3932.0</td>\n",
-       "      <td>2016-01-31</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1259.0</td>\n",
-       "      <td>2447.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>819.0</td>\n",
-       "      <td>1753.0</td>\n",
-       "      <td>2383.0</td>\n",
-       "      <td>2016-02-07</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   response  regressor_1  regressor_2  regressor_3  regressor_4  regressor_5  \\\n",
-       "0    1865.0          0.0       1842.0       2548.0       1047.0          0.0   \n",
-       "1    1680.0       1755.0       1858.0       1871.0        912.0          0.0   \n",
-       "2    2390.0       1429.0       2055.0       1936.0       1974.0       1765.0   \n",
-       "3     984.0          0.0       1272.0       1469.0          0.0       3932.0   \n",
-       "4    1259.0       2447.0          0.0        819.0       1753.0       2383.0   \n",
-       "\n",
-       "        date  \n",
-       "0 2016-01-10  \n",
-       "1 2016-01-17  \n",
-       "2 2016-01-24  \n",
-       "3 2016-01-31  \n",
-       "4 2016-02-07  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:19:26.404522Z",
-     "start_time": "2020-09-01T22:19:26.401017Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.15602066  0.05231785  0.16325487 -0.11182046  0.0027203 ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(coefs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:15:29.736810Z",
-     "start_time": "2020-09-01T22:15:29.552208Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x143813150>]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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Y4cXjspNMKf7zp6ep9Lt42zWblt2Wez2hLQKNZh0yNJFAKagJrIxraDw+OWulbvtwhOZKX96OFfK5CMdmP9ZMlFJ88LuHef+3DtFU6cvUDWys8LC91qiluKq1kqGJBMd6wgyOx3WRmYm2CDSadUj/eBxgZSwCM8c/HE1SlfX6SinOD0W4flv+Jgha4y5HIklq5nFzffzHx/n6M+2864YtvP/WHTx3bpivPX2ei+qDuB02nvngTSgFV/39wzxxcoAfHO4mmVL88I9ekbf1rlW0EGg064jP/ew0J3vHec2lxnzilYgRVJhVv6MzhKB/PE4smaYpzxYBwHAkMacQpNOKzz1xhl+6tJ7337oDEWHvpkr2bqrM7FNbZqS0bqsN8F8/O83AuNG+YnA8Pu3/UYpo15BGs06YTKX5j5+e4jsHO+kLGy6PlbQIZsYJ2ocMV0w+XUNWb6OhCeOkrZTiM4+1cXZGSungRILEZJqrWivnjU9ct62agfEEbodx+nv27HDe1rtW0UKwCkQTKd78Hz/nhfaFB700msXy89ODDIwbJ8TnzxvftaoVihEAF8wUbjc7kq6IRWAKwemBCf7xR8f55EMnpu23mPoFy3X1vlt2GC6jM0N5W+9aRQvBKtDWN84zZ4f4lh6OoVlB7j/Ylbn981MDuBy2THuFfFJudgad2W/Iak3dGMpfS4uMRWB2IT1gCtwPj/QwmlXdbAnBQiqab9hRw3+/7WX89nWtXN5cwTNnB/O23rWKFoJVoNssu3+ibaDAK9GsV+KTKX50tIfbd28A4OxghJqAO29pnNnkdg1FqAu68TjteTuWFY+wLIKD7cM4bEJiMs13D061n7CKxRoW0FdJRHjVzlrsNmFfaxUvdoUJzzFxrRTIixCIyG0iclxE2kTknlkef5uI9IvIQfPvHVmP3S0iJ82/u/OxnmKj20xRO90/kbly0WjyyYMv9jIWm+Sufc2Zk2H1CriFIHcr6vND+U0dBfA47fhddoYmjGMdbB/hqs2VXLwxyL3PtmcmmHWPxvC57Jm1LZSrWitJK3j6dGm7h5YtBCJiBz4N3A7sAu4SkV2z7PoNpdQe8+9z5nMrgb8ErgL2AX8pIqHlrqnYyO6Jrq0CzUrwjWfbaajwct3WarbXGTnzKxEoBnA5bPhc9lktgqZQfoUAIOR3MRJJEE2kONY9xuVNIX7j6haOdYd57EQ/YLiGljIVbe+mEBU+Z8a6ONI5SnxyeVPR1iL5sAj2AW1KqdNKqQRwL3DHAp97K/CgUmpIKTUMPAjcloc1FRU9ozGaKr1UB9w8cVILgSa/tA9F+NnJAd68twmbTdheVwasnBDAhW0mnjo1QHc4RmOeLQIw4gRDkQRHukZJpRV7mip40xWNNIa8fPLBEyiljOZyS2i37XbYecOeBh482st9z3Xw2n99gh8c6s77/6HYyYcQNADtWfc7zG0zeZOIHBKR+0SkaZHPXdN0j8SoL/dyzZYqnj1b2iaoJv98c387IvCrexsBpoSgbGVcQzC9FfXXnj7HW/7raTZV+fmVKxrzfqyQz8XwRIID5400zz3NFbgcNv7wxm0c6hjl4WN9dI3GFhQfmI1f3dtIIpXmz+57AZhy5ZYSqxUs/j6wSSl1KcZV/5cW+wIi8k4R2S8i+/v7+/O+wJWkazTKxnIPGyu8DI4nMn5NjSYfPHN2iMsaKzJXxJYQVPlX1iIIR5NMptL82yNt7G0J8cAfvoLmqhVwDfmcDEUSHGwfyVjWAL98RQP15R6++NRZ+sfiSx7Ac/HGci7eGEQAu02WPSd5LZIPIegEmrLuN5rbMiilBpVScfPu54ArF/rcrNf4rFJqr1Jqb01N/krYV5p0WtEbjlFf4aXc6ySRShNNlp4PUrNy9IXjNGSlbF5UX8bvvKKVWy6uW7Fjlnud9I7FePilPrpHY7zjFZvxuvKXLZRNyO9ieCLJgfMjXN40FUJ02G28fs/GTNytvnzpw3A++suX8Om3XMGGoCcTmC4l8iEEzwLbRKRVRFzAncD92TuISH3W3dcDx8zbPwZuEZGQGSS+xdy2bhiYiJNMKerLPTnT7jSapaKUMSO4rmzqJOiw2/jgL+2icQUCtxY37Kjl3GCE937jIHVBNzddVLtix6r0uRiPT9I9GuPy5oppj71hz5QneamuIYBLGyu4/ZJ6KnxObREsBaXUJPAejBP4MeB/lVJHReQjIvJ6c7c/FJGjIvIC8IfA28znDgF/gyEmzwIfMbetG6xBHfXlXi0EmrwzHp8kkkixoXx1e+W85apm3nXDFiYSKX7tZc047SvnZQ5lDdfZ0zRdCC6qD7Jzg+EKy8ds5pDPxXAJCkFeyg6VUg8AD8zY9uGs2x8APpDjuV8AvpCPdRQjVjFZfbknE1wbzTHvVaNZLL1mTyFr2Ptq8v5bd/CKrdXTGrutBFZ1sctuY9fG4AWPv+WqZj7x4AnqlzknGQzRyZ5kdqw7zJNtA7zjFZuX/drFjO4+usJYGQjZ/kttEWjyRW/YCL0VQghEhGu2Vq/4cax+Q7s2BnE7LoxD/ObVLbx5b9Osjy3+WM5pFsH/PH2er/ziHDfsqGFrbdmyX79Y0S0mVpju0Rhuh41Kvytnsy5N8aCU4m/+70WO94wVeikLwhqsUgghWC0si2BmfMBCRPLW1qLC52I0miSVNjL72oeN/knZfZzWI1oIVpjecIy6oFHxGMwa6KEpTgbGE3z+iTP8z9PnCr2UBdE7ZgnB+u2n3xjysrU2wK0Xb1jxY1X6nCg1ZbVbU87uf6FrXad9ayFYYSbikwTMDpBlbgciU18ypRR/fO8BvvWc7kpaLFjNx9ZKj/re0RhlHgc+1/r18vrdDh567yu5enPVih8rlDX/QClFx3CE6oCbs4MRDnWMrvjxC4UWghVmIp7C7zbMVptNCHqmSvOHI0m+e7CLP/3mC3z+iTOFXKbGxLLWXupZGx0pe8NxNqxjt9BqU2HGI0YiiczEtbe+vAW7TXj4WG+BV7dyaCFYYSLJFN6sq7UK35QQdJpm54agh79/4BjRhC40KzRjsUkA0mqq930x02O6HjX5oTIzGjOZmbi2uyFIS6WPk33jhVzaiqKFYIWJxCfxZ1VcZjfr6hwxAlGv3lVHKq0ywzc0hSPbCti/BvpC9WkhyCvZ8w86zEBxU8jH5poAp/q1EGiWSCSRmlZ6n92sywpEXdJQDsDQuBaCQhOOGhZBbZm76BsEptOKvrH4ug4UrzZWhtJwJJH5fTaEvGyp8XN2IJLJJlpvaCFYYSKJSfxZrqGg2awLDCHwu+xsrvEDaIugCLAsgpsuquXA+RFiRdwXanAiwWRasWEZPXY00/G57LjsNoYiCdqHIlQHXPhcDrbUBEik0hkrYTaeODnAfc91MBGfXMUV5wctBCtMJJHCl9M1FKUh5J26CpnQQlBowtEkDptwy64NxCfTRW0VnOwzah1qy7QQ5AsRMfoNTSRpH45k+jVtqTUu1k71j3OsO8zQLL/Vv/r+Ud73zRe4+qMPT6tOXgtoIVhBUmlFfDI9LbWvwhQCpRSdw1EaKqaEYLYvl2Z1CceSBL1Ort5chcth47HjxdnyvC8c433/+wK1ZW72ta5si4dSwxqE0z4UpckctLO52pj6duD8CG/8zJN86qETFzxvaCJBY8jLWGySc4MTq7rm5aKFIE986qGTfPrRtmnbIgnDRJxpEUymFZFEKmMRBD1ObEJJNrsqNsLRSYIeB16XnataK/npieIUgg9/7ygj0SRfeNvLMhcSmvxQ4XPSORylayRKo9neO+R3Uel38cUnzxJLpjneO73yPJVWjEQSbKs1BGMiXrwuxdnQQpAnvn+oi28/P70wLGKmg/rc04UADLfQaDRJQ4UPm00I+VwMaoug4FgWARitltv6xuf0C89HbzjG1X//MIfzXIx0emCc67fVsNtMNNDkj5DPxYvdYRTwqh1T7bW31PgZM/3/p/qnX/GHo0nSiowFMR4v/hqUbLQQ5Im+cIzzQxEmU+nMtowQuC4UgmPdYYDMQJFKv0vHCIqAsdgkZR7DlffK7cYApOVYBU+dGqAnHONIV36FYGgiMa09syZ/1Ja5sQl88tf2THO7bakxrvabK330j8WnNY+0rPmmkCUE2iIoOaKJFOHYJMmUyqScAZnsgewYgSUEL3YZQpBteuoYQeEJR5MEPcZntKXGT4XPyZHO8JJfzypKy+dnm04rhiNJKv3OvL2mZor33LiNb7/7Wl5/2cZp269oCVHhc/IHN24F4HRWXUFGCCyLILa2Moe0EOSBvrGpYdenB6a+HNZIymyLwHI7vGhaBI3mMI3KEh2IUWyEY1NCICJsrvZzZmDphUSWEAyMx+fZc+GMxSZJpRWVKziTuJSpKXNfMAAH4FevbOSZv7iZK1qMcZnZ7iFrvOXGCg82Yc2lkGohyAN9Y1M/8tNZX47ZLAKrcvHJtgHKvc7MIG7DIlhbfsX1SDg6SdA79XltrglM+0wXQyyZyrgA82kRDE4Y3zdtEawuIoLLYaO50ofDJrNaBCGfi4DbwbgWgtLDmhIFcHpg6qQRnSVGUB1wU+l3sbelkq+8fR82mwDGj3o4kiC9TisX1wKJyTTRZCpjEQBsrvHTNxZnbAkN6I50jjKZVojAYB6rxrNPOprVx2m30VLl41T/OO1DEUYjyUx8r9K/NoVg/faunYefnuinYzjCr1/VsuzX6jOnRG2u9k+7SpgwhSC7stjjtPPch24GjCsMi0q/m1RaMRabpNynr/QKgXWyt9x3MJU/fmZggksbZx+MAlND5OvLp+bmWm6hyxor8uoasizHKu0aKhhbagLsPzvMrf/8OK+7dCMVficuuw2fy07A49AxgrXCl586y788fDIvr9U7FsNlt3F5c2iaGyFq1hFk9xoCQwCyRQCmzHzdZqJwhM0f73TXkFFROp976JkzQ7z8o4/w5Z+fBSA+meKBI900hrzs3FCWV9fQkOkaCmnXUMHYXBNgcCJBJJHieO8YIxNJQn4nIoLf7WAiUYJCICK3ichxEWkTkXtmefy9IvKiiBwSkYdFpCXrsZSIHDT/7s/HehZC71iMgfFEXppI9YXj1JS5L3AjZCwC9/xj9CwzX2cOFQ6rB1S2a6ilyodNpmeIzIbVUuCv7j/Kpx46ybu/+jwHzo/wJzdvNypVJ/Ln9rMsAl1IVjhevqWKhgovL99cxdnBCYYiicxvOOB2ZNqZrxWWLQQiYgc+DdwO7ALuEpFdM3Y7AOxVSl0K3Af8Y9ZjUaXUHvPv9ctdz0LpC8dJpVVeMnX6xmLUBd1smXH1aNUReBYwVFv3Gyo8YxmLYEoI3A47jSEfpwbmtgisjrI7NgT55EMneOR4Hx9+7S7edGUjVQE3k2mVt0E3w5EEHqdtXU8lK3Zeub2GJ++5kZsuqmUkkuTMwETmNxxwO9Zc1lA+vkn7gDal1GkAEbkXuAN40dpBKfVo1v6/AH4jD8ddMqm0yvhs+8LxTObOUukNx9laE8hUeT57dojLmiqIxCfxueyZgPBcaIug8Fgn6myLAAz30HyuIau46P73XMtYbBJhauxhlfnv4EQiMwFrOQxNJDIDVDSFpaXKuPhr6xtnR10ZwJoMFufDNdQAtGfd7zC35eLtwA+z7ntEZL+I/EJE3pDrSSLyTnO//f39y+v/Mjgex7LS+/MQxDOGg7hpDPnYWhvINCqLJKd3Hp2LqoApBDpGUDAyriHv9OujzdUBzgyMz+naGY0mCXocOO02Kv2uaVW/1mebr8whXVVcPLRW+zK3rZiNv0SFYMGIyG8Ae4GPZ21uUUrtBd4C/LOIbJntuUqpzyql9iql9tbU1CxrHb3hqZN//9jyhMCqKq41p0TdsL2GZ84MMRGfNC2ChRldXqcdj9PGN/e3872Dnctak2Zp5LIIWqt9xJLpOS8aRiK5r/anusvmJ3NoaCKh4wNFQlOlEUOCqTGXZR7DNaTU2kkFz4cQdAJNWfcbzW3TEJGbgQ8Cr1dKZX4RSqlO89/TwGPA5XlY05xk5/1nVwUvBev5tWWGe+mGHbUkUml+fmrwglkEcyEifOSO3aTSij+69yB94eWtS7N4wtFJ7Da54DNrNNsGnB/K3XxuJKiI7YIAACAASURBVJrMFAvOxHI9DuTJIhiOaCEoFtwOOxvN7gDWhYDf7SCtpjoLrAXyIQTPAttEpFVEXMCdwLTsHxG5HPhPDBHoy9oeEhG3ebsauJas2MJKkV0JvFyL4OygcXKwLIKXtYbwuew8dqJvUUIA8Oa9Tfzl6y4GoH14bQ22WA+EY0nKPI4LUnubTSFon0sIIslMH6mZWPGfvLmGxrUQFBOt1UacIDtYDGur39CyhUApNQm8B/gxcAz4X6XUURH5iIhYWUAfBwLAN2ekiV4E7BeRF4BHgY8ppVZcCHrDMUSMH3jfMoTgh4e7efdXn6Pc62RXfRAwrhCuaq3k6dNDRBILdw1Z1FcYgtI9qoVgtZmIp6YV/1k0mFd87UO5P5PRaG4hcDlsBD2OvLiGEpNpxuKTOlhcRLRUGRcKoZlCsIbiBHnJP1NKPQA8MGPbh7Nu35zjeU8Bl+RjDYuhbyxOld9FfblnyRZBKq34i+8cprXGz3/+5l5qyqYyj3bWB/nZyQFaqnyLzkiyzMzuEe0aWm0m4pOz1nx4nHbqgu65XUORRE7XEBjuoYE8ZISNWO0ltEVQNGwyM4dC5ue/FoWgJCuL+8Ixaso81JS5GViiEBxsH2Y4kuSd12/JXDFabK8LMJlWnB2MLMo1BEagMuB2rLmZp+uBiTksuOZKH+3DxryJtr7pxWXptGI0mqTCm/vkXOl3MZQH15A1vKhKC0HR8KqdtVy/vYat5nQyvxaCtUHfWJy6oJuaMvesrqF0WvHX3z/KgfPDOV/jkZf6sNuEV267MINpW62RT5xKK3zuxRtd9eUe7RoqAJFEKnM1N5OmkI/2oQhf+cU5bv3nx6clHIzFJ0kr5rQIqgIuDraP8I4v7ef84NInnlkFh9oiKB621AT48m/vy1xEWIONSipGsBbpDceoK/NQW+ZhPD6ZmS1s8ZMXe/nvJ8/yo6M9OV/jkZf6ubI5NGuDuC01Aax4o8+5OIsADPdQl3YNrToTZgHgbDRV+ugJx/jBoW5SaZUZLART9Qe5YgQAd1+zieu3V/Nk2wAf/8nxJa/RSmGtDmghKFYsi2At9RsqOSGwqoprTYsApmcOKaX498eMIfS52j10j0Y51h3mVTtrZ33c67JnMk2WYhFsrNAWQSGIJFKZH/FMmip9KAX7zxlW4ks9U8PLrfYSc1UNX7Olmv/8zb285apmHjjcveTP1/qu1pR5lvR8zcpTkllDaw2rqrg26Mnk/mcLwZNtg7xgDhrPNSjmoWNGBuyNOYQAYJvpL1xsjABgY7mXgfEEsQXkISul+M6BjgXtq5kbI8tr9s/LEnYAm8BLPVMWwUjUuGCYyzVk8bZrNqGU4ktPnVvSGvvH4pksJE1xMhUsXju/yZITgkePGyfx1ip/xiKwKo2VUnzq4RPUBd3sbQllMjRm8n8vdLG5xs/2ukDO42wz+44sRQjqzeBzz+j87qHnzg3zJ994gYeP9c27r2ZuJuJzWQTGZ1LmdnDt1mpe6p7FIpjDNTT1Oj5u2bWB+57rWNIa+8fi1ATcF9Q6aIoHj9OG3SaMx9fOxMGSEoKxWJKP//g4e1tCXLu1ipYqH2UeR6alw+MnB3j27DDvuXEbdeWeWfv+9IZjPHN2iNddunHOH+OURbAE11C5YfZ3LcB9cLjTsF4G89S+YL3RPhThT75xcN4JY6m0IjpHb6i6Mg9uh43rtlVzSUM5p/rHSUymAaOqGOaOEWSzd1OIgfE4g0voc9U3Zrg1NcWLiOB32ZnQFkFx8m+PtDEwnuDDr9uFiOBzOXjHdZv5yYu9PNU2wMd//BINFV5+bW+TMUx+lhjBA4e7UQped1n9nMeyOpFm1xcslMXUEhzpzP9M3PXEX3//KN850Mlh092XCythYLaCMgCbTfjMr1/BPbfvZMeGMibTilPmjIJR84IhuEAhsNIMZ6ahLgTLItAUN2Ue55qaSVBSQmC3CXfta542cvC3r9tEudfJWz73NEe7wtxz+05cDhshn5PRaJJUWnF2YCKTE/yDQ93s3FDGVjNFNBfb68p46L3Xc/226kWvc4NlESygluBol3GC03MMLuTxE/2ZeM58XWat2RG+OYYI3XRRHS1Vfi4yq8iPdI7SMxpjJJI0mwYuzA2YEYJ5ht3MRt9YTFsEawC/276mZhKUVMTp/bftvKAjYJnHyYdfu4sfHe3hj27alrmSD/ldpJXROuCNn3mS11+2kT+/fScH2kf4vVduXtDx5hOLXHicdqoDLk7PMwwllkxx0ryqHIqsHX/kavGvj5ykLuimNxyft4Lc+tHmsgiyaa3247LbeP+3DiEYor+QQLHFxnIvPpd90RZBYjLNcCRJTUBnDBU7AbeDMR0jKF5m8+u/6cpG/uutezMiAFMNpE73jzMcSfLkqUEOdYySSiv2tlSu+DpftaOWHx3pyQw8mY3jPWOZUZvaIriQ80MRrt9Wg9Mu83b+jGTGis4vBE67jd+5vpU3Xt5Apd/FSz1jC44PgOFm2lITWLQQWMOUluJu1KwuIZ+L4RxZh8VIyQnBQrFywq1gbFvfOD852gvA5c0VOZ+XL+6+ZhPRZIpvzZFdcsR0C+V7OPp6QCnF0ESCqoDb6PMzj2toyiJYmHvnz27dySfevIc/uHEbsLDU0Wy21gY4tUghsKyaWi0ERU91wL2mEji0EOTA6u5oBWMB7n32PFtq/HkZNzgfuxvKuby5gq/+4lzOyVhHu8IEPQ4uaSjPy+zl9cR4fJJkSlHld1EdcM/rGpqKESzOW3rXvmY2V/tpCvnm3zmLrbUBukZji/Ij941pi2CtUBVwMTiemHOqXTGhhSAH1hXeEdMicNqFSCLFlS2hVVvDXS9r5vTABMd7x2Z9/GjnKLsbyo2GZhOJNTURaaUZyurJU1O2AIsgsTiLwMLlsPG991zL37xh96Ket6XGCBifWkTAuF8LwZqhOuBmMq0yU++KHS0EObBiBCf7xgi4HZm4wGoKwWVNhgvqxCxCkEylOdYzxsUbg4T8LuKT6TU1EWmlye7SWR1wLThYvJSWIGUe54IzhiyszKGTvXMLwYnescwUPOv/sNjW5prVx5pTPd8FSLGghSAHPpcdl8NGWkFjyMvVm6uA1RWC1mo/DptwvOdCIbAKmnY3lGfcWDpOMIXV8rnStAgGJ6bM9FRaMZlKT9vfKv5ZrEWwVDZV+Qi4HRxoz93hFuB3vryfjz3wEmCkjoZ8TlwO/bMtdvI9nnSl0d+oHIhIZtBEQ4WXt12ziU/duWfJKaFLweWw0Vrt58QsV41W7OLijeWZlsRrKUthpTgzMMHQRCIjipVmjCCVVpkK4L/49mF+9yvPTXueVVC2lErwpeCw27h6cyVPtg3OuV//WDzjGuwfi1Orm82tCSwhyNd40pVGC8EcWLNmG0Jeyn1O7tjTsOpr2F5Xxsm+Cy2CI52j+Fx2Wqv9VPoNweoejfKHXz/AoY6R1V5m0fCbn3+aj/3w2JRrKODK/Cgt18rhztELirkmEimcdlnVq+1rt1ZzZmCCjuHZ5xMkU2kiiRSn+ydIpxV9Y3EdH1gjaNfQOiIjBDMmkK0m2+oCnB+KEE1M9/8f7Rrlovogdptk1vnIS33c/0IXv/8/z8/bW2c9Ekum6BiOcrJvnOFIArfDhtdpz5w8rR9lTziWaRRnEYkvfr70crluq1F1/lQOq8CqIYkmU3SORGnrG2dT9eKykzSFIeRzYROW1E+qEORFCETkNhE5LiJtInLPLI+7ReQb5uNPi8imrMc+YG4/LiK35mM9+cIKGDcuMjUwn+yoK0Op6X1p0uZglN0bjVYH1joffslop9A5HOWvv//i6i+2wFgtOc4PRhgcT1DldyEiWf7aOLFkiqGJBOFYclpq30QitWrxAYuttQFqy9z8rG1g1sfDWcWEP3mxl/H4JHuaVi9GpVk6dptQ6XfRXyquIRGxA58Gbgd2AXeJyK4Zu70dGFZKbQU+CfyD+dxdwJ3AxcBtwGfM1ysKQqbLpSFUSIvAiEk8dryPz/3sNMlUmrODE0wkUlxsVkIHPU5sYrg+Ntf4uWtfM/e/0JWpOi4V2ocNIRicSHB+aIJK0zzPHkBkjZhUimlNwSKJyQVVFecTEeG6rdU81TYwa+pvdlW5VVi4p6n8gv00xUl1wF1SFsE+oE0pdVoplQDuBe6Ysc8dwJfM2/cBN4nR6+EO4F6lVFwpdQZoM1+vKCgG19CmKh8uu41/evAEf/uDYzzRNsARc0zi7o3GScGW5R66ojnE7oZyEpPpBTWtW09k+9oPd45S6TcEIOhx4LLb6B+PTxsBag2UAWOIyFJSR5fLnuYKBicSs87OzhaCF7vDlLkdbK7OPQNDU1xUBVyZWFWxkw8haADas+53mNtm3UcpNQmMAlULfC4AIvJOEdkvIvv7+/vzsOz5ee2lG/mDG7cWdD6sw27jZa2hTN75kY5RjnaO4rLb2JY1GMfKHLqiOURrtR+As4NzN61bb3QMTwlfLJmmynxPRISaMqO6uCc8tU92nCASn1x11xDANjMLbbYU4bBpsVh9jC5tKsdm0wNp1gpVfqOQ8R9+9BIf+u7hQi9nTtZMsFgp9Vml1F6l1N6amppVOeaODWX86S07Cj4N6r/fto8H/+R6Wqv9HO4c5WhXmB0bynDapz4+q5bg8uYKNptCcGae7qXrjY7h6LRiKyt2AlBd5qYvHKd7NNsimBKCiURq1YPFQGbK3WxFg5ZFYPW22tO08j2uNPmjOuCmNxzjy0+dnTdNuNDkQwg6gaas+43mtln3EREHUA4MLvC5JY/LYUNE2N1QzpHOUY50jbK7IThtn5DfScDtYHtdGTVlbvwuO6f7S00IIuzYEMhYcNlCsLOujCNdo9PcZdmjSI0YwepbBEZTPNesQmAFiy83A8SXNWohWEtUBVzEkmkmEqmcY2+LhXwIwbPANhFpFREXRvD3/hn73A/cbd7+FeARZUTH7gfuNLOKWoFtwDN5WNO6ZPfGIF3mIJSLN04PGv72ta185I6LsdsEEWFTtf8C19BTpwb4nS/vX7dB5I7hKI0VPlqqDIsoWwiubAkxEknyVNtgxmrI9sFPxAtjEYBRK3J8lqLBcDSJ22HjlovruLy5gqtaqwqwOs1SyZ4kNxpNFnUDumULgenzfw/wY+AY8L9KqaMi8hEReb252+eBKhFpA94L3GM+9yjwv8CLwI+A31dK6YY5Obgka17CxRunWwRXba7il69ozNxvrfZzZmCC7x3s5B1f2o9SigcOd/Pgi72ZzJn1RCyZon8sTmPIS0ulke6bLQRXmK1BTg9MsHOD4ZcfzY4RJAoTIwCzaLB37IITxWg0SbnXyUX1Qb7z7mspX2Sra01hsYrKvE47aQVjRTyxLC+XQEqpB4AHZmz7cNbtGPCrOZ77d8Df5WMd6x0rXdRuk8y4xFy0Vvt54HA3/+8nx2kfitI3Fs+0qmgfimTmIq8XOk2XT2Oll5SZilmVJQRG+3AnI5EkTZU+/C57JkaQTisiicJkDYEhBJGEUTTWVDlVs2IJgWZtsrM+yMZyD7dfUs/nnzhDuIg/zzUTLNYY2SMtVT621gTm7XbZWu0nraB9yDhBHu0a5aTph87OrlkvWP+nxpCP3RvLcdhkWiGgiHBls2EV1Jd7qPC5GIkkOTswwfcPdQGr13BuJjs2zB4wDseSBIv0xKGZn4YKL0994CZebjasnFnNXkyU1Mzi9cAHbt+J3Ta/flsppFV+I5f58RMDDJtfxPYcvW3WMlYNQWPIy96WEE/dcyO1wekN2q5oCfHwS31sKPdQ7nUyGk3w0R8e48fm5LlCWQRW0eDx3jFuuqgus300mtRN5tYBlksvu26l2NAWwRrjtt31vHpX3bz7ba4J4HLYuPuaTbRU+fg/86oXpqyE9UTHcBSnXagt8yAiF4gATPX22VYbyLiJjveMcWljOXfta+KG7auTljyToMdJhc95QQGgdg2tDyrMz1BbBJpVp9zr5OH3vpKNFV6OdYf54ZEeAFqqfDm7Xa5lOoajNFR4sc9RcHVZUwXPfPAmass8VPicHO4cpXM4yntu3MZ7X719FVd7IXVlHnrD06uLw9FJgh79E13rTFkExSsE2iJYxzRV+rDbJJNhVO51ckVzaF3GCNqHIgtqDmi5Wsq9TtqHoqTVVFFXIakr90zL5kqbYw61RbD2sT7D0SKuJdBCUALsMoVge12AppCX7tEoyRkTutY6HcNRGhfRHLDcO5VRtG0Vhw3loq7MPU0IxuKTKIUOFq8D3A47Ppe9qF1DWghKgF31RtrptroyGit9pBV0j6yfWoJYMsXAeHxRQlBhmut2mxRFj/8N5R76x+KZEZpWVbEWgvVBhdc5rYCx2NBCUALUBd288/rN/OqVjTSZ7pP1lDmUnTq6UKwA3qYqH25H4Tuf1wY9pBWZbpXWSUO7htYH5T5XUccIdCSqBBAR/uI1FwGGLz373/VAduroQrEsgmJwCwFsMLOcekZj1AU9GYtAC8H6oNzrmFbJXmxoi6DEqC/3YLcJ59eVECzeIrBiBNuKIFAMhtUGZOIE4ZgWgvVEhdel6wg0xYPDbmN7XRkvrKMB91M1BAsf7L6h3LgCn9mzqVBYFoElBFbxn44RrA+supViRQtBCXJVayXPnRsmMbk+Moc6hiM0VHgXNbSltdrP//3Bddyya8MKrmzhVAXc2G2SqSV49swQFT4ndYsQN03xUu5zFnWMQAtBCXL15kpiyTSHO9e+VRCfTNE+HF2UW8hid0PxTPyy24SagJuecIxUWvHo8T5etaMWh13/RNcDFV4Xick0sWRxNlfW37ISZJ/Z1/4Xp4cKvJLlcd9zHez40I94oX2Epsq1303VKio72D7McCTJjTtrC70kTZ6wkhOy3UP/+vBJXvOpn/HTE6szencutBCUIJV+F9vrAjx9Zm0LwRMn+wn5nLzrhi381rWthV7OsrGKyh461ofDJlxfoN5HmvyT6TdkBoyfPj3IJx46wZmBCe7+wjN84NuHiE8WzlrQQlCiXNVaxXNnhzIFTGuRo11hrmwJ8ee37WR7XXGkgS6HDeUeOoajfPdAJy/bVKkzhtYR5VmN5+KTKf70my/QXOnjqXtu5F03bOHrz7Tz2198tmDr00JQolzSUM5EIkXXGq0wjiZSnOofZ9eMkZ1rmZYqP5FEirRS/P6rthZ6OZo8kmk8F0lwdiBCx3CUP7ppGyG/iz+/bSfvuK6VJ9sGC3ZhpgvKSpQaM2+9fzxOc1XhWywslmM9YdKqeNI/88GvX9XM1Zsr2bkhOGcXVc3aoy6rYNBrzsbOnkZnpTNPJFKUe1f/+lxbBCWKNVh7YDw+534ze+QXC0e7woCR+bNe8DjtXLyxXIvAOqTK78LlsNE9GqNn1PhNbciamREwhyJNFGiusRaCEqXGzE/vH8stBM+dG+Kajz3CC+3Fl2Z6tHOUCp+TjeV6gpem+BER6ss9dI3G6Bk1fnO1wakaEf9aFgIRqRSRB0XkpPlvaJZ99ojIz0XkqIgcEpFfy3rsiyJyRkQOmn97lrMezcKp9LsQmVsIHnmpDyhOq+BoV5iLNwYR0VfPmrXBhqCH7pEoPeEYVX7XtGaHlkUwvhaFALgHeFgptQ142Lw/kwjwVqXUxcBtwD+LSEXW43+mlNpj/h1c5no0C8Rpt1Hpc83pGnqybRCY6ntTLEym0hzvGePidRQo1qx/NlZ4M66hDTMs2SmLoDAppMsVgjuAL5m3vwS8YeYOSqkTSqmT5u0uoA/QCdJFQHXAndMiCMeSHDL7EY3Fcl+lxCdTvOr/Pcab/+PnPHq8b0XWOZOukRiJVJotNf5VOZ5Gkw/qyz30hGN0jcSmxQcA/G7DOlirFkGdUqrbvN0DzDlVXUT2AS7gVNbmvzNdRp8UkZyNVUTknSKyX0T29/cXvhJvPVBT5qY/h0XwzOkh0sq4HZ5DCHpH45wZmOBgxwi/+5XnViX97ezgBACbqrQQaNYO9RVeUmlFW//4BRZB0QeLReQhETkyy98d2fsppRSg5nideuArwG8ppayzxQeAncDLgErgz3M9Xyn1WaXUXqXU3poabVDkg5qy3BbBk6cGcDtseJ12xuZwDQ1MGM9/1Y4aEpNpesIrX5dwzhKCai0EmrWDldiQSqtZLAJTCBKFEYJ56wiUUjfnekxEekWkXinVbZ7oZ/UNiEgQ+AHwQaXUL7Je27Im4iLy38D7FrV6zbKoKXMzMB5HKXVB0PVg+wh7mipoH4rM6RoaMIXksqYKfny015wdvLJ1CWcHI3id9kW1ndZoCk19+VQ/rFwWwVp1Dd0P3G3evhv43swdRMQFfAf4slLqvhmP1Zv/CkZ84cgy16NZBNUBF7FketYvX184zsYKL2Ue59wWwbjRO2VPoxH/7xxe+QyjswMTtFT5dMaQZk2xsWLq5D9TCNwOG3abFK9raB4+BrxaRE4CN5v3EZG9IvI5c583A9cDb5slTfRrInIYOAxUA3+7zPVoFkGuWgKlFAPjcaoDLso8DsLR3F/OQTPGsLvRyODpXIVU07ODEzo+oFlzlHudeJ1GULh+hhCICH6XvWBZQ8tqMaGUGgRummX7fuAd5u2vAl/N8fwbl3N8zfKoCRhfxoHxBJuzwi7j8Unik2mqA27KPI6cAWXjuXGCHgdBj5OaMndmfvBKkUor2oei3LxrzrwEjaboEBHqKzyc7p/ItJzIJuB2rFnXkGYNU11mzO2daRFY7p6aMrfpGpojRjCeoNpsV9FQ4V1xi6B7NEoilaZVWwSaNcjGci8Bt4Myz4WdZf1uR8FcQ7rpXAlj9RvqH5ue6WMVmVUH3AS9jnmEIJ4RgsaQlyOdoyu0WoOzA4bF0aKFQLMGuWZrFWWe2U+7/gJaBFoISpiQz4XdJhkLwMKyEAzXkBEsni2zCAwh2LHBmAXQEPLyk6O9pNMq7yMg02nFv//0VKbv0abqtdcxVaN59w2524sHtEWgKQQ2m1AdcNEbzmERlBnB4mRKEUum8brsF7zG4EQiyyLwkUil6R+Pz+oDXQ7f2N/Ox3983FhXwEVdmW42p1lf+N32OXt/rSRaCEqclko/5wanB3gHxuKIQKXPlfFljsWSFwhBMpVmJJKkym8KQYWRJ90xHM2rEAyMx/nYD1/iqtZK/uvuvShF0Qyd12jyRSFdQzpYXOJsqvZxxqzUtegfT1Dld+Gw2wia/szZ2kwMmi4lK+jcEDKEIN8B488+fppIYpK/e+Nugh6nHuGoWZcE3I6CVRZrIShxWqsD9I/FpxWNZQeAg1kWwUyyg8pgZA0BeU8hffbsEJc3h9hau/bnEms0uShk1pAWghKn1ezXY2XjgBEstk7uVobDbJlDU0JgWAR+t4Myj4O+cP78nMlUmhe7wlzWqFtOa9Y3AbcRj4tPrn5RmRaCEmez2cr59MB4ZptVVQxkYgSzzSSwso0s0QCoLXPTN5a/xnMneseIT6a5pLFi/p01mjWM34zBFaK6WAtBidNc6UMEzgxMcHZggt5wbJpraC6LYHCGawigtsxDbx4tgkMdRl2Ctgg0651CjqvUWUMljsdpZ2O5l5O947z5P39OwOMglkxn+hAFvbljBF0jUTxOG76sbKLaoJvnzw/nPF44lszEHRbCoY5Rgh4HzZW6bkCzvilkB1JtEWjYXOPnR0d76BuLc7rfyCCyrvL9Ljs2udAi2H92iP955jyv3F4zrdCsLuihL2y0tp7Jpx9tY+/fPsTJ3rEFr+1QxwiXNlboTqOadU8hLQItBBpaq/2k0orNNX52NwQBqDYtAhEh4J7eZiKaSPGurz1PQ4WXf3zTZdNeq7bMTXwyTTg2yeGOUYYmjDjCM2eG+KefHCcxmea+5zoWtK5wLMnxnjEu1W4hTQngn2ERfO3pcxw0K+n7xmIrKhBaCDSZzKHfumYT/98v7aK2zM32ukDm8TKPc1qwuK1vnP6xOO+/bSflvuluHsul1DMa487P/pwPf+8IqbTifd98geZKH9dureI7BzpJpXMOswMgkpjk7V98FkB3GtWUBIGsAfY9ozE+9N0j/PG9B+gZjXHrJx/nb39wbMWOrWMEGl69q46TfeO86cpGfC4Hz3xw+lC6mTMJrDqBlqoL/fa1ZuuH/eeGmEik+MnRXr53sJPzQxE+8+tXIMC7vvY8T7YNcP32C0eOJibTfOdAB5957BTtQxH+5a7LuaI5lMf/rUZTnFgD7Cfik/zgcDdKGdP4fvkzTzIcSfJid3jFjq2FQENjyMffv/GSnI8HvdOnlHWYU8hmG0lZGzQsgifbBgBIpNJ86LtHqClz8+pddaSVIuhxcP8LXdOEIJZM8c397fzHT0/TORLlkoZyvvhb+2YVC41mPZIdLP6/Q11cVB8k5HPy1KlByjwOTveN52z+uFy0EGjmJehx0DkyVRvQMRyhzOOYtdWDNUf4qVODAGytDdDWN87br2vFaTc8kZc2VkwLGPePxbnj356gazTGFc0V/O0bd3PDjCC0RrPesWIEjx7v48D5Ef7s1h289tJ67nuugzKPg79/4CX6x+MZqzuf6BiBZl6CXifh6JRF0D7HgPqA24HXaWckkqQx5OWdr9iM12nnzn3NmX2aq3ycH5qqZH76zCBdozH+9a7L+da7ruFVO2q1CGhKDqfdxu27N/Czk4Y1/dpL62mp8vOnt+xg5wYjicPK6ss32iLQzEtwRrC4YziSc2awiFAbdHNuMML2ujJ+dW8jr7m0PmP2ArRU+hiOJDM1Baf6JhCBmy+q0wKgKWn+/Teu5HT/OD3h2LThS1tqjeSNU/3jXL25Ku/HXZZFICKVIvKgiJw0/501qiciqazB9fdnbW8VkadFpE1EviEiruWsR7MylHuNcZWptEIpRcccFgFMuYe215Vl0k+zsYrDzpvtr0/1j9NQ4Z113oFGU2psrglwzZbqadvqHlqufwAACOhJREFUgx48TtuKWQTLdQ3dAzyslNoGPGzen42oUmqP+ff6rO3/AHxSKbUVGAbevsz1aFaA7Ori4UiSSCJFo9lyejYsH2Z2Cmo2zWa2keUeOtU/zpaa2ffVaDTG/I3W6gCn+8fn33kpr7/M598BfMm8/SXgDQt9ohg+gBuB+5byfM3qYQWFw9HJTOroXEJQk2URzEbGIhiKkE4rTvdPaCHQaOZhS42fU0VqEdQppbrN2z1Arsofj4jsF5FfiIh1sq8CRpRSVoJ6B9CQ60Ai8k7zNfb39/cvc9maxWAJwWg0OWfqqMXODWVU+JxsrZ395F7mcVLpd3FuMEJ3OEY0mWJLrR5Gr9HMxeaaAB3DkRVpUz1vsFhEHgI2zPLQB7PvKKWUiOQqF21RSnWKyGbgERE5DIwuZqFKqc8CnwXYu3fv3GWpmrwyNaUsSbvpzmmYwyJ4894mXnfZRjzO3D7/pkof7UMRTvUZpq62CDSaudlS4yetyCRi5JN5hUApdXOux0SkV0TqlVLdIlIP9OV4jU7z39Mi8hhwOfAtoEJEHKZV0Ah0LuH/oFlhrDYSlkUQzFFDYGGzSSYnOhctlT4OtA9zql8LgUazEF6xrYbvvPuaFenEu1zX0P3A3ebtu4HvzdxBREIi4jZvVwPXAi8qoz3lo8CvzPV8TeGx2kaHo0m6R6NsrMhtDSyU5kofXSMxjveMEfQ4MoNwNBrN7FT6XVzeHJrT0l4qyxWCjwGvFpGTwM3mfURkr4h8ztznImC/iLyAceL/mFLqRfOxPwfeKyJtGDGDzy9zPZoVIDtG0BuOs6F8+ZWNzVU+UmnFt5/vZEttQNcPaDQFZFkFZUqpQeCmWbbvB95h3n4KmLWRjVLqNLBvOWvQrDw+lx2HTRiNJukJx9hVH1z2a167tZqrWivZUhvg1/Y25WGVGo1mqejKYs28iAhBr5PhSIKB8Th1Qff8T5qHhgov3/jdl+dhdRqNZrnoXkOaBVHuNVpBKAV1eXANaTSa4kELgWZBBD0OjpsdQ+tWoPuhRqMpHFoINAsi6HUyanYgzUewWKPRFA9aCDQLIphVN1CbhxiBRqMpHrQQaBaElUJqtwnVfi0EGs16QguBZkFYQlBb5sZm0zn/Gs16QguBZkFY1cV1QR0f0GjWG1oINAvCsgjyUUOg0WiKCy0EmgUR9Bq1h9oi0GjWH1oINAtiyiLQQqDRrDe0EGgWhBYCjWb9ooVAsyAuqg/yu9dv5qadtYVeikajyTO66ZxmQTjtNj7wmosKvQyNRrMCaItAo9FoShwtBBqNRlPiaCHQaDSaEkcLgUaj0ZQ4Wgg0Go2mxNFCoNFoNCWOFgKNRqMpcbQQaDQaTYkjSqlCr2HRiEg/cG6JT68GBvK4nHxRrOuC4l2bXtfi0OtaPMW6tqWuq0UpVTNz45oUguUgIvuVUnsLvY6ZFOu6oHjXpte1OPS6Fk+xri3f69KuIY1GoylxtBBoNBpNiVOKQvDZQi8gB8W6Lijetel1LQ69rsVTrGvL67pKLkag0Wg0mumUokWg0Wg0miy0EGg0Gk2JU1JCICK3ichxEWkTkXsKuI4mEXlURF4UkaMi8kfm9r8SkU4ROWj+vaYAazsrIofN4+83t1WKyIMictL8N7TKa9qR9Z4cFJGwiPxxod4vEfmCiPSJyJGsbbO+R2LwL+Z37pCIXLHK6/q4iLxkHvs7IlJhbt8kItGs9+4/VnldOT87EfmA+X4dF5FbV3ld38ha01kROWhuX833K9f5YeW+Y0qpkvgD7MApYDPgAl4AdhVoLfXAFebtMuAEsAv4K+B9BX6fzgLVM7b9I3CPefse4B8K/Dn2AC2Fer+A64ErgCPzvUfAa4AfAgJcDTy9yuu6BXCYt/8ha12bsvcrwPs162dn/g5eANxAq/mbta/WumY8/k/AhwvwfuU6P6zYd6yULIJ9QJtS6rRSKgHcC9xRiIUopbqVUs+bt8eAY0BDIdayQO4AvmTe/hLwhgKu5SbglFJqqZXly0Yp9TgwNGNzrvfoDuDLyuAXQIWI1K/WupRSP1FKTZp3fwE0rsSxF7uuObgDuFcpFVdKnQHaMH67q7ouERHgzcDXV+LYczHH+WHFvmOlJAQNQHvW/Q6K4OQrIpuAy4GnzU3vMc27L6y2C8ZEAT8RkedE5J3mtjqlVLd5uweoK8C6LO5k+o+z0O+XRa73qJi+d7+NceVo0SoiB0TkpyLyigKsZ7bPrljer1cAvUqpk1nbVv39mnF+WLHvWCkJQdEhIgHgW8AfK6XCwL8DW4A9QDeGabraXKeUugK4Hfh9Ebk++0Fl2KIFyTkWERfweuCb5qZieL8uoJDvUS5E5IPAJPA1c1M30KyUuhx4L/A/IhJcxSUV5WeXxV1Mv+BY9fdrlvNDhnx/x0pJCDqBpqz7jea2giAiTowP+WtKqW8DKKV6lVIppVQa+C9WyCSeC6VUp/lvH/Adcw29lqlp/tu32usyuR14XinVa66x4O9XFrneo4J/70TkbcBrgV83TyCYrpdB8/ZzGL747au1pjk+u2J4vxzALwPfsLat9vs12/mBFfyOlZIQPAtsE5FW88ryTuD+QizE9D9+HjimlPpE1vZsv94bgSMzn7vC6/KLSJl1GyPQeATjfbrb3O1u4Hurua4spl2lFfr9mkGu9+h+4K1mZsfVwGiWeb/iiMhtwPuB1yulIlnba0TEbt7eDGwDTq/iunJ9dvcDd4qIW0RazXU9s1rrMrkZeEkp1WFtWM33K9f5gZX8jq1GFLxY/jCi6ycw1PyDBVzHdRhm3SHgoPn3GuArwGFz+/1A/SqvazNGxsYLwFHrPQKqgIeBk8BDQGUB3jM/MAiUZ20ryPuFIUbdQBLDH/v2XO8RRibHp83v3GFg7yqvqw3Df2x9z/7D3PdN5md8EHgeeN0qryvnZwd80Hy/jgO3r+a6zO1fBH7v/2/nDnEAAGEAiP3/1/gFJDPX6ilCuGSCMbt5Xq/34dsd88UEQFxpNQTAhRAAxAkBQJwQAMQJAUCcEADECQFA3AFpxbW8ImwNKwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(trend)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:15:29.904528Z",
-     "start_time": "2020-09-01T22:15:29.740136Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x143934ad0>]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(seas)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:15:30.114559Z",
-     "start_time": "2020-09-01T22:15:29.906577Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1437c8610>]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(raw_df['response'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Estimating Coefficients I - full relevance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Assume we observe the data frame `df` and the scaler `REG_BASE`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:19:30.507801Z",
-     "start_time": "2020-09-01T22:19:30.496789Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df = raw_df.copy()\n",
-    "regressor_cols = [f\"regressor_{x}\" for x in range(1, NUM_OF_REGRESSORS + 1)]\n",
-    "response_col = \"response\"\n",
-    "df[regressor_cols] = df[regressor_cols]/REG_BASE\n",
-    "df[regressor_cols] = df[regressor_cols].apply(np.log1p)\n",
-    "df[response_col] = np.log(df[response_col])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:19:38.860282Z",
-     "start_time": "2020-09-01T22:19:30.944605Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:9 of 1000 iterations ended with a divergence (0.9 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.9 to remove the divergences.\n"
-     ]
-    }
-   ],
-   "source": [
-    "mod_auto_ridge = DLTFull(\n",
-    "    response_col=response_col,\n",
-    "    date_col=\"date\",\n",
-    "    regressor_col=regressor_cols,\n",
-    "    seasonality=52,\n",
-    "    seed=SEED,\n",
-    "    is_multiplicative=False,\n",
-    "    regression_penalty='auto_ridge',\n",
-    "    num_warmup=4000,\n",
-    "    num_sample=1000,\n",
-    "    stan_mcmc_control={'adapt_delta':0.9},\n",
-    ")\n",
-    "mod_auto_ridge.fit(df=df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:19:44.484531Z",
-     "start_time": "2020-09-01T22:19:38.862863Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
-     ]
-    }
-   ],
-   "source": [
-    "mod_fixed_ridge1 = DLTFull(\n",
-    "    response_col=response_col,\n",
-    "    date_col=\"date\",\n",
-    "    regressor_col=regressor_cols,\n",
-    "    seasonality=52,\n",
-    "    seed=SEED,\n",
-    "    is_multiplicative=False,\n",
-    "    regression_penalty='fixed_ridge',\n",
-    "    regressor_sigma_prior=[0.5] * NUM_OF_REGRESSORS,\n",
-    "    num_warmup=4000,\n",
-    "    num_sample=1000,\n",
-    ")\n",
-    "mod_fixed_ridge1.fit(df=df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:19:50.512975Z",
-     "start_time": "2020-09-01T22:19:44.487830Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
-     ]
-    }
-   ],
-   "source": [
-    "mod_fixed_ridge2 = DLTFull(\n",
-    "    response_col=response_col,\n",
-    "    date_col=\"date\",\n",
-    "    regressor_col=regressor_cols,\n",
-    "    seasonality=52,\n",
-    "    seed=SEED,\n",
-    "    is_multiplicative=False,\n",
-    "    regression_penalty='fixed_ridge',\n",
-    "    regressor_sigma_prior=[0.05] * NUM_OF_REGRESSORS,\n",
-    "    num_warmup=4000,\n",
-    "    num_sample=1000,\n",
-    ")\n",
-    "mod_fixed_ridge2.fit(df=df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:19:50.521719Z",
-     "start_time": "2020-09-01T22:19:50.516000Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coef_auto_ridge = np.median(mod_auto_ridge._posterior_samples['rr_beta'], axis=0)\n",
-    "coef_fixed_ridge1 =np.median(mod_fixed_ridge1._posterior_samples['rr_beta'], axis=0)\n",
-    "coef_fixed_ridge2 =np.median(mod_fixed_ridge2._posterior_samples['rr_beta'], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:19:51.019336Z",
-     "start_time": "2020-09-01T22:19:50.704226Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lw=3\n",
-    "plt.figure(figsize=(20, 8))\n",
-    "plt.title(\"Weights of the model\")\n",
-    "plt.plot(coef_auto_ridge, color=QualitativePalette.Line4.value[1], linewidth=lw, label=\"Auto Ridge\", alpha=0.5, linestyle='--')\n",
-    "plt.plot(coef_fixed_ridge1, color=QualitativePalette.Line4.value[2], linewidth=lw, label=\"Fixed Ridge1\", alpha=0.5, linestyle='--')\n",
-    "plt.plot(coef_fixed_ridge2, color=QualitativePalette.Line4.value[3], linewidth=lw, label=\"Fixed Ridge2\", alpha=0.5, linestyle='--')\n",
-    "plt.plot(coefs, color=\"black\", linewidth=lw, label=\"Ground truth\")\n",
-    "plt.legend()\n",
-    "plt.grid()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result looks reasonable to the true coefficients."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Checking Model Convergence and Posterior Distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:20:16.734352Z",
-     "start_time": "2020-09-01T22:20:15.696141Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x864 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_posterior_params(mod_auto_ridge, kind='density')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:20:19.986180Z",
-     "start_time": "2020-09-01T22:20:19.203103Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x864 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_posterior_params(mod_auto_ridge, kind='trace')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Estimating Coefficients II - high sparsity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:21:09.305598Z",
-     "start_time": "2020-09-01T22:21:09.300370Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.15602066,  0.05231785,  0.16325487, -0.11182046,  0.0027203 ,\n",
-       "        0.0366778 ,  0.05510171,  0.05726848, -0.14605049,  0.13879701,\n",
-       "       -0.0262531 ,  0.08841281,  0.06847838,  0.07490149,  0.03854218,\n",
-       "        0.1632666 , -0.0597671 , -0.01806245, -0.05170049,  0.32790427,\n",
-       "        0.14872741, -0.02134489, -0.17082483,  0.0363513 ,  0.13933526])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "NUM_OF_REGRESSORS = 25\n",
-    "REG_RELEVANCE = 0.8\n",
-    "COEFS= np.random.default_rng(SEED).normal(.03, .1, NUM_OF_REGRESSORS)\n",
-    "COEFS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:21:10.284616Z",
-     "start_time": "2020-09-01T22:21:10.249652Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>response</th>\n",
-       "      <th>regressor_1</th>\n",
-       "      <th>regressor_2</th>\n",
-       "      <th>regressor_3</th>\n",
-       "      <th>regressor_4</th>\n",
-       "      <th>regressor_5</th>\n",
-       "      <th>regressor_6</th>\n",
-       "      <th>regressor_7</th>\n",
-       "      <th>regressor_8</th>\n",
-       "      <th>regressor_9</th>\n",
-       "      <th>...</th>\n",
-       "      <th>regressor_17</th>\n",
-       "      <th>regressor_18</th>\n",
-       "      <th>regressor_19</th>\n",
-       "      <th>regressor_20</th>\n",
-       "      <th>regressor_21</th>\n",
-       "      <th>regressor_22</th>\n",
-       "      <th>regressor_23</th>\n",
-       "      <th>regressor_24</th>\n",
-       "      <th>regressor_25</th>\n",
-       "      <th>date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2777.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1842.0</td>\n",
-       "      <td>2548.0</td>\n",
-       "      <td>1047.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1755.0</td>\n",
-       "      <td>1858.0</td>\n",
-       "      <td>1871.0</td>\n",
-       "      <td>912.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1272.0</td>\n",
-       "      <td>1469.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3932.0</td>\n",
-       "      <td>2447.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>819.0</td>\n",
-       "      <td>1753.0</td>\n",
-       "      <td>2383.0</td>\n",
-       "      <td>2016-01-10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1623.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1685.0</td>\n",
-       "      <td>1146.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1315.0</td>\n",
-       "      <td>1771.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1352.0</td>\n",
-       "      <td>2183.0</td>\n",
-       "      <td>1198.0</td>\n",
-       "      <td>1432.0</td>\n",
-       "      <td>1030.0</td>\n",
-       "      <td>1262.0</td>\n",
-       "      <td>1887.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2649.0</td>\n",
-       "      <td>2016-01-17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2856.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>791.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1805.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1685.0</td>\n",
-       "      <td>1179.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2223.0</td>\n",
-       "      <td>1749.0</td>\n",
-       "      <td>1745.0</td>\n",
-       "      <td>1751.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2092.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1701.0</td>\n",
-       "      <td>2016-01-24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>872.0</td>\n",
-       "      <td>2278.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1707.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2369.0</td>\n",
-       "      <td>1234.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1842.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1419.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1273.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1334.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1579.0</td>\n",
-       "      <td>2985.0</td>\n",
-       "      <td>2016-01-31</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1181.0</td>\n",
-       "      <td>1630.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>973.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1295.0</td>\n",
-       "      <td>1452.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2595.0</td>\n",
-       "      <td>2804.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1906.0</td>\n",
-       "      <td>1930.0</td>\n",
-       "      <td>1312.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2237.0</td>\n",
-       "      <td>2016-02-07</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 27 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   response  regressor_1  regressor_2  regressor_3  regressor_4  regressor_5  \\\n",
-       "0    2777.0          0.0       1842.0       2548.0       1047.0          0.0   \n",
-       "1    1623.0          0.0          0.0       1685.0       1146.0          0.0   \n",
-       "2    2856.0          0.0          0.0        791.0          0.0          0.0   \n",
-       "3     872.0       2278.0          0.0       1707.0          0.0       2369.0   \n",
-       "4    1181.0       1630.0          0.0          0.0          0.0        973.0   \n",
-       "\n",
-       "   regressor_6  regressor_7  regressor_8  regressor_9  ...  regressor_17  \\\n",
-       "0       1755.0       1858.0       1871.0        912.0  ...        1272.0   \n",
-       "1       1315.0       1771.0          0.0          0.0  ...        1352.0   \n",
-       "2       1805.0          0.0       1685.0       1179.0  ...           0.0   \n",
-       "3       1234.0          0.0       1842.0          0.0  ...        1419.0   \n",
-       "4          0.0          0.0       1295.0       1452.0  ...        2595.0   \n",
-       "\n",
-       "   regressor_18  regressor_19  regressor_20  regressor_21  regressor_22  \\\n",
-       "0        1469.0           0.0        3932.0        2447.0           0.0   \n",
-       "1        2183.0        1198.0        1432.0        1030.0        1262.0   \n",
-       "2        2223.0        1749.0        1745.0        1751.0           0.0   \n",
-       "3           0.0        1273.0           0.0           0.0        1334.0   \n",
-       "4        2804.0           0.0           0.0        1906.0        1930.0   \n",
-       "\n",
-       "   regressor_23  regressor_24  regressor_25       date  \n",
-       "0         819.0        1753.0        2383.0 2016-01-10  \n",
-       "1        1887.0           0.0        2649.0 2016-01-17  \n",
-       "2        2092.0           0.0        1701.0 2016-01-24  \n",
-       "3           0.0        1579.0        2985.0 2016-01-31  \n",
-       "4        1312.0           0.0        2237.0 2016-02-07  \n",
-       "\n",
-       "[5 rows x 27 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_df, trend, seas, coefs = make_ts_multiplicative(\n",
-    "    series_len=200, seasonality=52, coefs=COEFS, regressor_relevance=REG_RELEVANCE,\n",
-    "    regressor_log_loc=0.0, regressor_log_scale=0.2, noise_to_signal_ratio=1.0,\n",
-    "    regression_sparsity=0.5, obs_val_base=1000, regresspr_val_base=REG_BASE, trend_type='rw',\n",
-    "    seas_scale=.05, response_col='response', seed=SEED\n",
-    ")\n",
-    "raw_df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:21:13.553890Z",
-     "start_time": "2020-09-01T22:21:13.550084Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.          0.05231785  0.         -0.11182046  0.          0.\n",
-      "  0.          0.          0.          0.13879701  0.          0.\n",
-      "  0.          0.07490149  0.          0.          0.          0.\n",
-      "  0.          0.32790427  0.          0.          0.          0.\n",
-      "  0.        ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(coefs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:21:14.012608Z",
-     "start_time": "2020-09-01T22:21:13.996943Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df = raw_df.copy()\n",
-    "regressor_cols = [f\"regressor_{x}\" for x in range(1, NUM_OF_REGRESSORS + 1)]\n",
-    "response_col = \"response\"\n",
-    "df[regressor_cols] = df[regressor_cols]/REG_BASE\n",
-    "df[regressor_cols] = df[regressor_cols].apply(np.log1p)\n",
-    "df[response_col] = np.log(df[response_col])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:21:14.986291Z",
-     "start_time": "2020-09-01T22:21:14.718032Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "mod_lasso = DLTMAP(\n",
-    "    response_col=response_col,\n",
-    "    date_col=\"date\",\n",
-    "    regressor_col=regressor_cols,\n",
-    "    seasonality=52,\n",
-    "    seed=SEED,\n",
-    "    is_multiplicative=False,\n",
-    "    regression_penalty='lasso',\n",
-    "    num_warmup=4000,\n",
-    "    num_sample=1000,\n",
-    ")\n",
-    "mod_lasso.fit(df=df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:21:45.423823Z",
-     "start_time": "2020-09-01T22:21:16.369387Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:104 of 1000 iterations ended with a divergence (10.4 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.9 to remove the divergences.\n"
-     ]
-    }
-   ],
-   "source": [
-    "mod_auto_ridge = DLTFull(\n",
-    "    response_col=response_col,\n",
-    "    date_col=\"date\",\n",
-    "    regressor_col=regressor_cols,\n",
-    "    seasonality=52,\n",
-    "    seed=SEED,\n",
-    "    is_multiplicative=False,\n",
-    "    regression_penalty='auto_ridge',\n",
-    "    num_warmup=4000,\n",
-    "    num_sample=1000,\n",
-    "    stan_mcmc_control={'adapt_delta':0.9},\n",
-    ")\n",
-    "mod_auto_ridge.fit(df=df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:22:15.402150Z",
-     "start_time": "2020-09-01T22:22:08.845348Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
-     ]
-    }
-   ],
-   "source": [
-    "mod_fixed_ridge1 = DLTFull(\n",
-    "    response_col=response_col,\n",
-    "    date_col=\"date\",\n",
-    "    regressor_col=regressor_cols,\n",
-    "    seasonality=52,\n",
-    "    seed=SEED,\n",
-    "    is_multiplicative=False,\n",
-    "    regression_penalty='fixed_ridge',\n",
-    "    num_warmup=4000,\n",
-    "    num_sample=1000,\n",
-    ")\n",
-    "mod_fixed_ridge1.fit(df=df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:22:23.222245Z",
-     "start_time": "2020-09-01T22:22:16.554203Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
-     ]
-    }
-   ],
-   "source": [
-    "mod_fixed_ridge2 = DLTFull(\n",
-    "    response_col=response_col,\n",
-    "    date_col=\"date\",\n",
-    "    regressor_col=regressor_cols,\n",
-    "    seasonality=52,\n",
-    "    seed=SEED,\n",
-    "    is_multiplicative=False,\n",
-    "    regression_penalty='fixed_ridge',\n",
-    "    regressor_sigma_prior=[0.1] * NUM_OF_REGRESSORS,\n",
-    "    num_warmup=4000,\n",
-    "    num_sample=1000,\n",
-    ")\n",
-    "mod_fixed_ridge2.fit(df=df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:22:54.042140Z",
-     "start_time": "2020-09-01T22:22:54.036438Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coef_lasso = np.median(mod_lasso._posterior_samples['rr_beta'], axis=0)\n",
-    "coef_auto_ridge = np.median(mod_auto_ridge._posterior_samples['rr_beta'], axis=0)\n",
-    "coef_fixed_ridge1 =np.median(mod_fixed_ridge1._posterior_samples['rr_beta'], axis=0)\n",
-    "coef_fixed_ridge2 =np.median(mod_fixed_ridge2._posterior_samples['rr_beta'], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:22:55.440754Z",
-     "start_time": "2020-09-01T22:22:55.204780Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 1152x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lw=3\n",
-    "plt.figure(figsize=(16, 8))\n",
-    "plt.title(\"Weights of the model\")\n",
-    "plt.plot(coef_lasso, color=QualitativePalette.Line4.value[0], linewidth=lw, label=\"Lasso\", alpha=1.0, linestyle='--')\n",
-    "plt.plot(coef_auto_ridge, color=QualitativePalette.Line4.value[1], linewidth=lw, label=\"Auto Ridge\", alpha=0.5, linestyle='--')\n",
-    "plt.plot(coef_fixed_ridge1, color=QualitativePalette.Line4.value[2], linewidth=lw, label=\"Fixed Ridge1\", alpha=0.5, linestyle='--')\n",
-    "plt.plot(coef_fixed_ridge2, color=QualitativePalette.Line4.value[3], linewidth=lw, label=\"Fixed Ridge2\", alpha=0.5, linestyle='--')\n",
-    "plt.plot(coefs, color=\"black\", linewidth=lw, label=\"Ground truth\")\n",
-    "plt.legend()\n",
-    "plt.grid()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Again, with high dimensional data, the result looks reasonable to the true coefficients with irrelevant regressors fed into the model."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "env37_orbit",
-   "language": "python",
-   "name": "env37_orbit"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.7"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/archive/Regression_Advance2.ipynb b/examples/archive/Regression_Advance2.ipynb
deleted file mode 100644
index 6cd5ac4f..00000000
--- a/examples/archive/Regression_Advance2.ipynb
+++ /dev/null
@@ -1,716 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Regressorion with Orbit - Advance II"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Continue from demo I, we revisit the regression from with multivariate regressors and observe the limit of each regression penalty."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:29:15.908639Z",
-     "start_time": "2020-09-01T22:29:13.778397Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import gc\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "\n",
-    "import statsmodels.api as sm\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "from orbit.models.lgt import LGTMAP, LGTAggregated, LGTFull\n",
-    "from orbit.models.dlt import DLTMAP, DLTAggregated, DLTFull\n",
-    "\n",
-    "from orbit.diagnostics.plot import plot_posterior_params\n",
-    "from orbit.constants.palette import QualitativePalette\n",
-    "from orbit.utils.simulation import make_ts_multiplicative"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:29:15.913279Z",
-     "start_time": "2020-09-01T22:29:15.910337Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.19.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# randomization is using numpy with this version\n",
-    "print(np.__version__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simulation of Regression with Trend"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This time, we simulate regressor in a `multivariate normal` such that our observed values of regressors happen with covariance structure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:29:17.917785Z",
-     "start_time": "2020-09-01T22:29:17.912346Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# To scale regressor values in a nicer way\n",
-    "REG_BASE = 1000\n",
-    "COEFS = np.array([0.03, 0.08, -0.3, 0.35, 0.22], dtype=np.float64)\n",
-    "SEED = 2020\n",
-    "COVAR = np.array([[0.2, 0.3, 0.0, 0.0, 0.0], \n",
-    "                 [0.3, 0.2, 0.4, 0.0, 0.0], \n",
-    "                 [0.0, 0.4, 0.2, 0.0, 0.0], \n",
-    "                 [0.0, 0.0, 0.0, 0.2, 0.1], \n",
-    "                 [0.0, 0.0, 0.0, 0.1, 0.2]], dtype=np.float64)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:29:47.471325Z",
-     "start_time": "2020-09-01T22:29:47.456336Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.03,  0.08, -0.3 ,  0.35,  0.22])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Looks like the RuntimeWarning is not impactful\n",
-    "raw_df, trend, seas, coefs = make_ts_multiplicative(\n",
-    "    series_len=200, seasonality=52, coefs=COEFS,\n",
-    "    regressor_log_loc=0.0, noise_to_signal_ratio=1.0, \n",
-    "    regressor_log_cov=COVAR,\n",
-    "    regression_sparsity=0.5, obs_val_base=1000, regresspr_val_base=REG_BASE, trend_type='rw',\n",
-    "    seas_scale=.05, response_col='response', seed=SEED\n",
-    ")\n",
-    "num_of_regressors = len(coefs)\n",
-    "coefs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Estimating Coefficients I - full relevance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Assume we observe the data frame `df` and the scaler `REG_BASE`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:29:51.098571Z",
-     "start_time": "2020-09-01T22:29:51.087299Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df = raw_df.copy()\n",
-    "regressor_cols = [f\"regressor_{x}\" for x in range(1, num_of_regressors + 1)]\n",
-    "response_col = \"response\"\n",
-    "df[regressor_cols] = df[regressor_cols]/REG_BASE\n",
-    "df[regressor_cols] = df[regressor_cols].apply(np.log1p)\n",
-    "df[response_col] = np.log(df[response_col])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:25:20.913589Z",
-     "start_time": "2020-09-01T22:25:12.707663Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:10 of 1000 iterations ended with a divergence (1 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.9 to remove the divergences.\n"
-     ]
-    }
-   ],
-   "source": [
-    "mod_auto_ridge = DLTFull(\n",
-    "    response_col=response_col,\n",
-    "    date_col=\"date\",\n",
-    "    regressor_col=regressor_cols,\n",
-    "    seasonality=52,\n",
-    "    seed=SEED,\n",
-    "    is_multiplicative=False,\n",
-    "    regression_penalty='auto_ridge',\n",
-    "    num_warmup=4000,\n",
-    "    num_sample=1000,\n",
-    "    stan_mcmc_control={'adapt_delta':0.9},\n",
-    ")\n",
-    "mod_auto_ridge.fit(df=df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:25:26.122858Z",
-     "start_time": "2020-09-01T22:25:20.916682Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
-     ]
-    }
-   ],
-   "source": [
-    "mod_fixed_ridge1 = DLTFull(\n",
-    "    response_col=response_col,\n",
-    "    date_col=\"date\",\n",
-    "    regressor_col=regressor_cols,\n",
-    "    seasonality=52,\n",
-    "    seed=SEED,\n",
-    "    is_multiplicative=False,\n",
-    "    regression_penalty='fixed_ridge',\n",
-    "    regressor_sigma_prior=[0.5] * num_of_regressors,\n",
-    "    num_warmup=4000,\n",
-    "    num_sample=1000,\n",
-    ")\n",
-    "mod_fixed_ridge1.fit(df=df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:25:32.821170Z",
-     "start_time": "2020-09-01T22:25:27.371243Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
-     ]
-    }
-   ],
-   "source": [
-    "mod_fixed_ridge2 = DLTFull(\n",
-    "    response_col=response_col,\n",
-    "    date_col=\"date\",\n",
-    "    regressor_col=regressor_cols,\n",
-    "    seasonality=52,\n",
-    "    seed=SEED,\n",
-    "    is_multiplicative=False,\n",
-    "    regression_penalty='fixed_ridge',\n",
-    "    regressor_sigma_prior=[0.05] * num_of_regressors,\n",
-    "    num_warmup=4000,\n",
-    "    num_sample=1000,\n",
-    ")\n",
-    "mod_fixed_ridge2.fit(df=df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:25:38.634040Z",
-     "start_time": "2020-09-01T22:25:38.629026Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coef_auto_ridge = np.median(mod_auto_ridge._posterior_samples['rr_beta'], axis=0)\n",
-    "coef_fixed_ridge1 =np.median(mod_fixed_ridge1._posterior_samples['rr_beta'], axis=0)\n",
-    "coef_fixed_ridge2 =np.median(mod_fixed_ridge2._posterior_samples['rr_beta'], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Small `sigma_prior` may lead to over-regularize."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:25:46.222186Z",
-     "start_time": "2020-09-01T22:25:45.970482Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 1152x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lw=3\n",
-    "plt.figure(figsize=(16, 8))\n",
-    "plt.title(\"Weights of the model\")\n",
-    "plt.plot(coef_auto_ridge, color=QualitativePalette.Line4.value[0], linewidth=lw, label=\"Auto Ridge\", alpha=0.8, linestyle='--')\n",
-    "plt.plot(coef_fixed_ridge1, color=QualitativePalette.Line4.value[1], linewidth=lw, label=\"Fixed Ridge1\", alpha=0.8, linestyle='--')\n",
-    "plt.plot(coef_fixed_ridge2, color=QualitativePalette.Line4.value[2], linewidth=lw, label=\"Fixed Ridge2\", alpha=0.8, linestyle='--')\n",
-    "plt.plot(coefs, color=\"black\", linewidth=lw, label=\"Ground truth\", alpha=0.5)\n",
-    "plt.legend()\n",
-    "plt.grid()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:29:58.014963Z",
-     "start_time": "2020-09-01T22:29:58.010834Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.05 0.1  0.15 0.2  0.25 0.3  0.35 0.4  0.45 0.5 ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "scale_priors = np.round(np.arange(0.05, 0.5 + 0.01, 0.05), 2)\n",
-    "print(scale_priors)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:32:31.184308Z",
-     "start_time": "2020-09-01T22:29:59.926161Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting with scale prior: 0.05\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:26 of 1000 iterations ended with a divergence (2.6 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.9 to remove the divergences.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting with scale prior: 0.1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:29 of 1000 iterations ended with a divergence (2.9 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.9 to remove the divergences.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting with scale prior: 0.15\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:17 of 1000 iterations ended with a divergence (1.7 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.9 to remove the divergences.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting with scale prior: 0.2\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:9 of 1000 iterations ended with a divergence (0.9 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.9 to remove the divergences.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting with scale prior: 0.25\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:10 of 1000 iterations ended with a divergence (1 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.9 to remove the divergences.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting with scale prior: 0.3\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:30 of 1000 iterations ended with a divergence (3 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.9 to remove the divergences.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting with scale prior: 0.35\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:4 of 1000 iterations ended with a divergence (0.4 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.9 to remove the divergences.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting with scale prior: 0.4\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:2 of 1000 iterations ended with a divergence (0.2 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.9 to remove the divergences.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting with scale prior: 0.45\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:10 of 1000 iterations ended with a divergence (1 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.9 to remove the divergences.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting with scale prior: 0.5\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:10 of 1000 iterations ended with a divergence (1 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.9 to remove the divergences.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "285"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coef_sum_list = []\n",
-    "for idx, scale_prior in enumerate(scale_priors):\n",
-    "    print(f\"Fitting with scale prior: {scale_prior}\")\n",
-    "    # fit a fixed ridge\n",
-    "    mod = DLTAggregated(\n",
-    "        response_col=response_col,\n",
-    "        date_col=\"date\",\n",
-    "        regressor_col=regressor_cols,\n",
-    "        seasonality=52,\n",
-    "        seed=SEED,\n",
-    "        is_multiplicative=False,\n",
-    "        regression_penalty='fixed_ridge',\n",
-    "        regressor_sigma_prior=[scale_prior] * num_of_regressors,\n",
-    "        num_sample=1000,\n",
-    "        num_warmup=4000,\n",
-    "    )\n",
-    "    mod.fit(df=df)\n",
-    "    temp = mod.get_regression_coefs()\n",
-    "    temp['scale_prior'] = scale_prior\n",
-    "    temp.rename(columns={'coefficient': 'fixed_ridge_estimate'}, inplace=True)\n",
-    "    temp.drop(['regressor_sign'], inplace=True, axis=1)\n",
-    "    # fit a auto ridge\n",
-    "    mod = DLTAggregated(\n",
-    "        response_col=response_col,\n",
-    "        date_col=\"date\",\n",
-    "        regressor_col=regressor_cols,\n",
-    "        seasonality=52,\n",
-    "        seed=SEED,\n",
-    "        is_multiplicative=False,\n",
-    "        regression_penalty='auto_ridge',\n",
-    "        auto_ridge_scale=scale_prior,\n",
-    "        regressor_sigma_prior=[scale_prior] * num_of_regressors,\n",
-    "        num_sample=1000,\n",
-    "        num_warmup=4000,\n",
-    "        stan_mcmc_control={'adapt_delta':0.9},\n",
-    "    )\n",
-    "    mod.fit(df=df)\n",
-    "    temp2 = mod.get_regression_coefs()\n",
-    "    temp['auto_ridge_estimate'] = temp2['coefficient'].values\n",
-    "    coef_sum_list.append(temp)\n",
-    "coef_summary = pd.concat(coef_sum_list, axis=0)\n",
-    "del temp, coef_sum_list\n",
-    "gc.collect()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-09-01T22:32:31.907163Z",
-     "start_time": "2020-09-01T22:32:31.186938Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x864 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "figsize=(12, 12)\n",
-    "fig, axes = plt.subplots(len(regressor_cols), 1, facecolor='w', figsize=figsize)\n",
-    "idx = 0\n",
-    "lw=3\n",
-    "# for idx, reg in enumerate(regressor_cols):\n",
-    "for ax, reg in zip(axes, regressor_cols):\n",
-    "\n",
-    "    sub_df = coef_summary[coef_summary['regressor'] == reg]\n",
-    "    x = sub_df['scale_prior'].values\n",
-    "    y = sub_df['fixed_ridge_estimate'].values\n",
-    "    ax.plot(x, y, marker='.', color=QualitativePalette.Line4.value[0], label=\"fixed_ridge_estimate\", \n",
-    "            lw=lw, markersize=20, alpha=0.8)\n",
-    "    y = sub_df['auto_ridge_estimate'].values\n",
-    "    ax.plot(x, y, marker='.', color=QualitativePalette.Line4.value[1], label=\"auto_ridge_estimate\", \n",
-    "            lw=lw, markersize=20, alpha=0.8)\n",
-    " \n",
-    "    ax.axhline(y=coefs[idx], marker=None, color='black', label='ground_truth', lw=lw, alpha=0.5,  linestyle='--')\n",
-    "    ax.grid(True, which='both', c='gray', ls='-', lw=1, alpha=0.2)\n",
-    "    ax.set_title(reg, fontsize=16)\n",
-    "    ax.set_ylim(coefs[idx] - 0.15, coefs[idx] + 0.15)\n",
-    "    idx += 1\n",
-    "\n",
-    "handles, labels = ax.get_legend_handles_labels()\n",
-    "fig.legend(handles, labels, loc='lower right')\n",
-    "fig.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using `auto_ridge` would give more stable estimation where user has less worry about picking the right size of scale prior in setting non-informative prior."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.7"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/hyper_parameters_tuning.ipynb b/examples/archive/hyper_parameters_tuning.ipynb
similarity index 100%
rename from examples/hyper_parameters_tuning.ipynb
rename to examples/archive/hyper_parameters_tuning.ipynb
diff --git a/examples/archive/mcmc_diagnostic.ipynb b/examples/archive/mcmc_diagnostic.ipynb
deleted file mode 100644
index 9b8715eb..00000000
--- a/examples/archive/mcmc_diagnostic.ipynb
+++ /dev/null
@@ -1,610 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Diagnostic Visualization demo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this demo, we want to demonstrate how to use the plotting utilities to visualize the posterior samples in some of the Orbit models (e.g. **SVI** and **Full Bayesian**) . Those could be very useful in practice when checking the convergence status of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-10-25T19:44:09.144208Z",
-     "start_time": "2021-10-25T19:44:09.141943Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "from orbit.models import LGT, DLT\n",
-    "from orbit.diagnostics.plot import plot_posterior_params\n",
-    "from orbit.utils.dataset import load_iclaims\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:39:28.790080Z",
-     "start_time": "2021-09-11T01:39:28.604277Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "week              datetime64[ns]\n",
-       "claims                   float64\n",
-       "trend.unemploy           float64\n",
-       "trend.filling            float64\n",
-       "trend.job                float64\n",
-       "sp500                    float64\n",
-       "vix                      float64\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_df = load_iclaims()\n",
-    "\n",
-    "raw_df.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:39:28.805798Z",
-     "start_time": "2021-09-11T01:39:28.792704Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "      <th>sp500</th>\n",
-       "      <th>vix</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.318452</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.417633</td>\n",
-       "      <td>0.122654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>13.624218</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.168794</td>\n",
-       "      <td>-0.425480</td>\n",
-       "      <td>0.110445</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>13.398741</td>\n",
-       "      <td>0.236143</td>\n",
-       "      <td>-0.292477</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.465229</td>\n",
-       "      <td>0.532339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>13.137549</td>\n",
-       "      <td>0.203353</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.106918</td>\n",
-       "      <td>-0.481751</td>\n",
-       "      <td>0.428645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>13.196760</td>\n",
-       "      <td>0.134360</td>\n",
-       "      <td>-0.242466</td>\n",
-       "      <td>0.074483</td>\n",
-       "      <td>-0.488929</td>\n",
-       "      <td>0.487404</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week     claims  trend.unemploy  trend.filling  trend.job     sp500  \\\n",
-       "0 2010-01-03  13.386595        0.219882      -0.318452   0.117500 -0.417633   \n",
-       "1 2010-01-10  13.624218        0.219882      -0.194838   0.168794 -0.425480   \n",
-       "2 2010-01-17  13.398741        0.236143      -0.292477   0.117500 -0.465229   \n",
-       "3 2010-01-24  13.137549        0.203353      -0.194838   0.106918 -0.481751   \n",
-       "4 2010-01-31  13.196760        0.134360      -0.242466   0.074483 -0.488929   \n",
-       "\n",
-       "        vix  \n",
-       "0  0.122654  \n",
-       "1  0.110445  \n",
-       "2  0.532339  \n",
-       "3  0.428645  \n",
-       "4  0.487404  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:39:28.811849Z",
-     "start_time": "2021-09-11T01:39:28.808536Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df=raw_df.copy()\n",
-    "\n",
-    "test_size=52\n",
-    "\n",
-    "train_df=df[:-test_size]\n",
-    "test_df=df[-test_size:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fit a Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:39:28.815964Z",
-     "start_time": "2021-09-11T01:39:28.813647Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "DATE_COL=\"week\"\n",
-    "RESPONSE_COL=\"claims\"\n",
-    "REGRESSOR_COL=['trend.unemploy', 'trend.filling', 'trend.job']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:39:28.821398Z",
-     "start_time": "2021-09-11T01:39:28.817748Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "dlt_mcmc = DLT(response_col=RESPONSE_COL,\n",
-    "               date_col=DATE_COL,\n",
-    "               regressor_col=REGRESSOR_COL,\n",
-    "               regressor_sign=[\"+\", '+', '='],\n",
-    "               seasonality=52,         \n",
-    "               num_warmup=2000,\n",
-    "               num_sample=2000,\n",
-    "               chains=4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Posterior Diagnostic Viz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:39:40.705409Z",
-     "start_time": "2021-09-11T01:39:28.823417Z"
-    },
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Gradient evaluation took 0.00083 seconds\n",
-      "1000 transitions using 10 leapfrog steps per transition would take 8.3 seconds.\n",
-      "Adjust your expectations accordingly!\n",
-      "\n",
-      "\n",
-      "\n",
-      "Gradient evaluation took 0.00079 seconds\n",
-      "1000 transitions using 10 leapfrog steps per transition would take 7.9 seconds.\n",
-      "Adjust your expectations accordingly!\n",
-      "\n",
-      "\n",
-      "\n",
-      "Gradient evaluation took 0.000784 seconds\n",
-      "1000 transitions using 10 leapfrog steps per transition would take 7.84 seconds.\n",
-      "Adjust your expectations accordingly!\n",
-      "\n",
-      "\n",
-      "\n",
-      "Gradient evaluation took 0.000836 seconds\n",
-      "1000 transitions using 10 leapfrog steps per transition would take 8.36 seconds.\n",
-      "Adjust your expectations accordingly!\n",
-      "\n",
-      "\n",
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-      "Iteration: 1000 / 1000 [100%]  (Sampling)\n",
-      "Iteration: 1000 / 1000 [100%]  (Sampling)\n",
-      "\n",
-      " Elapsed Time: 5.70148 seconds (Warm-up)\n",
-      "               3.42737 seconds (Sampling)\n",
-      "               9.12885 seconds (Total)\n",
-      "\n",
-      "\n",
-      " Elapsed Time: 5.72275 seconds (Warm-up)\n",
-      "               3.40772 seconds (Sampling)\n",
-      "               9.13047 seconds (Total)\n",
-      "\n",
-      "Iteration: 1000 / 1000 [100%]  (Sampling)\n",
-      "\n",
-      " Elapsed Time: 5.87368 seconds (Warm-up)\n",
-      "               3.41324 seconds (Sampling)\n",
-      "               9.28692 seconds (Total)\n",
-      "\n",
-      "Iteration: 900 / 1000 [ 90%]  (Sampling)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration: 1000 / 1000 [100%]  (Sampling)\n",
-      "\n",
-      " Elapsed Time: 6.9263 seconds (Warm-up)\n",
-      "               3.34168 seconds (Sampling)\n",
-      "               10.268 seconds (Total)\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x14ac327f0>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dlt_mcmc.fit(df=train_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### histogram"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you see certain warning message related to `scipy`, which can be resolved by upgrading to `scipy>=1.2` for python >= 3.6.\n",
-    "\n",
-    "You can specify a path string (e.g., './density.png') to save the chart."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:39:40.712119Z",
-     "start_time": "2021-09-11T01:39:40.709183Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "## Suppressing Seaborn warning is temporary solution until we upgrade Seaborn version\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:39:41.272330Z",
-     "start_time": "2021-09-11T01:39:40.714124Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 800x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_posterior_params(dlt_mcmc, kind='hist')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### trace"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Currently, trace plot may not represent the actual sample process for different chainse since this information is not stored in orbit model objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:39:41.660626Z",
-     "start_time": "2021-09-11T01:39:41.274142Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 800x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_posterior_params(dlt_mcmc, kind='trace')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### pair plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:39:43.084523Z",
-     "start_time": "2021-09-11T01:39:41.662507Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 750x750 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_posterior_params(dlt_mcmc, kind='pair', pair_type='scatter')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:39:45.573865Z",
-     "start_time": "2021-09-11T01:39:43.086698Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 750x750 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_posterior_params(dlt_mcmc,\n",
-    "                          kind='pair',\n",
-    "                          pair_type='reg')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.8"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/archive/mcmc_diagnostic_arviz.ipynb b/examples/archive/mcmc_diagnostic_arviz.ipynb
deleted file mode 100644
index e7b72253..00000000
--- a/examples/archive/mcmc_diagnostic_arviz.ipynb
+++ /dev/null
@@ -1,698 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Diagnostic Visualization demo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this demo, we want to demonstrate how to use the plotting utilities to visualize the posterior samples in some of the Orbit models (e.g. **SVI** and **Full Bayesian**)\n",
-    "\n",
-    "We leveraged some built-in functions and [arviz](https://arviz-devs.github.io/arviz/index.html) for the plots. ArviZ is a Python package for exploratory analysis of Bayesian models, includes functions for posterior analysis, data storage, model checking, comparison and diagnostics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-10-25T19:44:55.798552Z",
-     "start_time": "2021-10-25T19:44:53.593403Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "cannot import name 'plot_param_diagnostics' from 'orbit.diagnostics.plot' (/Users/zhishiw/Desktop/uTS-py/orbit/orbit/diagnostics/plot.py)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-94320b57e33b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0morbit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLGT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDLT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0morbit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_iclaims\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0morbit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiagnostics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplot_param_diagnostics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_posterior_params\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mImportError\u001b[0m: cannot import name 'plot_param_diagnostics' from 'orbit.diagnostics.plot' (/Users/zhishiw/Desktop/uTS-py/orbit/orbit/diagnostics/plot.py)"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "import arviz as az\n",
-    "\n",
-    "from orbit.models import LGT, DLT\n",
-    "from orbit.utils.dataset import load_iclaims\n",
-    "from orbit.diagnostics.plot import plot_param_diagnostics, plot_posterior_params\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "%reload_ext autoreload"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:44:19.849848Z",
-     "start_time": "2021-09-11T01:44:19.484118Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "week              datetime64[ns]\n",
-       "claims                   float64\n",
-       "trend.unemploy           float64\n",
-       "trend.filling            float64\n",
-       "trend.job                float64\n",
-       "sp500                    float64\n",
-       "vix                      float64\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_df = load_iclaims()\n",
-    "raw_df.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:44:19.864718Z",
-     "start_time": "2021-09-11T01:44:19.852124Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "      <th>sp500</th>\n",
-       "      <th>vix</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.318452</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.417633</td>\n",
-       "      <td>0.122654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>13.624218</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.168794</td>\n",
-       "      <td>-0.425480</td>\n",
-       "      <td>0.110445</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>13.398741</td>\n",
-       "      <td>0.236143</td>\n",
-       "      <td>-0.292477</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.465229</td>\n",
-       "      <td>0.532339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>13.137549</td>\n",
-       "      <td>0.203353</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.106918</td>\n",
-       "      <td>-0.481751</td>\n",
-       "      <td>0.428645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>13.196760</td>\n",
-       "      <td>0.134360</td>\n",
-       "      <td>-0.242466</td>\n",
-       "      <td>0.074483</td>\n",
-       "      <td>-0.488929</td>\n",
-       "      <td>0.487404</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week     claims  trend.unemploy  trend.filling  trend.job     sp500  \\\n",
-       "0 2010-01-03  13.386595        0.219882      -0.318452   0.117500 -0.417633   \n",
-       "1 2010-01-10  13.624218        0.219882      -0.194838   0.168794 -0.425480   \n",
-       "2 2010-01-17  13.398741        0.236143      -0.292477   0.117500 -0.465229   \n",
-       "3 2010-01-24  13.137549        0.203353      -0.194838   0.106918 -0.481751   \n",
-       "4 2010-01-31  13.196760        0.134360      -0.242466   0.074483 -0.488929   \n",
-       "\n",
-       "        vix  \n",
-       "0  0.122654  \n",
-       "1  0.110445  \n",
-       "2  0.532339  \n",
-       "3  0.428645  \n",
-       "4  0.487404  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fit a Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:44:19.874641Z",
-     "start_time": "2021-09-11T01:44:19.872233Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "DATE_COL=\"week\"\n",
-    "RESPONSE_COL=\"claims\"\n",
-    "REGRESSOR_COL=['trend.unemploy', 'trend.filling', 'trend.job']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:44:19.880388Z",
-     "start_time": "2021-09-11T01:44:19.876841Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "dlt = DLT(response_col=RESPONSE_COL,\n",
-    "          date_col=DATE_COL,\n",
-    "          regressor_col=REGRESSOR_COL,\n",
-    "          seasonality=52,\n",
-    "          num_warmup=2000,\n",
-    "          num_sample=2000,\n",
-    "          chains=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:44:31.838944Z",
-     "start_time": "2021-09-11T01:44:19.882321Z"
-    },
-    "scrolled": false,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x15520db50>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dlt.fit(df=raw_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Orbit Built-in Diagnostic Plot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### histogram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 800x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_posterior_params(dlt, kind='hist');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### trace plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 800x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_posterior_params(dlt, kind='trace');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### pair plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 750x750 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_posterior_params(dlt, kind='pair', pair_type='scatter');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Posterior Diagnostic Viz\n",
-    "\n",
-    "Alternatively, we can use `arviz` package in the backend to visualize posteriors distribution and model convergence status."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### trace plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:44:32.721626Z",
-     "start_time": "2021-09-11T01:44:31.842761Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x600 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_param_diagnostics(dlt,\n",
-    "                       which='trace',\n",
-    "                       kind=\"trace\", \n",
-    "                       legend=True,\n",
-    "                       chain_prop={\"color\": [\"r\", \"b\",\"g\",\"y\"]});"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:44:34.076897Z",
-     "start_time": "2021-09-11T01:44:32.723601Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x600 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_param_diagnostics(dlt,\n",
-    "                       which='trace',\n",
-    "                       kind=\"rank_bars\",\n",
-    "                       legend=True,\n",
-    "                       chain_prop={\"color\": [\"r\", \"b\",\"g\",\"y\"]});"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:44:34.919415Z",
-     "start_time": "2021-09-11T01:44:34.079055Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x600 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_param_diagnostics(dlt,\n",
-    "                       which='trace',\n",
-    "                       kind=\"rank_vlines\",\n",
-    "                       legend=True,\n",
-    "                       chain_prop={\"color\": [\"r\", \"b\",\"g\",\"y\"]});"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### density plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:45:00.386407Z",
-     "start_time": "2021-09-11T01:44:59.888507Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 2070x460 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_param_diagnostics(dlt, which='density', shade=0.1);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### pair plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:44:35.760756Z",
-     "start_time": "2021-09-11T01:44:35.351974Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 2070x1380 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_param_diagnostics(\n",
-    "    dlt, \n",
-    "    which='pair',\n",
-    "    kind=[\"scatter\", \"kde\"],\n",
-    "    kde_kwargs={\"fill_last\": False},\n",
-    "    marginals=True,\n",
-    "    point_estimate=\"median\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### forest plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:44:35.994606Z",
-     "start_time": "2021-09-11T01:44:35.762272Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 600x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_param_diagnostics(dlt, which='forest');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### posterior plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:44:36.785428Z",
-     "start_time": "2021-09-11T01:44:36.237960Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 2070x460 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_param_diagnostics(dlt, which='posterior');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### autocorr plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-11T01:44:38.508366Z",
-     "start_time": "2021-09-11T01:44:36.786753Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2070x1840 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_param_diagnostics(dlt, which='autocorr');"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.8"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/archive/orbit_m3_backtest.ipynb b/examples/archive/orbit_m3_backtest.ipynb
new file mode 100644
index 00000000..a72b98e9
--- /dev/null
+++ b/examples/archive/orbit_m3_backtest.ipynb
@@ -0,0 +1,513 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "994f63e1",
+   "metadata": {},
+   "source": [
+    "# A Demo on Backtesting M3 with Various Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be2cd336",
+   "metadata": {},
+   "source": [
+    "This notebook aims to\n",
+    "1. provide a simple demo how to backtest models with orbit provided functions.  \n",
+    "2. add transperancy how our accuracy metrics are derived in https://arxiv.org/abs/2004.08492.\n",
+    "\n",
+    "Due to versioning and random seed, there could be subtle difference for the final numbers. This notebook should also be available in colab."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2166de6a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-07-13T22:37:42.983007Z",
+     "start_time": "2021-07-13T22:37:08.360143Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JkM4yXCFaee8",
+    "outputId": "31a50da2-eb80-4769-a421-fe670956ae85"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install orbit-ml==1.0.13\n",
+    "!pip install fbprophet==0.7.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8a85a5b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-09-03T00:44:11.247033Z",
+     "start_time": "2021-09-03T00:44:11.239738Z"
+    },
+    "id": "environmental-dealing"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tqdm\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import inspect\n",
+    "import random\n",
+    "from fbprophet import Prophet\n",
+    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "\n",
+    "import orbit\n",
+    "from orbit.models import DLT\n",
+    "from orbit.utils.dataset import load_m3monthly\n",
+    "from orbit.diagnostics.backtest import BackTester\n",
+    "from orbit.diagnostics.metrics import smape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be3b8390",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-09-03T00:44:12.661903Z",
+     "start_time": "2021-09-03T00:44:12.659176Z"
+    },
+    "id": "0_43vxJ3cG2J"
+   },
+   "outputs": [],
+   "source": [
+    "seed=2021\n",
+    "n_sample=10\n",
+    "random.seed(seed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e394eb60",
+   "metadata": {},
+   "source": [
+    "We can load the m3 dataset from orbit repository.  For demo purpose, i set `n_sample` to be `10`. Feel free to adjust it or simply run the entire dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f9a81b7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-09-03T00:44:16.340126Z",
+     "start_time": "2021-09-03T00:44:14.294332Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "adopted-panel",
+    "outputId": "7a15482b-33ff-4b0d-9d81-ffa5f3ef2a6a"
+   },
+   "outputs": [],
+   "source": [
+    "data = load_m3monthly()\n",
+    "unique_keys = data['key'].unique().tolist()\n",
+    "if n_sample > 0:\n",
+    "    sample_keys = random.sample(unique_keys, 10)\n",
+    "    # just get the first 5 series for demo\n",
+    "    data = data[data['key'].isin(sample_keys)].reset_index(drop=True)\n",
+    "else:\n",
+    "    sample_keys = unique_keys\n",
+    "print(sample_keys)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21b41737",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-09-03T00:44:16.348761Z",
+     "start_time": "2021-09-03T00:44:16.342154Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "processed-barbados",
+    "outputId": "f76fbc7a-85b3-4f4a-fbcc-f8897929e4fc"
+   },
+   "outputs": [],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45dd86cb",
+   "metadata": {},
+   "source": [
+    "We need to provide some meta data such as date column, response column etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8831518f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-09-03T00:44:16.925387Z",
+     "start_time": "2021-09-03T00:44:16.920908Z"
+    },
+    "id": "fabulous-humor"
+   },
+   "outputs": [],
+   "source": [
+    "key_col='key'\n",
+    "response_col='value'\n",
+    "date_col='date'\n",
+    "seasonality=12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "351f226e",
+   "metadata": {},
+   "source": [
+    "We also provide some setting mimic M3 (see https://forecasters.org/resources/time-series-data/m3-competition/) criteria."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7dab9ec7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-09-03T00:44:17.473534Z",
+     "start_time": "2021-09-03T00:44:17.470679Z"
+    },
+    "id": "right-naples"
+   },
+   "outputs": [],
+   "source": [
+    "backtest_args = {\n",
+    "    'min_train_len': 1, # not useful; a placeholder\n",
+    "    'incremental_len': 18,  # not useful; a placeholder\n",
+    "    'forecast_len': 18,\n",
+    "    'n_splits': 1,\n",
+    "    'window_type': \"expanding\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40f2411e",
+   "metadata": {},
+   "source": [
+    "We are using `DLT` here.  To use a multiplicative form, we need a natural log transformation of response.  Hence, we need to a wrapper for `DLT`.  We also need to build wrapper for signature prupose for `prophet` and `sarima`.\n",
+    "Note that prophet comes with its own multiplicative form."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac574fc4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-09-03T00:44:45.403466Z",
+     "start_time": "2021-09-03T00:44:45.398075Z"
+    },
+    "id": "incorporated-buddy"
+   },
+   "outputs": [],
+   "source": [
+    "class DLTMAPWrapper(object):\n",
+    "    def __init__(self, response_col, date_col, **kwargs):\n",
+    "        kw_params = locals()['kwargs']\n",
+    "        for key, value in kw_params.items():\n",
+    "            setattr(self, key, value)\n",
+    "        self.response_col = response_col\n",
+    "        self.date_col = date_col\n",
+    "        self.model = DLT(\n",
+    "            response_col=response_col,\n",
+    "            date_col=date_col,\n",
+    "            estimator='stan-map',\n",
+    "            **kwargs)\n",
+    "\n",
+    "    def fit(self, df):\n",
+    "        df = df.copy()\n",
+    "        df[[self.response_col]] = df[[self.response_col]].apply(np.log1p)\n",
+    "        self.model.fit(df)\n",
+    "\n",
+    "    def predict(self, df):\n",
+    "        df = df.copy()\n",
+    "        pred_df = self.model.predict(df)\n",
+    "        pred_df['prediction'] = np.clip(np.expm1(pred_df['prediction']).values, 0, None)\n",
+    "        return pred_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d0ac828",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-09-03T00:44:20.932012Z",
+     "start_time": "2021-09-03T00:44:20.920257Z"
+    },
+    "id": "y3fP5z6ofG4C"
+   },
+   "outputs": [],
+   "source": [
+    "class SARIMAXWrapper(object):\n",
+    "    def __init__(self, response_col, date_col, **kwargs):\n",
+    "        kw_params = locals()['kwargs']\n",
+    "        for key, value in kw_params.items():\n",
+    "            setattr(self, key, value)\n",
+    "        self.response_col = response_col\n",
+    "        self.date_col = date_col\n",
+    "        self.model = None\n",
+    "        self.df = None\n",
+    "\n",
+    "    def fit(self, df):\n",
+    "\n",
+    "        df_copy = df.copy()\n",
+    "        infer_freq = pd.infer_freq(df_copy[self.date_col])\n",
+    "        df_copy = df_copy.set_index(self.date_col)\n",
+    "        df_copy = df_copy.asfreq(infer_freq)\n",
+    "        endog = df_copy[self.response_col]\n",
+    "        sig = inspect.signature(SARIMAX)\n",
+    "        all_params = dict()\n",
+    "        for key in sig.parameters.keys():\n",
+    "            if hasattr(self, key):\n",
+    "                all_params[key] = getattr(self, key)\n",
+    "        self.df = df_copy\n",
+    "        self.model = SARIMAX(endog=endog, **all_params).fit(disp=False)\n",
+    "\n",
+    "    def predict(self, df, **kwargs):\n",
+    "        df_copy = df.copy()\n",
+    "        infer_freq = pd.infer_freq(df_copy[self.date_col])\n",
+    "        df_copy = df_copy.set_index(self.date_col)\n",
+    "        df_copy = df_copy.asfreq(infer_freq)\n",
+    "\n",
+    "        pred_array = np.array(self.model.predict(start=df_copy.index[0],\n",
+    "                                                 end=df_copy.index[-1],\n",
+    "                                                 **kwargs))\n",
+    "\n",
+    "        out = pd.DataFrame({\n",
+    "            self.date_col: df[self.date_col],\n",
+    "            'prediction': pred_array\n",
+    "        })\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b03a8c3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-09-03T00:44:21.234126Z",
+     "start_time": "2021-09-03T00:44:21.226394Z"
+    },
+    "id": "Ehq9Ve2L6k0o"
+   },
+   "outputs": [],
+   "source": [
+    "class ProphetWrapper(object):\n",
+    "    def __init__(self, response_col, date_col, **kwargs):\n",
+    "        kw_params = locals()['kwargs']\n",
+    "        for key, value in kw_params.items():\n",
+    "            setattr(self, key, value)\n",
+    "        self.response_col = response_col\n",
+    "        self.date_col = date_col\n",
+    "        self.model = Prophet(**kwargs)\n",
+    "\n",
+    "    def fit(self, df):\n",
+    "        sig = inspect.signature(Prophet)\n",
+    "        all_params = dict()\n",
+    "        for key in sig.parameters.keys():\n",
+    "            if hasattr(self, key):\n",
+    "                all_params[key] = getattr(self, key)\n",
+    "        object_type = type(self.model)\n",
+    "        self.model = object_type(**all_params)\n",
+    "\n",
+    "        train_df = df.copy()\n",
+    "        train_df = train_df.rename(columns={self.date_col: \"ds\", self.response_col: \"y\"})\n",
+    "        self.model.fit(train_df)\n",
+    "\n",
+    "    def predict(self, df):\n",
+    "        df = df.copy()\n",
+    "        df = df.rename(columns={self.date_col: \"ds\"})\n",
+    "        pred_df = self.model.predict(df)\n",
+    "        pred_df = pred_df.rename(columns={'yhat': 'prediction', 'ds': self.date_col})\n",
+    "        pred_df = pred_df[[self.date_col, 'prediction']]\n",
+    "        return pred_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6bc1dfc",
+   "metadata": {},
+   "source": [
+    "Declare model objects and run backtest. Score shows in the end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db00bc70",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-09-03T00:44:48.700884Z",
+     "start_time": "2021-09-03T00:44:48.473609Z"
+    },
+    "id": "bound-occurrence"
+   },
+   "outputs": [],
+   "source": [
+    "dlt = DLTMAPWrapper(\n",
+    "    response_col=response_col,\n",
+    "    date_col=date_col,\n",
+    "    seasonality=seasonality,\n",
+    "    seed=seed,\n",
+    ")\n",
+    "\n",
+    "sarima = SARIMAXWrapper(\n",
+    "    response_col=response_col,\n",
+    "    date_col=date_col,\n",
+    "    seasonality=seasonality,\n",
+    "    seed=seed,\n",
+    ")\n",
+    "\n",
+    "prophet = ProphetWrapper(\n",
+    "    response_col=response_col,\n",
+    "    date_col=date_col,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13f984c2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-09-03T00:45:20.549713Z",
+     "start_time": "2021-09-03T00:44:50.214556Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "extra-robertson",
+    "outputId": "1b8a808c-aa64-46f1-ffb5-85709f5c9f5a"
+   },
+   "outputs": [],
+   "source": [
+    "all_scores = []\n",
+    "\n",
+    "for key in tqdm.tqdm(sample_keys):\n",
+    "    # dlt\n",
+    "    df = data[data[key_col] == key]\n",
+    "    bt = BackTester(\n",
+    "        model=dlt,\n",
+    "        df=df,\n",
+    "        **backtest_args,\n",
+    "    )\n",
+    "    bt.fit_predict()\n",
+    "    scores_df = bt.score(metrics=[smape])\n",
+    "    scores_df[key_col] = key\n",
+    "    scores_df['model'] = 'dlt'\n",
+    "    all_scores.append(scores_df)\n",
+    "    # sarima\n",
+    "    df = data[data[key_col] == key]\n",
+    "    bt = BackTester(\n",
+    "        model=sarima,\n",
+    "        df=df,\n",
+    "        **backtest_args,\n",
+    "    )\n",
+    "    bt.fit_predict()\n",
+    "    scores_df = bt.score(metrics=[smape])\n",
+    "    scores_df[key_col] = key\n",
+    "    scores_df['model'] = 'sarima'\n",
+    "    all_scores.append(scores_df)\n",
+    "    # prophet\n",
+    "    df = data[data[key_col] == key]\n",
+    "    bt = BackTester(\n",
+    "        model=prophet,\n",
+    "        df=df,\n",
+    "        **backtest_args,\n",
+    "    )\n",
+    "    bt.fit_predict()\n",
+    "    scores_df = bt.score(metrics=[smape])\n",
+    "    scores_df[key_col] = key\n",
+    "    scores_df['model'] = 'prophet'\n",
+    "    all_scores.append(scores_df)\n",
+    "\n",
+    "\n",
+    "all_scores = pd.concat(all_scores, axis=0, ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ac520a5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-09-03T00:45:52.749454Z",
+     "start_time": "2021-09-03T00:45:52.735908Z"
+    },
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 142
+    },
+    "id": "bigger-insulation",
+    "outputId": "56676af4-b62c-43b4-ca3d-508fae4a3550"
+   },
+   "outputs": [],
+   "source": [
+    "all_scores.groupby('model')['metric_values'].apply(np.mean).reset_index()"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "orbit_m3_backtest.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/backtest.ipynb b/examples/backtest.ipynb
deleted file mode 100644
index 93998478..00000000
--- a/examples/backtest.ipynb
+++ /dev/null
@@ -1,1509 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Backtest a Single Model \n",
-    "\n",
-    "The way to gauge the performance of a time-series model is through re-training models with different historic periods and check their forecast within certain steps. This is similar to a time-based style cross-validation. More often, we called it `backtest` in time-series modeling.\n",
-    "\n",
-    "The purpose of this notebook is to illustrate how to do 'backtest' on a single model using `BackTester`\n",
-    "\n",
-    "`BackTester` will compose a `TimeSeriesSplitter` within it, but `TimeSeriesSplitter` is useful as a standalone, in case there are other tasks to perform that requires splitting but not backtesting. You can also retrieve the composed `TimeSeriesSplitter` object from `BackTester` to utilize the additional methods in `TimeSeriesSplitter`\n",
-    "\n",
-    "Currently, there are two schemes supported for the back-testing engine: expanding window and rolling window.\n",
-    "\n",
-    "* expanding window: for each back-testing model training, the train start date is fixed, while the train end date is extended forward.\n",
-    "* rolling window: for each back-testing model training, the training window length is fixed but the window is moving forward."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T22:57:54.834676Z",
-     "start_time": "2022-03-08T22:57:52.659798Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "\n",
-    "from orbit.models import DLT\n",
-    "from orbit.diagnostics.backtest import BackTester, TimeSeriesSplitter\n",
-    "from orbit.diagnostics.plot import plot_bt_predictions\n",
-    "from orbit.diagnostics.metrics import smape, wmape\n",
-    "from orbit.utils.dataset import load_iclaims\n",
-    "from orbit.utils.plot import get_orbit_style"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T22:57:54.865156Z",
-     "start_time": "2022-03-08T22:57:54.836369Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T22:57:55.190586Z",
-     "start_time": "2022-03-08T22:57:54.867453Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(443, 7)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "      <th>sp500</th>\n",
-       "      <th>vix</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.318452</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.417633</td>\n",
-       "      <td>0.122654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>13.624218</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.168794</td>\n",
-       "      <td>-0.425480</td>\n",
-       "      <td>0.110445</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>13.398741</td>\n",
-       "      <td>0.236143</td>\n",
-       "      <td>-0.292477</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.465229</td>\n",
-       "      <td>0.532339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>13.137549</td>\n",
-       "      <td>0.203353</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.106918</td>\n",
-       "      <td>-0.481751</td>\n",
-       "      <td>0.428645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>13.196760</td>\n",
-       "      <td>0.134360</td>\n",
-       "      <td>-0.242466</td>\n",
-       "      <td>0.074483</td>\n",
-       "      <td>-0.488929</td>\n",
-       "      <td>0.487404</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week     claims  trend.unemploy  trend.filling  trend.job     sp500  \\\n",
-       "0 2010-01-03  13.386595        0.219882      -0.318452   0.117500 -0.417633   \n",
-       "1 2010-01-10  13.624218        0.219882      -0.194838   0.168794 -0.425480   \n",
-       "2 2010-01-17  13.398741        0.236143      -0.292477   0.117500 -0.465229   \n",
-       "3 2010-01-24  13.137549        0.203353      -0.194838   0.106918 -0.481751   \n",
-       "4 2010-01-31  13.196760        0.134360      -0.242466   0.074483 -0.488929   \n",
-       "\n",
-       "        vix  \n",
-       "0  0.122654  \n",
-       "1  0.110445  \n",
-       "2  0.532339  \n",
-       "3  0.428645  \n",
-       "4  0.487404  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_data = load_iclaims()\n",
-    "data = raw_data.copy()\n",
-    "\n",
-    "print(data.shape)\n",
-    "\n",
-    "data.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a BackTester"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T22:58:39.998178Z",
-     "start_time": "2022-03-08T22:58:39.965820Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# instantiate a model\n",
-    "\n",
-    "dlt = DLT(date_col='week',\n",
-    "          response_col='claims',\n",
-    "          regressor_col=['trend.unemploy', 'trend.filling', 'trend.job'],\n",
-    "          seasonality=52,\n",
-    "          prediction_percentiles=[10, 90],\n",
-    "          n_bootstrap_draws=100,\n",
-    "          estimator='stan-map')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T22:58:41.789029Z",
-     "start_time": "2022-03-08T22:58:41.757817Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "bt = BackTester(model=dlt,\n",
-    "                df=data,\n",
-    "                min_train_len=100,\n",
-    "                incremental_len=100,\n",
-    "                forecast_len=20)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Backtest Fit and Predict\n",
-    "\n",
-    "The most expensive portion of backtesting is fitting the model iteratively. Thus, we separate the API calls for `fit_predict` and `score` to avoid redundant computation for multiple metrics or scoring methods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T22:58:44.378243Z",
-     "start_time": "2022-03-08T22:58:43.404655Z"
-    },
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "bt.fit_predict();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once `fit_predict()` is called, the fitted models and predictions can be easily retrieved from `BackTester`. Here the data is grouped by the date, split_key, and whether or not that observation is part of the training or test data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T22:58:47.263863Z",
-     "start_time": "2022-03-08T22:58:47.226488Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>actual</th>\n",
-       "      <th>prediction_10</th>\n",
-       "      <th>prediction</th>\n",
-       "      <th>prediction_90</th>\n",
-       "      <th>training_data</th>\n",
-       "      <th>split_key</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>13.362432</td>\n",
-       "      <td>13.386595</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>13.624218</td>\n",
-       "      <td>13.620192</td>\n",
-       "      <td>13.649077</td>\n",
-       "      <td>13.664710</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>13.398741</td>\n",
-       "      <td>13.348670</td>\n",
-       "      <td>13.373116</td>\n",
-       "      <td>13.395082</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>13.137549</td>\n",
-       "      <td>13.132957</td>\n",
-       "      <td>13.151829</td>\n",
-       "      <td>13.173657</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>13.196760</td>\n",
-       "      <td>13.163632</td>\n",
-       "      <td>13.187769</td>\n",
-       "      <td>13.205264</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        date     actual  prediction_10  prediction  prediction_90  \\\n",
-       "0 2010-01-03  13.386595      13.362432   13.386595      13.405258   \n",
-       "1 2010-01-10  13.624218      13.620192   13.649077      13.664710   \n",
-       "2 2010-01-17  13.398741      13.348670   13.373116      13.395082   \n",
-       "3 2010-01-24  13.137549      13.132957   13.151829      13.173657   \n",
-       "4 2010-01-31  13.196760      13.163632   13.187769      13.205264   \n",
-       "\n",
-       "   training_data  split_key  \n",
-       "0           True          0  \n",
-       "1           True          0  \n",
-       "2           True          0  \n",
-       "3           True          0  \n",
-       "4           True          0  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predicted_df = bt.get_predicted_df()\n",
-    "predicted_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We also provide a plotting utility to visualize the predictions against the actuals for each split."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T22:58:50.502051Z",
-     "start_time": "2022-03-08T22:58:49.794589Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_bt_predictions(predicted_df, metrics=smape, ncol=2, include_vline=True);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Users might find this useful for any custom computations that may need to be performed on the set of predicted data. Note that the columns are renamed to generic and consistent names."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sometimes, it might be useful to match the data back to the original dataset for ad-hoc diagnostics. This can easily be done by merging back to the orignal dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T22:58:55.346687Z",
-     "start_time": "2022-03-08T22:58:55.293938Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>actual</th>\n",
-       "      <th>prediction_10</th>\n",
-       "      <th>prediction</th>\n",
-       "      <th>prediction_90</th>\n",
-       "      <th>training_data</th>\n",
-       "      <th>split_key</th>\n",
-       "      <th>week</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "      <th>sp500</th>\n",
-       "      <th>vix</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>13.362432</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>13.405258</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.318452</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.417633</td>\n",
-       "      <td>0.122654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>13.347227</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>13.414730</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.318452</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.417633</td>\n",
-       "      <td>0.122654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>13.333119</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>13.453696</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.318452</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.417633</td>\n",
-       "      <td>0.122654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>13.324952</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>13.451474</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.318452</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.417633</td>\n",
-       "      <td>0.122654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>13.624218</td>\n",
-       "      <td>13.620192</td>\n",
-       "      <td>13.649077</td>\n",
-       "      <td>13.664710</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>13.624218</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.168794</td>\n",
-       "      <td>-0.425480</td>\n",
-       "      <td>0.110445</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1075</th>\n",
-       "      <td>2017-12-17</td>\n",
-       "      <td>12.568616</td>\n",
-       "      <td>12.502380</td>\n",
-       "      <td>12.578824</td>\n",
-       "      <td>12.635715</td>\n",
-       "      <td>False</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2017-12-17</td>\n",
-       "      <td>12.568616</td>\n",
-       "      <td>0.298663</td>\n",
-       "      <td>0.248654</td>\n",
-       "      <td>-0.216869</td>\n",
-       "      <td>0.434042</td>\n",
-       "      <td>-0.482380</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1076</th>\n",
-       "      <td>2017-12-24</td>\n",
-       "      <td>12.691451</td>\n",
-       "      <td>12.613007</td>\n",
-       "      <td>12.682855</td>\n",
-       "      <td>12.756929</td>\n",
-       "      <td>False</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2017-12-24</td>\n",
-       "      <td>12.691451</td>\n",
-       "      <td>0.328516</td>\n",
-       "      <td>0.233616</td>\n",
-       "      <td>-0.358839</td>\n",
-       "      <td>0.430410</td>\n",
-       "      <td>-0.373389</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1077</th>\n",
-       "      <td>2017-12-31</td>\n",
-       "      <td>12.769532</td>\n",
-       "      <td>12.660860</td>\n",
-       "      <td>12.732984</td>\n",
-       "      <td>12.794730</td>\n",
-       "      <td>False</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2017-12-31</td>\n",
-       "      <td>12.769532</td>\n",
-       "      <td>0.503457</td>\n",
-       "      <td>0.069313</td>\n",
-       "      <td>-0.092571</td>\n",
-       "      <td>0.456087</td>\n",
-       "      <td>-0.553539</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1078</th>\n",
-       "      <td>2018-01-07</td>\n",
-       "      <td>12.908227</td>\n",
-       "      <td>12.836532</td>\n",
-       "      <td>12.911132</td>\n",
-       "      <td>12.975398</td>\n",
-       "      <td>False</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2018-01-07</td>\n",
-       "      <td>12.908227</td>\n",
-       "      <td>0.527849</td>\n",
-       "      <td>0.051295</td>\n",
-       "      <td>0.029532</td>\n",
-       "      <td>0.471673</td>\n",
-       "      <td>-0.456456</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1079</th>\n",
-       "      <td>2018-01-14</td>\n",
-       "      <td>12.777193</td>\n",
-       "      <td>12.638055</td>\n",
-       "      <td>12.695397</td>\n",
-       "      <td>12.766672</td>\n",
-       "      <td>False</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2018-01-14</td>\n",
-       "      <td>12.777193</td>\n",
-       "      <td>0.465717</td>\n",
-       "      <td>0.032946</td>\n",
-       "      <td>0.006275</td>\n",
-       "      <td>0.480271</td>\n",
-       "      <td>-0.352770</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1080 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           date     actual  prediction_10  prediction  prediction_90  \\\n",
-       "0    2010-01-03  13.386595      13.362432   13.386595      13.405258   \n",
-       "1    2010-01-03  13.386595      13.347227   13.386595      13.414730   \n",
-       "2    2010-01-03  13.386595      13.333119   13.386595      13.453696   \n",
-       "3    2010-01-03  13.386595      13.324952   13.386595      13.451474   \n",
-       "4    2010-01-10  13.624218      13.620192   13.649077      13.664710   \n",
-       "...         ...        ...            ...         ...            ...   \n",
-       "1075 2017-12-17  12.568616      12.502380   12.578824      12.635715   \n",
-       "1076 2017-12-24  12.691451      12.613007   12.682855      12.756929   \n",
-       "1077 2017-12-31  12.769532      12.660860   12.732984      12.794730   \n",
-       "1078 2018-01-07  12.908227      12.836532   12.911132      12.975398   \n",
-       "1079 2018-01-14  12.777193      12.638055   12.695397      12.766672   \n",
-       "\n",
-       "      training_data  split_key       week     claims  trend.unemploy  \\\n",
-       "0              True          0 2010-01-03  13.386595        0.219882   \n",
-       "1              True          1 2010-01-03  13.386595        0.219882   \n",
-       "2              True          2 2010-01-03  13.386595        0.219882   \n",
-       "3              True          3 2010-01-03  13.386595        0.219882   \n",
-       "4              True          0 2010-01-10  13.624218        0.219882   \n",
-       "...             ...        ...        ...        ...             ...   \n",
-       "1075          False          3 2017-12-17  12.568616        0.298663   \n",
-       "1076          False          3 2017-12-24  12.691451        0.328516   \n",
-       "1077          False          3 2017-12-31  12.769532        0.503457   \n",
-       "1078          False          3 2018-01-07  12.908227        0.527849   \n",
-       "1079          False          3 2018-01-14  12.777193        0.465717   \n",
-       "\n",
-       "      trend.filling  trend.job     sp500       vix  \n",
-       "0         -0.318452   0.117500 -0.417633  0.122654  \n",
-       "1         -0.318452   0.117500 -0.417633  0.122654  \n",
-       "2         -0.318452   0.117500 -0.417633  0.122654  \n",
-       "3         -0.318452   0.117500 -0.417633  0.122654  \n",
-       "4         -0.194838   0.168794 -0.425480  0.110445  \n",
-       "...             ...        ...       ...       ...  \n",
-       "1075       0.248654  -0.216869  0.434042 -0.482380  \n",
-       "1076       0.233616  -0.358839  0.430410 -0.373389  \n",
-       "1077       0.069313  -0.092571  0.456087 -0.553539  \n",
-       "1078       0.051295   0.029532  0.471673 -0.456456  \n",
-       "1079       0.032946   0.006275  0.480271 -0.352770  \n",
-       "\n",
-       "[1080 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predicted_df.merge(data, left_on='date', right_on='week')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Backtest Scoring\n",
-    "\n",
-    "The main purpose of `BackTester` are the evaluation metrics. Some of the most widely used metrics are implemented and built into the `BackTester` API.\n",
-    "\n",
-    "The default metric list is **smape, wmape, mape, mse, mae, rmsse**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T22:59:03.858581Z",
-     "start_time": "2022-03-08T22:59:03.821817Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "\n",
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-       "      <th></th>\n",
-       "      <th>metric_name</th>\n",
-       "      <th>metric_values</th>\n",
-       "      <th>is_training_metric</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>smape</td>\n",
-       "      <td>0.006426</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>wmape</td>\n",
-       "      <td>0.006417</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>mape</td>\n",
-       "      <td>0.006410</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>mse</td>\n",
-       "      <td>0.012286</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>mae</td>\n",
-       "      <td>0.081519</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>rmsse</td>\n",
-       "      <td>0.791175</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  metric_name  metric_values  is_training_metric\n",
-       "0       smape       0.006426               False\n",
-       "1       wmape       0.006417               False\n",
-       "2        mape       0.006410               False\n",
-       "3         mse       0.012286               False\n",
-       "4         mae       0.081519               False\n",
-       "5       rmsse       0.791175               False"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bt.score()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is possible to filter for only specific metrics of interest, or even implement your own callable and pass into the `score()` method. For example, see this function that uses last observed value as a predictor and computes the `mse`. Or `naive_error` which computes the error as the delta between predicted values and the training period mean. \n",
-    "\n",
-    "Note these are not really useful error metrics, just showing some examples of callables you can use ;)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T22:59:50.760847Z",
-     "start_time": "2022-03-08T22:59:50.730257Z"
-    },
-    "code_folding": []
-   },
-   "outputs": [],
-   "source": [
-    "def mse_naive(test_actual):\n",
-    "    actual = test_actual[1:]\n",
-    "    predicted = test_actual[:-1]\n",
-    "    return np.mean(np.square(actual - predicted))\n",
-    "\n",
-    "\n",
-    "def naive_error(train_actual, test_prediction):\n",
-    "    train_mean = np.mean(train_actual)\n",
-    "    return np.mean(np.abs(test_prediction - train_mean))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T22:59:55.791420Z",
-     "start_time": "2022-03-08T22:59:55.755279Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>metric_name</th>\n",
-       "      <th>metric_values</th>\n",
-       "      <th>is_training_metric</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>mse_naive</td>\n",
-       "      <td>0.019628</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>naive_error</td>\n",
-       "      <td>0.231076</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   metric_name  metric_values  is_training_metric\n",
-       "0    mse_naive       0.019628               False\n",
-       "1  naive_error       0.231076               False"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bt.score(metrics=[mse_naive, naive_error])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It doesn't take additional time to refit and predict the model, since the results are stored when `fit_predict()` is called. Check docstrings for function criteria that is required for it to be supported with this API."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In some cases, we may want to evaluate our metrics on both train and test data. To do this you can call score again with the following indicator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T23:00:08.102522Z",
-     "start_time": "2022-03-08T23:00:08.066436Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>metric_name</th>\n",
-       "      <th>metric_values</th>\n",
-       "      <th>is_training_metric</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>smape</td>\n",
-       "      <td>0.006426</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>wmape</td>\n",
-       "      <td>0.006417</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>mape</td>\n",
-       "      <td>0.006410</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>mse</td>\n",
-       "      <td>0.012286</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>mae</td>\n",
-       "      <td>0.081519</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>rmsse</td>\n",
-       "      <td>0.791175</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>smape</td>\n",
-       "      <td>0.002689</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>wmape</td>\n",
-       "      <td>0.002684</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>mape</td>\n",
-       "      <td>0.002687</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>mse</td>\n",
-       "      <td>0.002980</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>mae</td>\n",
-       "      <td>0.034299</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   metric_name  metric_values  is_training_metric\n",
-       "0        smape       0.006426               False\n",
-       "1        wmape       0.006417               False\n",
-       "2         mape       0.006410               False\n",
-       "3          mse       0.012286               False\n",
-       "4          mae       0.081519               False\n",
-       "5        rmsse       0.791175               False\n",
-       "6        smape       0.002689                True\n",
-       "7        wmape       0.002684                True\n",
-       "8         mape       0.002687                True\n",
-       "9          mse       0.002980                True\n",
-       "10         mae       0.034299                True"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bt.score(include_training_metrics=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Backtest Get Models\n",
-    "\n",
-    "In cases where `BackTester` doesn't cut it or for more custom use-cases, there's an interface to export the `TimeSeriesSplitter` and predicted data, as shown earlier. It's also possible to get each of the fitted models for deeper diving."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T23:00:09.886449Z",
-     "start_time": "2022-03-08T23:00:09.855130Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "fitted_models = bt.get_fitted_models()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T23:00:10.354581Z",
-     "start_time": "2022-03-08T23:00:10.321193Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>regressor</th>\n",
-       "      <th>regressor_sign</th>\n",
-       "      <th>coefficient</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>trend.unemploy</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>-0.048348</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>trend.filling</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>-0.119761</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>trend.job</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>0.027741</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        regressor regressor_sign  coefficient\n",
-       "0  trend.unemploy        Regular    -0.048348\n",
-       "1   trend.filling        Regular    -0.119761\n",
-       "2       trend.job        Regular     0.027741"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_1 = fitted_models[0]\n",
-    "\n",
-    "model_1.get_regression_coefs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get TimeSeriesSplitter\n",
-    "\n",
-    "BackTester composes a TimeSeriesSplitter within it, but TimeSeriesSplitter can also be created on its own as a standalone object. See section below on TimeSeriesSplitter for more details on how to use the splitter.\n",
-    "\n",
-    "All of the additional TimeSeriesSplitter args can also be passed into BackTester on instantiation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T23:00:11.489773Z",
-     "start_time": "2022-03-08T23:00:11.247449Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 2000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ts_splitter = bt.get_splitter()\n",
-    "ts_splitter.plot()\n",
-    "plt.grid();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Appendix"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create a TimeSeriesSplitter"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "####  Expanding window"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T23:00:12.783116Z",
-     "start_time": "2022-03-08T23:00:12.755048Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "min_train_len = 380\n",
-    "forecast_len = 20\n",
-    "incremental_len = 20"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T23:00:13.086847Z",
-     "start_time": "2022-03-08T23:00:13.057918Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ex_splitter = TimeSeriesSplitter(df=data,\n",
-    "                                 min_train_len=min_train_len,\n",
-    "                                 incremental_len=incremental_len,\n",
-    "                                 forecast_len=forecast_len, \n",
-    "                                 window_type='expanding',\n",
-    "                                 date_col='week')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T23:00:13.436766Z",
-     "start_time": "2022-03-08T23:00:13.406888Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "------------ Fold: (1 / 3)------------\n",
-      "Train start date: 2010-01-03 00:00:00 Train end date: 2017-04-09 00:00:00\n",
-      "Test start date: 2017-04-16 00:00:00 Test end date: 2017-08-27 00:00:00\n",
-      "\n",
-      "------------ Fold: (2 / 3)------------\n",
-      "Train start date: 2010-01-03 00:00:00 Train end date: 2017-08-27 00:00:00\n",
-      "Test start date: 2017-09-03 00:00:00 Test end date: 2018-01-14 00:00:00\n",
-      "\n",
-      "------------ Fold: (3 / 3)------------\n",
-      "Train start date: 2010-01-03 00:00:00 Train end date: 2018-01-14 00:00:00\n",
-      "Test start date: 2018-01-21 00:00:00 Test end date: 2018-06-03 00:00:00\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(ex_splitter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T23:00:13.963681Z",
-     "start_time": "2022-03-08T23:00:13.794583Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 2000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ex_splitter.plot()\n",
-    "\n",
-    "plt.grid();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Rolling window"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T23:00:14.621681Z",
-     "start_time": "2022-03-08T23:00:14.591493Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "roll_splitter = TimeSeriesSplitter(df=data,\n",
-    "                                   min_train_len=min_train_len,\n",
-    "                                   incremental_len=incremental_len,\n",
-    "                                   forecast_len=forecast_len, \n",
-    "                                   window_type='rolling',\n",
-    "                                   date_col='week')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T23:00:15.043213Z",
-     "start_time": "2022-03-08T23:00:14.866960Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 2000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "roll_splitter.plot()\n",
-    "plt.grid();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Specifying number of splits"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "User can also define number of splits using `n_splits` instead of specifying minimum training length.  That way, minimum training length will be automatically calculated."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T23:00:15.796253Z",
-     "start_time": "2022-03-08T23:00:15.766982Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ex_splitter2 = TimeSeriesSplitter(df=data,\n",
-    "                                  min_train_len=min_train_len,\n",
-    "                                  incremental_len=incremental_len,\n",
-    "                                  forecast_len=forecast_len, \n",
-    "                                  n_splits=5,\n",
-    "                                  window_type='expanding',\n",
-    "                                  date_col='week')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-08T23:00:16.271573Z",
-     "start_time": "2022-03-08T23:00:16.049705Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 2000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ex_splitter2.plot()\n",
-    "\n",
-    "plt.grid();"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
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-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
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-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "306.391px"
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-   "toc_section_display": true,
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-  "toc-autonumbering": false,
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- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/dlt-animation.ipynb b/examples/dlt-animation.ipynb
index 8260138c..88e126df 100644
--- a/examples/dlt-animation.ipynb
+++ b/examples/dlt-animation.ipynb
@@ -139,7 +139,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.11.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/examples/dlt.ipynb b/examples/dlt.ipynb
deleted file mode 100644
index 7555d409..00000000
--- a/examples/dlt.ipynb
+++ /dev/null
@@ -1,548 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Damped Local Trend (DLT)\n",
-    "\n",
-    "In this section, we will cover:\n",
-    "\n",
-    "- DLT model structure\n",
-    "- DLT global trend configurations\n",
-    "- Adding regressors in DLT\n",
-    "- Other configurations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-24T22:41:48.787550Z",
-     "start_time": "2022-01-24T22:41:46.348154Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import orbit\n",
-    "from orbit.models import DLT\n",
-    "from orbit.diagnostics.plot import plot_predicted_data, plot_predicted_components\n",
-    "from orbit.utils.dataset import load_iclaims"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-24T22:41:48.794620Z",
-     "start_time": "2022-01-24T22:41:48.790072Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.1.4dev\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(orbit.__version__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Model Structure\n",
-    "\n",
-    "**DLT** is one of the main exponential smoothing models we support in `orbit`.  Performance is benchmarked with M3 monthly, M4 weekly dataset and some Uber internal dataset [(Ng and Wang et al., 2020)](https://arxiv.org/abs/2004.08492).  The model is a fusion between the classical ETS [(Hyndman et. al., 2008)](http://www.exponentialsmoothing.net/home)) with some refinement leveraging ideas from Rlgt [(Smyl et al., 2019)](https://cran.r-project.org/web/packages/Rlgt/index.html). The model has a structural forecast equations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$\n",
-    "\\begin{align*}\n",
-    "y_t &=\\mu_t + s_t + r_t +  \\epsilon_t \\\\\n",
-    "\\mu_t &=g_t + l_{t-1} +  \\theta{b_{t-1}} \\\\\n",
-    "\\epsilon_t  &~\\sim \\mathtt{Student}(\\nu, 0, \\sigma)\\\\\n",
-    "\\sigma &~\\sim  \\mathtt{HalfCauchy}(0, \\gamma_0)\n",
-    "\\end{align*}\n",
-    "$$\n",
-    "\n",
-    "with the update process\n",
-    "\n",
-    "$$\n",
-    "\\begin{align*}\n",
-    "g_t &= D(t)\\\\\n",
-    "l_t &= \\rho_l(y_t - g_{t} - s_t - r_t) + (1-\\rho_l)(l_{t-1} + \\theta b_{t-1})\\\\\n",
-    "b_t &=  \\rho_b(l_t - l_{t-1}) + (1-\\rho_b)\\theta b_{t-1}\\\\\n",
-    "s_{t+m} &=  \\rho_s(y_t - l_t - r_t) + (1-\\rho_s)s_t\\\\\n",
-    "r_t &=  \\Sigma_{j}\\beta_j x_{jt}\n",
-    "\\end{align*}\n",
-    "$$\n",
-    "\n",
-    "One important point is that using $y_t$ as a log-transformed response usually yield better result, especially we can interpret such log-transformed model as a *multiplicative form* of the original model.  Besides, there are two new additional components compared to the classical damped ETS model:\n",
-    "\n",
-    "1. $D(t)$ as the deterministic trend process\n",
-    "2. $r$ as the regression component with $x$ as the regressors"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-24T22:41:48.968188Z",
-     "start_time": "2022-01-24T22:41:48.799422Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# load log-transformed data\n",
-    "df = load_iclaims()\n",
-    "response_col = 'claims'\n",
-    "date_col = 'week'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-info\">\n",
-    "\n",
-    "<font size=\"+2\">Note</font>\n",
-    "\n",
-    "Just like LGT model, we also provide MAP and MCMC (full Bayesian) methods for DLT model (by specifying `estimator='stan-map'` or `estimator='stan-mcmc'` when instantiating a model). \n",
-    "    \n",
-    "MCMC is usually more robust but may take longer time to train. In this notebook, we will use the MAP method for illustration purpose.\n",
-    "\n",
-    "        \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Global Trend Configurations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There are a few choices of $D(t)$ configured by `global_trend_option`:\n",
-    "\n",
-    "1. `linear` (default)\n",
-    "2. `loglinear`\n",
-    "3. `flat`\n",
-    "4. `logistic`    \n",
-    "\n",
-    "Mathematically, they are expressed as such,\n",
-    "\n",
-    "**1. Linear**: \n",
-    "\n",
-    "$D(t) = \\delta_{\\text{intercept}} + \\delta_{\\text{slope}} \\cdot t$ \n",
-    "\n",
-    "**2. Log-linear**:\n",
-    "\n",
-    "$D(t) = \\delta_{\\text{intercept}} + ln(\\delta_{\\text{slope}} \\cdot t)$ \n",
-    "\n",
-    "**3. Logistic**:\n",
-    "\n",
-    "$D(t) = L + \\frac{U - L} {1 + e^{- \\delta_{\\text{slope}} \\cdot t}}$ \n",
-    "\n",
-    "**4. Flat**:\n",
-    "\n",
-    "$D(t) = \\delta_{\\text{intercept}}$ \n",
-    "\n",
-    "where $\\delta_{\\text{intercept}}$ and $\\delta_{\\text{slope}}$ are fitted parameters and $t$ is rescaled time-step between $0$ and $T$ (=number of time steps)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To show the difference among these options, their predictions are projected in the charts below. Note that the default is set to `linear` which is also used in the benchmarking process mentioned previously. During prediction, a convenient function `make_future_df()` is called to generate future data frame (ONLY applied when you don't have any regressors!)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-24T22:41:49.550367Z",
-     "start_time": "2022-01-24T22:41:48.971655Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "18:28:44 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "18:28:44 - cmdstanpy - INFO - Chain [1] done processing\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 611 ms, sys: 1.48 s, total: 2.1 s\n",
-      "Wall time: 493 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# linear global trend\n",
-    "dlt = DLT(\n",
-    "    response_col=response_col,\n",
-    "    date_col=date_col,\n",
-    "    estimator='stan-map',\n",
-    "    seasonality=52,\n",
-    "    seed=8888,\n",
-    "    global_trend_option='linear',\n",
-    "    # for prediction uncertainty\n",
-    "    n_bootstrap_draws=1000,\n",
-    ")\n",
-    "\n",
-    "dlt.fit(df)\n",
-    "test_df = dlt.make_future_df(periods=52 * 10)\n",
-    "predicted_df = dlt.predict(test_df)\n",
-    "# predicted_df.to_csv('./data/dlt-pystan-output-test.csv', index=False)\n",
-    "_ = plot_predicted_data(df, predicted_df, date_col, response_col,  title='DLT Linear Global Trend')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-24T22:41:50.033801Z",
-     "start_time": "2022-01-24T22:41:49.551778Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "18:28:45 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "18:28:45 - cmdstanpy - INFO - Chain [1] done processing\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 679 ms, sys: 1.27 s, total: 1.95 s\n",
-      "Wall time: 530 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# log-linear global trend\n",
-    "dlt = DLT(\n",
-    "    response_col=response_col,\n",
-    "    date_col=date_col,\n",
-    "    seasonality=52,\n",
-    "    estimator='stan-map',\n",
-    "    seed=8888,\n",
-    "    global_trend_option='loglinear',\n",
-    "    # for prediction uncertainty\n",
-    "    n_bootstrap_draws=1000,\n",
-    ")\n",
-    "\n",
-    "dlt.fit(df)\n",
-    "# re-use the test_df generated above\n",
-    "predicted_df = dlt.predict(test_df)\n",
-    "_ = plot_predicted_data(df, predicted_df, date_col, response_col,  title='DLT Log-Linear Global Trend')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In logistic trend, users need to specify the args `global_floor` and `global_cap`. These args are with default `0` and `1`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-24T22:41:50.912697Z",
-     "start_time": "2022-01-24T22:41:50.518942Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "18:28:45 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "18:28:46 - cmdstanpy - INFO - Chain [1] done processing\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 312 ms, sys: 288 ms, total: 600 ms\n",
-      "Wall time: 396 ms\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# logistic global trend\n",
-    "dlt = DLT(\n",
-    "    response_col=response_col,\n",
-    "    date_col=date_col,\n",
-    "    estimator='stan-map',\n",
-    "    seasonality=52,\n",
-    "    seed=8888,\n",
-    "    global_trend_option='logistic',\n",
-    "    global_cap=9999,\n",
-    "    global_floor=11.75,\n",
-    "    damped_factor=0.1,\n",
-    "    # for prediction uncertainty\n",
-    "    n_bootstrap_draws=1000,\n",
-    ")\n",
-    "\n",
-    "dlt.fit(df)\n",
-    "predicted_df = dlt.predict(test_df)\n",
-    "ax = plot_predicted_data(df, predicted_df, date_col, response_col, \n",
-    "                         title='DLT Logistic Global Trend', is_visible=False);\n",
-    "ax.axhline(y=11.75, linestyle='--', color='orange')\n",
-    "ax.figure"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note: Theoretically, the trend is bounded by the `global_floor` and `global_cap`. However, because of seasonality and regression, the predictions can still be slightly lower than the floor or higher than the cap."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-24T22:41:50.516722Z",
-     "start_time": "2022-01-24T22:41:50.035681Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "18:28:46 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "18:28:46 - cmdstanpy - INFO - Chain [1] done processing\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 673 ms, sys: 1.12 s, total: 1.79 s\n",
-      "Wall time: 454 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# flat trend\n",
-    "dlt = DLT(\n",
-    "    response_col=response_col,\n",
-    "    date_col=date_col,\n",
-    "    estimator='stan-map',\n",
-    "    seasonality=52,\n",
-    "    seed=8888,\n",
-    "    global_trend_option='flat',\n",
-    "    # for prediction uncertainty\n",
-    "    n_bootstrap_draws=1000,\n",
-    ")\n",
-    "\n",
-    "dlt.fit(df)\n",
-    "predicted_df = dlt.predict(test_df)\n",
-    "_ = plot_predicted_data(df, predicted_df, date_col, response_col,  title='DLT Flat Global Trend')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also add regressors into the model by specifying `regressor_col`. This serves the purpose of nowcasting or forecasting when exogenous regressors are known such as events and holidays. Without losing generality, the interface is set to be\n",
-    "\n",
-    "$$\\beta_j ~\\sim \\mathcal{N}(\\mu_j, \\sigma_j^2)$$\n",
-    "\n",
-    "where $\\mu_j = 0$  and $\\sigma_j = 1$ by default as a non-informative prior. These two parameters are set by the arguments `regressor_beta_prior` and `regressor_sigma_prior` as a list.  For example,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-24T22:41:51.580552Z",
-     "start_time": "2022-01-24T22:41:50.946298Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "18:28:47 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "18:28:47 - cmdstanpy - INFO - Chain [1] done processing\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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Lc+dZaA2oqbZRT51wsKvdQm9rY5unmmMGchcrGp0JCZ+HzzsREREREREREdFGsyECiYceegi33XYbvvCFL+CDH/zgvPOPHz+OP/zDP8R3v/tdpNNpdHR04Pbbb8fv/u7vore394Jv61Of+hSeeeYZlMtlXHnllfjN3/xN/If/8B9W6uHQGhFCIOzHeYcwa62RLwPlmkalbuYhZEsamaLGZFFjJKMxklU4PdZYbiEF0BoVJqSYCSumWkT5BTw2N80vlNLA0WEXI1kFS5rKl7oDlOuzIcaZcYVtrRLbWiUHohMREREREREREW0g6z6QOHz4MN7xjndA64WHCxw9ehQ33XQTJicnsX//ftxyyy04fPgw7rvvPvzv//2/8cMf/hBXXXXVsm7rf/2v/4X3vOc9sG0br33ta2FZFh544AH84i/+Il588UV88pOfXMmHRuuEEALRIBANLr25Xa6ZcGI4ozCc0RjJKAxnNV44o/CcVvMu7/cAyYhAW1SgLSaRigqkYgJtUYlYkG2glnJuFcVcjgJOjCoMpDU853yCCZigKBwwwZCqa0wWFMZyGhN5DSGBkE8g5AMiARMc+b3mdajWNUpVjVhQMOggIiIiIiIiIiJaBes6kHjwwQfxjne8A6Ojo4te5t3vfjcmJyfxyU9+Ev/1v/7XmU3eP/zDP8QnPvEJfOhDH8JPfvKT897WyMgIPvShDyEUCuGhhx7CddddBwA4dOgQXvOa1+CP/uiP8Na3vnXmdNp6Al6BvjaBvrbGdkKu0hjLaQxnNEYzCrmyqbjIlTXG8xrPnlbQ5wQWXhtIRQXaYgKpqJz6boILzqxYnqqjUXUWPi9b1hhIA7msg2issbKlVNUYm/OzxxLQWsOZeonCfoHLe6yGyppsSUMpjWhQwGJYQUREREREREREdFHWZSAxOjqKP/iDP8B9990HKSW2bduGM2fOzLvc0aNH8fjjj6Ovr68hjACA3/u938O9996Lxx9/HOl0Gslkcsnb/Mu//EuUy2V8/OMfbwgd9u/fj7vvvhvvf//78Wd/9mf427/925V7oLQpWNK0beqIA4A17/y6q5HOa4xmNUZzGmM5hdGsxlhW45mTChqNYYXPY8KK6YCiLSoQC5mh2yGfmbHhZTuoFVN3G6sxChWNp4472NVhQQpgIK2Qr5jLCJjKiu0piVRULnBtREREREREREREtJh1GUj88R//MT7/+c9j7969+NKXvoQvf/nL+Ju/+Zt5l9uzZw9GR0eRyWTmHVFeq9VQq9UAAJY1f5P4XN/61rcAAG9729vmnfe2t70NH/jAB/DNb37zIh4NbXUey8yWaI/PP6/umlZCJqBQU4GF+XmhsGJaPASkoqYNVFtUIBWTiAcFAj5TyWFJwHFN9YbjmjZHrgtoAJYEPBbg8whEAuAR/wtwNXBkyJ13uoapfHn+jIvOuMaeTtn0weZam4HgDKmIiIiIiIiIiGi9W5eBxM6dO/G5z30OH/zgB+HxePDlL3950cumUimkUqmG00qlEn79138dtVoNb3vb2xCLxZa8Pa01Dh48CAC44oor5p2fSCTQ0dGBoaEhDAwMoLu7+yIeFdF8Hmvx6oq6Y1o+jeU0ciUz36BYBSaLGqNZhbMTCkeHLu32hQCiAVN1Mf0VD5nv0aBAS9iEKQwt5hvKKEwWNXpbJFqjAgHv2j5HIxmFsbzGZEGj7mpEAwKtEYHWqDzvIPeao1FzTCAmYF7zC1FzNEpV0zarNcL1QUREREREREREy7MuA4kPf/jDF/V7/+///T98/vOfx09+8hNkMhm8+c1vxle+8pXz/t7k5CQqlQoikQhCodCCl+ns7MTQ0BBGRkYYSFwkblleGI8t0JkQ6EwsfL7WJqAYy2qM5hRyJY1yzQzfdhVgW4BtmWoJW5qfAcBVpnqiXNPIlTUyRTMjYSCt4C5QkOG1gd4Wid5WgY747LyLSICtoyp1jaPDLo4Om9kTybB5XqKB1Q0oToy4ODXW+GLlyub1PDGq4LEE4lPhUiIsZgKKXEnj9LjCeE5hbqOqsF9ge6t5besukC9r5MsaGrPv20odM6HY3DZXXlugLyXRleAwcCIiIiIiIiIiWtq6DCQu1ve+9z185zvfmfm5WCzi6NGjuOGGG5b8vWKxCAAIBoOLXiYQCAAACoXCopepVquoVqsNp/l8Pvh8vvPe963ghl0W+icUBtNqZoDwcgkA+ryX2lqEEAj7zWbyjvZLn2egtDnqPVPUyJbM12hW4/SYwukxheMjANDYxshrm9ufvh9z/xwJCHQlJDqTAn7P5t+oLlQ0CpXZVSqFCYQ8lnmevLaAz2PWcqUOVGoatgV0JiRaI8vfzF8ojDhX3dUYy2uM5c3PHkvA78HMLIyF7vuLZ10cHsQFvzdrjsaRIRenx0wQ4/MAQZ9gQEFERERERERERPNsqkDid3/3d/HpT38aAwMD+Iu/+Av86Z/+KW677TY88cQT2L9//6K/Nz1j4tw5FAtRavHdurvvvhuf/OQnG06766678LGPfWyZj2DzmpycBAAkvUA0pZEpalQd05YIMBuYIb+AJYBsWSNbNH3x4yGBREjAawNjOY2RjEJukU3VcwkALRGJWEDg1JgLl4nGskRtIBoFeqMAOgDsA5QGMkWB8YLAWF5gsihRrALFqkCpKpArAcMZgZqz8HuoJazQEdPoiCl0xBTiIY1YQCPsNxv3qyWfm1y9K18hJwcAr2WqGabne3hsgZAXCPjM2q+7QM0BxnMKZyYuMDFYQ2Nz/hz2CezvthD0bb1QYvrzjmitce1RM3DdUTNw3VEzcN1Rs3DtUTNw3dHFSCaTy7rcpgokOjo6AJgZFJ/5zGdQKpVw33334Z577lmydVM4HAYAlMvlRS8zfd70ZRfyO7/zO/jIRz7ScBorJGbNXZRtqcUv17XI6W0p4ADM0d9KmU1ywAxplsJUUBTKGrmyuUxXQs5shu7o1Xj+tItyfTaVsAQYUlyAeBzoO89lao6eqhQwlRaDaYXBSY2BCYGXBjVeHGickyHFbOiUCJtWUO1xgfaYRGdiZVpCRWPL+zBstgpgFrFjvsYqC18uuvRInHXlxCSwu8OaCVtsa+sMUV/uX8JEK41rj5qB646ageuOmoHrjpqFa4+ageuOVsumCiTO9Z73vAf33XcffvrTny55uUgkgkgkgmw2i3K5PNOeaa6hITM9uLOzc9HrYfiwNjyWOHf+84xEWCCxQGYU9gtcv8vCyVGFkM/0+vd7gLNp05Jouid+PCTQFpUoVMxA6ZqjIYX5/ZBPYKJgTqOFeW3z3CbDwLZW4Krtsy9U3dEYzmgzDLqgMVk0A5kzRY2R7HRLqFlCAO0xgZ4WiZ6Wqe9JiUhgedVM1HyOAg4NNrb5CvvN8O1kWMK2zDwTxzWzKXIlMwejNSqws12a9zqAYlXj8KAL1zUtrtrjYuY8IiIiIiIiIiLaODZ0IPHwww/jq1/9Kl72spfhgx/84Lzzp8OBer2+5PUIIXDgwAE89thjeOmll3Ddddc1nJ9OpzE8PIxEIsGB1huY1xbY19WYZGxrFeiICwxnNJJzhv9OK1Y1/J7Zo7qV0hjNmSP/K3UzIFopzUqLZfDYAr2tAr2tC8+7qNY1xnJmbsVwRmEgrXB2QuPJ4y6ePD57uYAXaIuZ4CgVE1NDtiWiAdP6y+9hYLGeTc/aWGoOxkDarIXdHRLlmgkNpyui8kMujg0DPS0S21OSwQQRERERERER0QayoQOJiYkJfPGLX8QPf/hDvP/974eUjRud//Zv/wYAuP766897Xbfffjsee+wx/Mu//Mu8QOJf/uVfoLXGz/7sz67cnad1w2sLbGtdeFMzdE7/eylNgNERb1xr1bpGvqyRr2icndAzFRfnw2Hds3weMVUJAcwtgSnX9Ew4MTChMJLVGMspnB5zF7weKUxg0dMi0Rq00dHqIOQTCHoFMiWNoUmF0axGe0zgxt0WWqPmtZwOm/weM8+BmqvmaBw8u/BrrDRwZlxhaFJjR5tER1zAPieYKFU1HBfwecxQcYZURERERERERETNt6EDidtvvx3bt2/H4cOH8fGPfxz33HPPTCjxjW98A5/61KdgWVbDXId6vY7jx83h1rt27YLH4wEAvP/978d//+//HZ/5zGfwxje+ES9/+csBAIcPH8bv/u7vAgA++tGPruXDow3E5xHweQRao0B3UuPYsMJwxhwBHvQKtEQEKjVgsqjgKMCWQFdSordF4vCgwnh+/Q4qbraAV2B3h4XdHY2nV6YqKsayGqM5hWLFbEIXKqY11JPHXQAeAItXSH3jKQe72iWEAPonFKpTF42HgB1tEn0pib42iW2tEj4PN7TXm7qrcWTIxZEhwGcLhPyA1kChgoZQUMC0c9uekkiEFq7QISIiIiIiIiKi1behAwmfz4evfe1reMMb3oB7770X//RP/4SrrroKJ06cwLPPPgvbtnHfffc1VDwMDAzgsssuAwCcPHkSfX19AICenh589rOfxYc+9CHceuutuO222+Dz+fDAAw+gUqng7rvvxtVXX92Mh0kbjNcWuLzHQnfS9MifW2WhlES2rBH2z/bAv7xH4qkTGsWqbrgOS6BhCDc18nsEelsEes+pqJhWrmkc68/ClREUqxqlqkYkINCZkEhFBU6MKDx+zMWzp1xYEtjWaloAlasaJ8cUnjml8PRJExRJASRCAuGAmYHQEZe4vEdid4eEZwUGb9Olqzoa1cLC52kA6YJGuuAiHlRIhgWUNi3XAPP6CmG+A6aawrbMax3xm8qomqNRqprLxYJ8zYmIiIiIiIiILsaGDiQA4JZbbsEzzzyD//bf/hu+853v4Bvf+AaSySR+4Rd+AR/72Mdwww03LPu6PvCBD6Cnpwf33HMPHnvsMViWheuuuw6//du/jbe//e2r+ChoM1po01JKgcQ57YBsS+Cq7RaePO7CcTW6knJmoG/N0ciVNCbypp3QcltBkams2N6qEI0tPAH9im0WrthmwXE1pATkOS19KjWNM+MKp0YVTo4ppAsauRIwmFZ4sV/hgecBjwXcsMvCz93sQdDHTeqNIFPSyJSW/z4SMO/Rue+9eNBUW7REWG1BRERERERERHQhhNaaO5y06tLpNJLJZLPvxrqWLWlIAUQCC29sK6WRLpiWRK4yR3cXKotvrvpsgYAP8EgBDUBpDdcF6q7pz+9sgS5RuWwa0djKrjutNQbSGgf7XTx7WuHkqEIsCLz7Vi8O9C4cfqy0mqPxwhmF4yMuTowoTOQ1uhISva0SXQmzflxlZi14bVNxY1uAmjpNa41IUCAeNIPcXWXmoNQcc/3T1QIBr0DQh4b5DFprlGtApqiRK2tEAwKtUQHvFqwU8doCXhuwLcCWAgIABFDIptHT2YJIwDx/NccEXJW6aTVWrgGOqxELCrREzEB2zriglcC/a6kZuO6oGbjuqBm47qhZuPaoGbjuaDVt+AoJos3ifG1gpDQbv63RxtNrjpmlUHM0gl6BoE8g4MW8Ib/nKlZnh0RPH/3tsQQ8NlB3zIYp08r5hJgevi3x+qs1Hjvq4h9+XMdf/nsN21NiqtLCDMdOxQTaohLJiEAsIBANmk1+cz0mLLDkhW1Ev3TWxd//qI6JvHl1Qj6gNSpwekzhyNDqpExmKDQADbgacM6ZNT09o+GKbRKv2G+jt2VrVA7UnNkQZ+54+lxOoSwWHsg912RR49SYgiXM+1sKE24kQgKpqEAsKCAvcH0QEREREREREa1nDCSINjivLdCdvPBNy5BPYG+XhV0dpje+34uZuRbT6q6pqnCU2XwtVExVRq5sZjJsdUII3LLXxr4uia8/UsfpMQUhzPNSqmq8NAAAi29Mm+HmAttaJdpjZvNZCNMKKhYUiIcE/B6gVDPX9/hRF48ddeGzgbfdaOPqPgttMQEhBJTSGMmacEoIwJKmyqHumNeu7prTLCmgtUaubKpy8mUN2zKD2b1TfyPoqfkK5Zo5mr9U1dDahBJCANGAuW/RgEC2ZNqJDUwo/PCgix8edLGtVeCV+23csMuC38sN9fNxNeBOhYJVx4SFZ9OAJYCAz4SMQe90xYqpujj3vUpEREREREREtBEwkCDa4iwpEAksfJ7HEvDMdCESSIZnz6s5GpmiaSE1d7M64BUIeM3vlWsaxarZ+B7PqU1bcZEMS/zKG3wNp2mtkS0BozmFyYKZBZIrm9Y9GmbTP1c2cyrOjJ//aPppl/dIvOOVnnnzC6QU6EwIdCZW4hFdOK01To9p/OiQg6eOmyqOf3ysjut3Wbhxt4XdHfKCq0G2OlebALBQaXznCJjAqi0m0RIRKFRMEJUuaAS9QDJsWkE5rnn/laoaYb+p2GGQQURERERERETNxECCiC6K1xZoiwm0xRa/TNAn0BIxfy7XJPrHFYYmFc6dzR0PCnTEJQbSCvnK5ogthBCIh4B4aOm5ElprTBbNZjI0oABU60CuZAKfSt20ZQr6TBuf/d1yXc4bEEKgr02gr82Ln79Z48njLn50yMGPD7v48WEXQR9woNdCT1IgGjTVFe1xM+R9PT6e9Uxjeji3Cww1nldzzHknRue37zoyZNpBtUQkkmExM4i9UtPIVzR8tnltppVrGiMZZdrFRUTD4Paao2FLzGsppZSZT3O+mSJK6QtuR5UpmvuTLpjbUEpDaVMJJKUJQRMhYVqkBcVMRRARERERERERrR8MJIhoTQS80y2iJLIljcmCaRXV3SKQDJuj/buSEiMZhZNjCuXq1phhIYRAMtxYfbLR+b0Cr7zMxisvszGQVnj2lIvnTrt44piLJ865bNgPbGuViIfEzADuUlVjIq8xUdCoO9MbzkA8ZNpbbU8J9KVMdQDDjOVTGpgoaEwUTEVOwCPgahMuTPPaZj1WahqZ0uzpx4ZNKGZLoFwz7dz8HvN6dCUENIDBtKn4qTrmvEjABE/RABAJCCgNDGc0hiYVilUNS5jbC/hMKBkPSYT8pgIEMLNKMiWN7FRgV64v/Ikw3fKq7gKlmsbAZOP50/ezMyHWXUBRrJrg0Web943W5rmtO6aNWjQwG/rkShqjOYVq3bR789imfRrfB0RERERERLSRMJAgojVlyaU34NvjEu1xCa3NBmPNMRtx2amKgcU2JWl96k5KdCclfvY6D3IlEzRky+b1HEwrnBk3w7jPHZTtsYBkxAwCVxpQCugfVzg6Z3B3yAdsT8mGr/MNh6dZC72Xao7GcGbh99i5c2MqdY0jQy5OjYmZ3517XqWuMZabvfzUXPQZrjb3oVyHqRDC6gxln3s/O+ICrgLqLlB3NBzXzEuBAHqS80OL2tRl1NQdD3hXturi8KCLTHHxzzQpgIhfoOYs/HoBprVee0ygNSpQP7f8bI66qzGe05DSzCfxewXC/uU9llJVYzijsKNtfVZoERERERER0cbBQIKI1iUhpo+YB8J+ga6kOT1dUDg7oTGeX3zz0pZms83rSihphnJT80WDjS2BpillWlPVpgZwB7wCYT/mbXwqrTGS0Tg9pszXVJhx8OzsCxycOto+FhQI+U0bH9syR5L3tUnsaJMNrYfo0s0NIpbS7Cix5micGV/8XkyHFp1xgXIdyBY1qgs8Nr9HIOIX2NMp5w1td1yNXNnMjCnVTGAWWmS9jWbVkmEEYIKQbHnpy9RdjbNpMwg9l3XQMemgI26qQqarK/JljRfOuPNCDb/HVFhE/GJmtk3Qh5mqNcDMAHrutIu6a0LhA70WfB6+h4iIiIiIiOjiMJAgog0lGZZIhoFKTaJSB1yl4Sqz6ey1Z9v+AEA6bSESs9E/rjCQ1rAk0BoxsxhqLjBZMK2jFjry2GuLmX74tLqkFAj6zEbobMOeBS4npgd3S9y815zmuBqDkxpnpkKK0Zxp8XN8RKHmLHw9qag5Ur49Lqe+C3TEJAI+oFgxG7COCyTCZuC75BHhW0bN0Ti9RGgBzFZ/ZEoa+7ok2mISrtLoH1fon9ANVQrjOYV93RbaY41D6JXSOD68Oh8upZrGiVHzvtjZLqGUCVvUAg+rUtcYSM8/I+xX2NYqYUngYL87M/cnU9J44riLnW0S0aBA0Dt/jghgKirSBY2uhLjgWSFERERERES0uTGQIKINye8V8HuBpTawAdPOZGe7he0pPa/VSvvUQO66a+ZZlKp65mj66SOAS1Uz8Hcgff6jmWnt2ZbAtlZzNPgrL5s9XWsTVDmuac8zUdA4OaJwclRhYMJUVTx/pnFDWAhzhPhcljTrwe8BfF4g7BPoSEh0JwV6W0yLH7aw2ZrqrsYL/S7apkKwhaopHAW82O8iW9Toa5MzYemZcbXq7ecqdY2DZ93zX3ABhcriv1tzNA4NmvOkMBVsiZBpxefzCJwZVxiaVNAA+scFdndKpKIS1brGeN5Uj0QComGwOhEREREREW0dDCSIaEtYqu+7xxKIBbHg/IGgz2yatcckxnIKx4cVSjUGE+udEAL2VLsmP8xQ5b6UxG1T57vK9NMfyZre+MMZjVJVIxow7Z5sC8hMDVPOljSqdVNRc3Zc44X+2SCjNSJw3U4L+7ulGapc1UhnLLiyjlLVhCL7uixc3jO/vQ9tDqPZ81c6nE0rDKQVWiISbTGzab8ZKA3ToqqscXp8/vnlusbzZ1wEPI0BzFDGfA94BFIxgVSU81+IiIiIiIi2CgYSRETLlIpKtIQFqs5sXUb/hEL/xObYXNxKLGlaNbXHgau2W8v+vekgY3BS49iwi6dPuvjusw6+++zcS3kBzPaLevglF7YE+trMUeLZqfkCLWFzH9piEmE/EPSKqQDM/DnkB5JhVmBsFhrAeF5hPN/se7L2FqsGKdfNXI8z4woeS8xUKVkS6IgLdCcl51UQERERERFtMgwkiIgugJQCAe/sz3s6LQR9AkcG3TUb2hsNmBZFZycUMqXGW5UCC/aKP5cA0BaT6G2RqLsaxamWVY4CXNcc2V9zTNsXpc2g8IBXwO8RyJUb29MEpgbjjudNb/3NbG6Qce0OCz93s8bJUYXToxp+r6mogZNHWzKKoE/AVRovnFF49rSLU6MKIZ9AS0SixwtM5M15Si8eaMWCwGU9Fi7rlmiPScRCAhH/wn37iTayubM36i5waswMsG+NmmH0YT/XPBERERER0WbAQIKI6BJ1JyWCXmAkq+H3mI37qgMMphvbO9kS8HoE6o4ZxnwxW/dtMYnLuiUsaY6snywojGY1okHTx93nMa2GxvMa+bLp1R4PCYR8AsWqOa1aB3pb527wCbREFr9Nx9WwrcbNwHxZY7JoWhzFQ+a83UrjbNpsIs7dXNzMpBDY1W5hV/vsabmsQnRmiLHAqw9IvPrAwn/dOq5pCzU9w6RUm/1ztqRxeFDhsSMuHjsy289fCCAaMC3GGr5C5nt86ucwB3LTBqcBjOUUxnIKqYhEb6tEpa4xWdDIljWC3qk1HxII+TDvc2pV7pPWrFoiIiIiIiK6BAwkiIhWQCIskQg3nrat1QQG5RoQDZoNs7kbWfmyRv+EwmhWQWkzy6Itas4fmGw8al4KYHtKYkdbY3uhhW43ERbzTgPM0fup6IU/toU2+SIBgUig8XQpzYDpzoTA8KQZBM55G0uzLYG22NKbm+mCwtEhZTZhSxqZkvmeLQJnJ9SiFTFSANHg/OCiKyHR1yZngqSFVGoag5MKQ5Mm7LqsW67JZu/5OK7GaFbD1YBWgJBAIjT/vUWbz1heYSzf+LlYquqGFlhe27Q880gBKU3rJzPc3lR9xYIC7fFLq7Y4MqhgWcDujuW3eiMiIiIiIqJZDCSIiFZRIiyRWOS8SEDg8h4Lu9olSlWNeGh2XkAqJvDSWYWqo5EMC+zptBDybYwNV48l0Nsq0NMiMFk0FRnTHYbOjCvkKwwpLkQyLHHTHrngeUprFCswAUVJI1PUyM0NLUoa2RLQPz4/uIiHBHa0mWHf29skclMVGUcHFUZzjRcO+oCrt5vh3W0xiVTUzLu4FFqb+5YtmRZg1TrgsUyQN/e6667GiRGFJ4+bmR2l6vzr8lhAS0Sgp0Wit8V8T0UFEmGx5ED7ZhvOKLgKaI+JdRH4bHQ1R6PmAFik/ixT0jg9rhD0CXTEBDoTFzaj4uSoOxMWCwC7GEoQERERERFdMAYSRERN5vOIeZtiybDEy/YI5MsayfDCm9HrnRACyXDj42qLCZweUzg1tviR/bR8UghEAibc6mlZ/HJKaRQqpp1X/4TCqVGFk2MKz5xUePpk41HnrRGBG3ZZ6EoKdMYlhjMKT51w8egR8zXNZwMBn2lRFg2Y17olIhDwCtRdDcc1m7aBqUHdjguMZjXGcmrq+/Tm8XztMRMmjOVMSys9tVa6kgKv3G/B5zEhl6vMY0oXzPU9edzFk8fnPj+mgqI1au5bMmyClMDUvI9trRKxYHOCgNNjCp/+1ypcZe5nR3w25NEa8HmAkN9UfyhtKqoKFcBrA6moQCoq0ZUU2J6S8HPw8wUpVTVOjJr5L4mwWbNSmIqKaUKYNZIICXhtgcG0wsnR2ffK6XHz553tktU5REREREREF0BorbklRKsunU4jmUw2+27QFsN1t34Vq+Zo/rqjUXWAyYJesr2TzzZ94qf7xVfrGgNpjYm8WrNh4suVy6YRjW2MdVepmSPGz4wphP0Ce7skWiILB2CjWYX+cVM9MZYzlRjlqZkX2ZJGpb682xQAkmGBVEzMVDH4PQI+GyjXgFNjJjDJlTVaowLtMdNm6todFrqSS4dz5ZrG2Qk1tTY0xnPKfM8vfv+SYYHtKQFbCigNuMpUa1TqZm36PQJBrwkH2mMC3S1mGPxSLa/Op1LTuPufq5gsaLz6gI3RrMJgenZYvBBAtY55gU3IB1QdE+7MPJ8C6E4I7GiX6IiUcaAvglRULLhJPphW+OlJF0+fcFFzgFv2WXjlfhvRJUKZSn0qPKpr9LZKeO2tt/ke9gsUKwvP/ZHCfD75vSZc9tqYeY4qNbOGEiFTtbNZ8e9aagauO2oGrjtqFq49agauO1pNDCRoTfCDjJqB625jyZY0hjMKlZoZGisAhPxAa0QuumFaqZnN5sniVMCxDoZpb6RAYqVorVGuAeN5jWpdw2MLeCxAKaBU0yhXASlNhUxLRMCzxu2JtNYoVk3wVa6ZcCJXMkfInxxVGM40rhu/x1QoeG0TfpWqgNNYSIJr+iT+w8u9M8GEUub6IICI38xY8XsWnm3xtz+o4bGjLn7+Fg9ee8Xixao1x7TkkhII+wFLCiht1vpo1lS7nBwxjyFbmv29tpjAW27w4Nod5uj9I0Mu/vUJBydGzIMI+wHbAjJFUxWwq91slrsKcJSG65rHW6pq5Mqz12tJYFurwO4OCwd6JXZ1yHXdEms9SUUk9vfINV/7a2E1/q6tORrHhxUc17Rja4nMrySkrY3/xqNm4LqjZuHao2bguqPVxECC1gQ/yKgZuO62npqjUXfNEeS5ksbApEKpOvvXXNgvYFvmvNVqGbUVA4mNru5qCJij3YWYHyJobUKM4UmNs2mFF/tdPHdawe8B7rjORq4MPH7MaQgFALPpb8IJoDMhsbNdouYA//hYHQd6JX71Dd4VafejtWlbdfBUHoO5AH5y1EWlDvSlTBuog2cVpABu3mvhxl0WdneaAOK50woPvejgzLiCJc39taQwf5aAzwukohJtUQHLAk6MKBwfVijXzO0GvMC+LonupERnQqIjboa0LzUPQ2kNuUVbHPk9psrGUSb8ifjNrJ2N3vJpJf+uVUrjbFrj1Kg7LwSMBgRaIwKtUQmfB6g7QN01Lc48tpklsxUreLYq/huPmoHrjpqFa4+ageuOVhMDCVoT/CCjZuC6IwCYLCpU66ZtyvQRtkppZMsa4zmN4cz8ygqPZY5u93sF6o4ZEr1cDCS2hudOu/j6I3VMFs3aaIkIvGy3haAXyFfMzId8WSNfMcO7M8XZNRQNAL/7c35EAiu7eTq99vJljX97uo6HX3LhKuDGXRbefION1uiltw1SWuPsuMYL/S5eOOPi9FhjKyMpYNpxRQTk1M05rhkoPVkw7bD2dklct9PCFb0WXGWqMKp1jb6UhN+7tTaUowGB/d0Wwv6Vf9ylqr7k4fPLsdy/a+uuafVWqACFiobfg5lWfKUqMJQx82UupdLNYwlEA0A0KOCzTRs2rc2cnUtps0brD/+NR83AdUfNwrVHzcB1R6uJgQStCX6QUTNw3dFyKKUxltcoVjTCfjOg+dxN0ZGMwtFhhdpUj/+wXyDkE1OzExr/Gp3eFLaE2RTLFBfuPU8bX6VuBmm3xQR2d8glj/zPlzVOjCicGVe4aruF7amVnylwbhiWKWo4rl6RIGIx1brGSFZjaNK0vhqeVBjKmBke0//CtKTZeI6HTPXF0SEFV82/Lo8FXLnNwvW7LFzRK+HZIke7CwBtMTkzeP1CjvKvuxqnxxRawub5FUJgsmgqWXJljc64aa21EpUD4zmFw4MKAa+5v8mwgOMCZ4cn4A8lkAgJJMLz11qhYma7jGQUmtlVrzViqpRWI/yhtcd/41EzcN1Rs3DtUTNw3dFqYiBBa4IfZNQMXHe0kuquRjqvEQ819jIv1zSyJfOVL2tMjKexf2crupJmVkKpanr9D082dzOONr+NUp1Tqmo8d9rFsWGFgNe0tJLCtJA6NmySCr8HuLrPwg27LOzv3jqzKqaHvve1WYgtMWwcMJ9Jz55ykSubDxafLRD0YaZqZ5rHMq2hgl7z2RWYGsA912jWDIDf3y3ntZByXI2jQwpDmQVSJDSuu4BHoD0uoDVQqgLFmm5om9dsAiYcmw5PIn5AzllbhcrsPKLWiFzxKiZaOfw3HjUD1x01C9ceNQPXHa0mBhK0JvhBRs3AdUfNsNi6U0qj6gDVOlB1TOuaSs0MWc5XMFN9QXSxNkogsZTJgsZPTzh44riLM+PTFUnA9TstXLvTglbARMFsGrdEBLa1SrTHRMOm8maRDAt0JSSCPoGgt3Hj/Nww4kKFfKYaI+wXODuhkK+Y69nWKrG7w5q5XM3RePqki+ISocJGX3dSmNBGacxrFzUdsPS0rEyVCa0c/huPmoHrjpqFa4+ageuOVpPd7DtARES0FUhpjkwOeAFznG6jSk2jUNGwJOCxBWwJlOsaxQpQrGiM5zWqDC1ok0uEBX7mKg9+5ioPRrMKTx138fhxFw8dNF8L8dlm8z4aNF+dCYG+lMS2VrkmMxRWS7qgkS6Yxyxgqho8lhngXKnjkioPilW9YMhwZlwhGhBoi0nUXY1nTi0dRmwGSmPRz9ZyXePUmEb/uEJXUqItJqG0hlLm9WD7JyIiIiKiC8dAgoiIaB3we+fPrvB7BRIh8+e92gzXHsuaSgtXaTiuaTGilrlfaAls6LZRtjTDaauOCXCW+7hpY2qLSdx+ncQbr7VxdsIM0g54BVrCZhDyaE7hzLjZLM4UNc6MK5Rr51xHVGB7SmJbSiLsN0fDCwHTKsovEA4A8eD6r7DQMDNLKvXVv61DAy78HoHDgy4KFb7JAPO52T+h0D/R2LaqIy6xq102tMBylRnePVnUKNWAkM8ML48GTRs/IiIiIqKtjoEEERHRBiDEVN/zUOPpdVcjXdCYLJjqirBfIOgTKNdMW5tsyQzrbouZjdx0QePYsEKpdv6NxqBXwLbMQOZmbkuG/aZ1TUdcwLYa+72fGTfDcrltunkJIdDbKtDb2jgweVtK4oZdjZet1DUGJhROj5lhz6fHFJ447uKJ4wtXVwBmmHZ3i0Bvi0RfygyBTkVFwyyF8ZzCT466eOKYi6qj0R6T6EiYdbktJdGdEJtmCLejgKdOOHxPLcNwRmEsq9Aalag5GpWaWYMLPXcCJphIRgRSUQ7XJiIiIqKti4EEERHRBuaxBNpjAu2xxtNjQYGO+PzLt0ZN7/izaY10Qc1soM2tNgj5TMubtpjZlHWVRq5sKjJsy9xmtW6OAE4X9AUdRS2Fqfw4t92MzxaIBASEMJfx2uYxxENi0d7tYb/A5T0WdrZJ9E8ojGT1smZxTPeMb3YLLEuYx+C1Bbw2kK/oi54JQIbfI7Crw8KujtnTpgfLV+umPY8ZuKzN813SGM5onJ1QODXq4uGXTHARDZj15yqg7gJjOfO6xIJmKPLpcYUjQwBgLi8F0JUwocl0QJGKSUQDmDckeiPgKlw+VwMj2YUHfs+lAWTLGtmyxslRhYhfoCtpZqDYrJwgIiIioi2EgQQREdEWI6XAtlYzEHiaqzRcBbjKtLOZy5KmOmOusF+gJWL+XHfNxm62ZK4j6BMI+oBaHTg7oZCd2mRvj5n2Jn6vQM0xVR1118wNCF1Cr3+/V2BPp4XdHRoTeROUBH2mv3vACzguUJtqcxXwmtOEMKHIRN4EM80YLH5Zj4W2WONR/6WqxkhWwVVmcz3oM69JoaJRrALOnJ5bNcdcnq2rlhb0Cezrspa8jNYamSJwYsTF8RGF48Om/ZM1NbPhxl0WbtpjYV+3hCUFtDbrbGDCVOn0TyicGVd49IiLR4/MVmP4bBMCpqLmqPiupMD+bguxIDegt7p8RePwoIuTowKXdUu0ROT5f4mIiIiIaBMQWmv+N5ZWXTqdRjKZbPbdoC2G646agetuvnzZbJqv903YmmOqPYoVM/S3UtfwTw2uDflMkCMAuFrj5IhCpnTx/4TqSkjs7156k3w5lDJ96nMljf7BCQh/YtGKFVuax2AJ05anvoyBIj5boD0uEPQKDGcu7TFvBbmSCSiGMwpjOT3zlS5ozP0Xd3dS4PqdFl5/tQ1rnc+vOJ9cNo1obPU+84oVU1FQc4CQH1PvR4GQH5tqJkNviwlsHQVkSybkFQLw2maYeammUSib720xge6kXLR6bCvg37XUDFx31Cxce9QMXHe0mlghQURERKsqEtgYm2Ze27SzSobPd0mBxE6JsxMKJ0ZcWFKgNSLQGm0cWpsuKAxn9bz2VEGfwJ7OlTkaWkqB8NQmrR8WkkkbwxmFQwNuQ+XEzjaJvrbGAKTmaOTLGuWaaWNk2hlpWFJASiDoNe2JplsOdSUlChWN8ZxC3TWtjHLl+Y9vK4sGBa7YZuGKbY3PteOaapxTYwoHzyq8dNbFvz7p4MV+hQ/8jBfx0MZ4j6ymkYzCc6ddTBY1ao6pABpIKwxNLr6+/B5T0eX3AH4v0B6XuHGXhX1dct0PKj9X/4TCcEYvKyg8OapxZkyhPS6hNVCuaVTrQEfCzEKZbgHluCYQC3hFwxpLFxT6xxUiAdNmbDMFO0RERES0/rFCgtYEk1VqBq47agauu62l7urzbubly+YI+UzRtIa6ps9alYG2c9deuqDwwhkXrgL2dVnoSq5OOxilNI4OKQxMLtxDX8CEGvmyhnP+NvtbhuNq/MvjdTz4gotIAHjPrV4c6JUrNm9Ca7Op77EBuYIzLKZbhPk8puJGCIFsJo1INLFoAFB3NI6PKBwZVMiX9VSYpSGFmZ1iSeDEiMLgAsFDPCSwu0NiZ7tE2A8UK6Z92XQLs0JFo1zTqNSBSk0jWzK/Fw0Auzss2Ja5/q6kxK2XW1ti491jCfS0CJSqZhj7dL7h95jgNFsy81Om2RLoaWkcsu2xAJ9HwOfBuq3g4d+11Axcd9QsXHvUDFx3tJoYSNCa4AcZNQPXHTUD1x01y7lrL1cyQ75bo6vfm34kq3B4wG0IHRIhM9sj7BdQyoQy43lTKdDsgeLrxdMnXXz1oRoqdSAZFrhhl4Xrd1roaRHnDScqNY0fHHRwsF/B5zHD6C0JjGQ1hjMKpaq5nM82VTm9rWZY/Y52id0dcmajWWmNZ04qPH3SxZtvsOfNNQHMxv+/PlHHI4fcmYHXUphBzdP/k9jfLfHaK2xc3itRqwPPnnbx5HEXRwZNRc1Swn7g6u0WrtlhTbUiMmHKhQYIo1mFx4+5eOKYOzOIfFpHXOCdr/Rgd+elt0rbSvpSEjvb199zxr9rqRm47qhZuPaoGbjuaDUxkKA1wQ8yagauO2oGrjtqlmavPa1nj1QHgER48SAkX9bIFE2PfCkAIUzbmWLVzAwo1bbOP0/Hcwo/fMls3meKU89dSOCKbRJXb58dpD2tVNX40SEH33vOQaFi2hY5ygxvB4CQD+iIS7REBOquaeWTK2kMTs4OQA/7get2Wtiekvj+Cw7OTpgzepICH32rDx57Nqx45JCLf32ijmIV2NUu0ZkQqDpAta4hACi3DhceHBpQUBpoCQvkpiohpAB2d0hc1iNxWbeFloiYChpMi7C6Y1p/Tc9oWUmu0nCnnpeHDjr4t6cdOC5w1XaJvpREV1KiIy6QCJtWazVH45lTLh474mJoUiEaFEiEBNpjEi/fZ6E9vnWHTrdGJC7vmW0FtR40+/OOtiauO2oWrj1qBq47Wk0MJGhN8IOMmoHrjpqB646aZTOtveke9xOF+f9M9VimH346P9uOZjNQWuPEsMIzp1w8f0bNHOEf8gHX7LDQGZd4od9UHChtKireeK2Nm/dYsKc21OsOEPRhweqKmmMGbh8ZVHjiuIuRjLl+jwXcerkZK/fA8w5uu8LCL9zihas0vvpQHY8fcxELAj93swfX77TmXff0UOt0QeGhF108fsxBKipxwy4L1+1cnfZkF2M0q/D1R+p4aaCxd5gAEA0C1TpQqZsWT91JgUIFyBRnQ5zLeyRu2mPB5xEQwjzPO1Ibb1bFxQr6BK7oXT+v52b6vKONg+uOmoVrj5qB645WEwMJWhP8IKNm4LqjZuC6o2bZjGuvVNVTcwLMEfmJsEBLWEBKgWrdzCYYzmy+4RRaawxnNJ456eKpE+7MfAVbAvu6Ja7bYeGG3Rc/E0FrjbNpjVOjCldttxALCrhK439+q4ZjwwofeK0Hjx118WK/whXbJN5/mxd+78K3NR1IbBSVmsZQRmMwrTCS1UjnNdIFBSFMy6wbd89uuiutcWxY4aEXHTxzSuHc/zXFQ8DLdtt42W4LnYnzt9na6KQAdrRZ2Nba/Me6GT/vaP3juqNm4dqjZuC6o9XEQILWBD/IqBm47qgZuO6oWbbq2psecmza/5jwolzTKFU3zyDtwbTCRF5jd6dEYJFgYCWkCwp//E/VmfkTt+y18M5XeZYcbLzRAomLlS4oHB821SlaA2M5jZ8cdZGequIJeIFtrRLdSTHzfFkSaIkIpKICqahELLSyQ8abJR4S6ElKRANi0aBqtW3VzztqLq47ahauPWoGrjtaTXaz7wARERER0cUK+8WCbWSU0jg1pnB6TGGjH33TlZToWoP/DybDEu++1Yv/34M1vPZKG2+5wW76kfDrRTIskdzdOEfijuttHBtSeO60i9NjGidGFA4PLn4dHgtojQikYgKtEYGWiER7TMybE7LeZYoamaIZWuKxBEJ+IOAR8HuBVFQ2ta1T3dV4sd9FZ0KifYEB7auhUNHIlUww2tMiEfRtnNeSiIiIqBkYSBARERHRpiOlwM52C8mwxEtnXQgBtEYFWiMSGhrFimkJJQTg9wh4PWb483BGo37OcAopgJaIRFdCQGlgeFJjoqBm5gtsJtf0Wfgf7/OvqwHG65UUAnu7LOztsgCYQdqZop5p7VR3gYm8xlhOYyynpr5rvHBmeu2YTf2WsMAbrrFx096Lb8PVLHVXI1MEMlOx36lRhe6kxI522ZTHcmhAIV3QSBdcnB5T2J6SaAmLS17PhYqG4wK2BQhhPivM7TR+XgxNKuxst9DbunWHoBMRERGdDwMJIiIiItq04iGBm/ZY5wwfFkiE5l+2PQbsbNcYyWhUHQ2vLeC1gVhQwGvP/n4qCtQciXJt+tpMm6jRnMZkwcwaSIYlUjEBVwFnxhTK9Y2TXjCMuDiWFGiJND53nYn5l1NKY7KoMZ7XOD6s8P0XHPz9j+r45lN1tMUkAl4g5BPoaZXY1S7R0yI2TAWFBnB2aj5HKirg9wABn4AUQM0xs2CqdRMGlqbeP5GAeY8FvSbwUxpwXDPvo1TT8NgCu9vledtDDaQVxnKzfdoKFVMtIYW5/vaYRFdyflBQrWtYcuF1ny1pnBydbc11Pq4Gjg67GMsrXNZtrWqLNSIiIqKNioEEEREREW1q8gI2cy0p0JU8/+Wnw4pp0aBAexxQSkJPXc+0roTA0KTZhPbapnXPWE4jX9k4IQWtHDkVXLREgH1dFl57pY2HDzp49IiLoUmFcs1syuOoqaCwLcBnA1KaddWXErhim4XLui2UaxpDkwqjWY2g38yraIsKJMKLhxhaa+TKQNCHVatiqLt6Zhj7+aQLOM+Gv8ZETqGvzQwPL1Y0+scVJqouEiHzWMs14NiQu+BvKw1MFjUmiy6Gswr7uywEfQLVummzNZwxbd08lnlPW9JURSkN5MoX9x7NFDUeP+pgV4eFnpaFqyXqrt5wFTFEREREK4GBBBERERHRClko/BDChBxz50CkYhpPHnPgMpNYkNcWsC1zpLzj6iXbY9kS6G2VKFWBkezGm2Tu9wi8/moPXn+1B4AJDMo10/7o+IjCmXGFmmOGaVfqGs+eUnjmlAJQX/Q65w7UDnhNhYIUwHheYyBtQg+/B7hyu4Vrd1jY3ioR8qOhEmg9cTVwfMTF8RHzcy7rIlpT6J8wj8uSYlnvpUxR44ljDtpiEqNZ1fA7dVejvnCmcdH3+ciQi9GcQtAr4CjAdTWqjqn+cBQQ9Al0JyTa42LdPvdEREREK42BBBERERHRGgv5BHZ1WDiyyFHd65HfI1BZodZTAkAkIJAICUgJ1B2g5gJBL9AakYgGGzdnHVej5pi5DOWaRr5sKkwifoHtKQmvLaC1hm2Z1j3TQlMDhovVjZP8CCEQ9AGX91q4vNead/50K6JjQwohvwm72mMSpappGzaWNfMqRnMahwcUnDkZTcALdCclOuICZycUnjjm4oljs2vQawPtcYFd7aZd1GU91rof0qw0oC4g2XM1MJRZu+DKDAFf+P6VqhpHh03Q0hGX6G2VM2uWiIiIaLNiIEFERERE1AQ9LRLjebXs/vQXIugTKK3gJnzEL3DtDgvjeY3DA+6yjkYXU/cjGhCIBAQ8NmAJc/R+OCAuqF2NbZmKCcDMA+iIL3B7QmBflwWPZVoAbU9JpKISSmmcGDWVBsu5z36PWNczP8J+gZv22Lhpz/zzLjvnZ61NdYmrAKUAn8c8T9Mm8grPnVYYzysUKkC+rHF2QuEH4y5+8KKLaKCOd73Kiyu3zw9GaOUoDQxOKgxOKrRGJJJhE0qFfAI+DwMKIiIi2lwYSBARERERNcll3RZe7Hehp/a/aw6WvRk+XWXguEDN0RBCoCMu0J2UMz3yx3JmdoUtAY9t2v4Mphtb1QS9Au1xgZOjC2/YB70CV/dZsC1z/WG/wPOnXVTqGl5LLFg5YUvTDigRWrh//mra2W5hZ/vsz1IK7O6w0BIWeGlALVrl4bEEDvRKJEJm5sepscUvu1EIIWZCoIW0RCRuu6LxTK3NunmxX+GbT9Xx+e/W8Ip9Fn7uZs95B0vTpRvPK4znZ3/22WZOxnQ1UaVmWj5FAub9eCEzcoiIiIjWAwYSRERERERN4vMIXLez8Z/kxapGOq8xMKkWrHIQANrjEttTje1dtNYNR7/7PAI9LQI9LY2/39sicXxEYTSr0NsqsbNNQkqBXElj4pxqDa8tcNV2q6G/fdgvcPNeC0IIpNM2kkkbg2mFY8MuHGU29q/abiEWXF8bpYmwxMt2CxwbNkeizxX2C1zRO9ueqCtpNnuHMqZiYCO1fLpUQgi0xQTaYhLX9Fn46g9reOSwixf6XdxxnQe37LMWHZhNK6/qaAxnNIYz55wxCZweMy3LWiICHmvhGTYXy3E1bA7dJiIiolUgtNZb51/X1DTpdBrJZPL8FyRaQVx31Axcd9QsXHubj6s0Dg2ohkHNrRGJ3R1yRfr61xzdEDSUqhqPH3NmBkh7LNOmKexf/LbmrrtyTePYkEJfm0QksL43MtMFhZGMRtgvEPYD0aBYcpM9U9QYy5nh0jXHzLMo15Yetr1ZaK3xyCEX33iqjnwZaIsJvPEaG9f0WU2rmMhl04jG+Hl3Lo8l4LVNq7SQDwh4BfweE076PY2BRc3RmMibQd6JkGmrprXGSFbj9JhCuabRFpPoSc6f6bJV8e9ZahauPWoGrjtaTayQICIiIiJahywpcKDXQjQgMDCpsKdDoiWyci2Q5oYRgNnE3NYqcWpMwRLAVduXDiPOFfCKDTNrIBmWSIaXf/l4SCAemv/YKjWNXFljIK0wucjg4o1OCIFXXmbjht0WHnjOwfeed/C3D9XxtR/VceV2C1duk+htkWiPLx3q0OqruyZgKFY1xhY432ebYEJpIF9pXK8eS0AKU5ExbTijMJxR8NkClgSkBFrCArs6Nsb7nIiIiNYnBhJEREREROtYb6tEb+vazGLoS0mM5TT2dsp113JpPfJ7Bfxe096oUNEYySiU60CtrlGqmaPQNwu/R+CO6z149QEbPz3h4snjLn56wnwBgMcCdnVIvHK/jav75HnDicG0wnhe47JuCY+98daa0hquMvNS5rZKW8+qjkbVWfi8+hKT6ueGFIWKqQza09kYSpzbMu58pxMREdHWtWECiYceegi33XYbvvCFL+CDH/zgvPOPHDmCu+++Gw888ACGh4cRDodx44034iMf+Qje8IY3LPt2/vRP/xQf+chHFj3/rrvuwj333HNRj4GIiIiIaD2TUuCGXZwRcDHCfoHwOUeOZ4oao1mFdEFDCLNp77EEglPtdADgyJC7oVo/hf0Ct15u49bLbUwWNI6PuDg7oXFmXOHokMKhgRpiQeDaHRZ6WyS6khKdCTFTkVOpaXzzqTq+/6IZ5h7yAbfss3GgV6JcMxveALC9VaIreWFVF9mSxpPHXQxNKgxNapSqGrdebuNVl1kXPQ+hf1zh0SOOGRBf0MiUNGp1wJnqpOaxTMuvWFDgym0mkAldQGXRRtQ/YR78nk4LkwWFM+OmQigeEmiNSIT9ApNFhYm8Rr6s4fcIhAPmOWqPCfg8Cz8/NUdDKXB4OhER0Sa3IQKJw4cP4x3veAcWG3fxyCOP4A1veAOKxSL27NmDO+64AwMDA/jud7+L7373u7j33nvxX/7Lf1nWbf30pz8FANxxxx2Ix+Pzzr/22msv+nEQEREREa13DCNWzmKtnubyeYDnz2ysUGJaIixwQ9jGDbvMz7mSxo8PO/jRIRc/eNEFYKonhABSUYHupMDJUY1MUaOvzQw/f+ywi+895+B7z82/fo8FbGuV6GuT2NEm0REXcFwgnZUIlVy0xSSiAaBSB777rIMHn3dQNzeJsN98/4dH6/j+Cw7edIONy7qthvkmpaq5L/GQaJjLUqxonBhVePB5B4cHzea7bZlZC91JCb/HBEuWBEo1jVxJY2hS4cSIwr897eCWvRZee6WNVHRtKpuaoX9CYSynUanPLtx0QSNdcOddtlzXKNc1xnLA8WGzblojEjXHVBKVqhqVmoajAAFgd4c1rypsMwzZdpXe0J+vWk/PzzEVNckw27QREdHFWfdDrR988EG84x3vwOjoKADgi1/8YkOFhOM42Lt3L06ePIl77rkHH/vYx2ZKQu+//3686U1vguM4ePbZZ3HFFVec9/auuOIKHDx4ENlsFpFIZHUe1BbEYTjUDFx31Axcd9QsXHvUDFx3K2Mir5YMJWwJpKISLRGBsxMKmdK6/i8clNYYz2kMpjUGJxUG0gqDaY3RnIbfA7z1Rg9eeZkFKcwg5SODCmfTGmG/qcBwFXBqVOHUmMKpUYVKffHbCnjN93IN6EwIvOl6D3Z3mMHqNUfjwRccfPcZZ+Y6IgGgJSKQzmvkyrPXE/abwGGyqFGomNOkAK7baeF1V9robRVLth5yXI2nTrh44HkHZyc0BICr+iRee4UNAeD4iMLJUYV8WaNSB2p1YFvKVJvs7ZRsa3SO9pjE/m6JfNlUqaQLCldss1Z0js6FutjPu2pd4+BZF9U6sL/bQjy0fl/rUtWEcUoBljSfPZW6Rrlmvs/9jPJYAr0tAt0tEnXHBFKZkkbIZz6vLmQGES2Nf9dSM3Dd0Wpat4HE6Ogo/uAP/gD33XcfpJTo6urCmTNn5gUS3/ve9/D6178eN954Ix5//PF51/Prv/7r+Iu/+Av83u/9Hv7oj/5oydssl8uIRCLYvXs3Dh06tOKPaSvjBxk1A9cdNQPXHTUL1x41A9fdyilUdMPMCa3N8GEpTKXF3CORB9LmaHxbms31ZFhgoqAxPKmwxCiApqs5062rlr9RqbTGSEbj1JjCeE7DYwPaKcHrC2EkozCc0ajWNW67wsbLdluQCxyxXahoPHHMnQpGFCYKGi1hgfa4RCIkkClpjGZnqyXaYwLtcYHrd174BrjWGkeGFB54zsEL/arhPCmAaBAzQ6IHJ82L1RE3wcRNe6yZVl5k1snc2RZSAFdtt5AMNyeUWO7n3fQ6lwIYz5vAbfpxCJiqn95WCUuaijRXaeTKGvmyCS98HgGfDYT8omFT31Um5MsUNbanJKILzPmp1DQmChqFsp5pk7VYMJAuKFjStJCTAjg9ZtpvXWi1lgCw0K/4PQJe25wvpGnD1sxAaSPj37XUDFx3tJrWbcumP/7jP8bnP/957N27F1/60pfw5S9/GX/zN38z73L5fB433ngjbr/99gWvZ+/evQCAwcHB897mc889B9d1cf3111/anSciIiIiIroAZtNweZvR3UnTvmhuSNEaBXa0SQymTSudQkU3bBJOhwCuMkc5+z0CkYBANCDguNMbonpmNsJq8F7E8GopBDoTAp2J2Y3MXNZFNLb8/8qG/QK3XbE2//UVQmBfl4V9XRaGMwqPHnYR8AG72iW2p2TDczCaVfjhQQePHnHxf35cx/97vI6X7bFMJUBYIBkR8C8yb2ErOHfQttLA86ddXLkdTQslzufEiItTY4u/iTSA0+MKp8fNZcSc0xcS8Ai0Rs0Mlv4JNRNajuUVUhGJ9rhAuQYUq+b9W6zOuaZJ883vEdjZLtERN89ZzdE4NKAwnp+9n1LgotvGLfZrlbpuqG7KFF10xDX2dMpFQ8maYz67ihXA7wVawmLBkJGIiDa2dRtI7Ny5E5/73OfwwQ9+EB6PB1/+8pcXvNydd96JO++8c9Hrma6a6OnpOe9tTs+PSCQS+OVf/mXcf//9GBwcRF9fH971rnfhox/9KPx+/0U8GiIiIiIiopWzUO92ry3Q12ahr81s7GVLGpYEQr7GQcJa6wVbBGlt2hilC2pmgDNdvI64xJ03Lb5x3haT+PlbvHjLjaaC44cHHTz8kouHX5qdw5CKCuztlNjbJdEel1OD0YFYUMBzEQHPRudq4JlTLny2QjQgEAmYde+xzZwPKQSkMHNLgl4sezM7VzJB3aW0UzozrpYMIxZyvndYua7RP7HwpcbyCmP5899GZaplVP+4Qntc4vSYWjDsWQvDGYV0QaMjLtAeM63VHFdjOKMxkFaNgQowVQUmEQuaSo6gV6DqmDZ3E3kNxzXBRcBrXndXmYHzjqtRqQFVR8NnC3QlJToT4qJCUSIiWnnrNpD48Ic/fMnX8fzzz+NrX/sahBB4+9vfft7LTwcSf/mXf4lUKoWXv/zl6O7uxlNPPYXf//3fx7e//W1873vfQygUuuT7RkREREREtFq8tkAquvDm22LzCoQQiAWBWNDCjjZgsqhweFChVGUwsZq8tsAr9tt4+T4LZ8Y1zk6YzdaJvMbJUYVHDrt45HDjsGhLAttTErs7JHpbBFqiEq0RgZBv8dd3M6k6GmN5veSGvBRAxC8QDZqNaEuaMCcRbtyYPj2mcHLUzHAJeATaYgLxkEDIJ+BfZgutwbTCseH5A73Xk3xFI78O7mPN0TgzrnFmXJmAoa4XbTXnKGAkqzCSXfz6ynVgsrj4Z1S5rnF8xMXJUaAlLJGMCLRGGkNaIiJaW+s2kLhUo6Oj+Lmf+zm4rov/9J/+E66++urz/s7TTz8NAHj/+9+Pz33uc/D5fACAkydP4s4778Rjjz2Gj3/84/jzP//zBX+/Wq2iWq02nObz+Wauh4iIiIiIaKNIhCRetkvMHPm9VkdRb1VCCGxPCWxPNVZVpAsKRwYVMkWNugvUHHOk+YkR8zVXNGAGJ+/vlut+gPJqUxrIljWy5caFKwAkp2aITB+xP61c1zg9rnF63PzssQSCXjPPoV5WqEsFTM2HqDmYai9kqpHowpVqa/e8KT1bVXIYZpaL3wP4vCagmG5pRUREq29TBhKDg4N4/etfj6NHj+KGG27AX/zFXyzr9374wx/i5MmT2L9/PyzLmjl9x44d+MpXvoLrrrsOX/rSl3Dvvfcu2Lrp7rvvxic/+cmG0+666y587GMfu7QHtAlMTk42+y7QFsR1R83AdUfNwrVHzcB1tzVEbaAvpnFowEXFaf7Gaz63tdadDeDy9vmnKwWM5ARGchKTBYGJokB/WuLxY8Djx1wAdbRFFXa3K3QnFPweDZ8NeG3z3WcDUmpUHYFq3bQPCnqBkM+cv5kLLbJZ4OTA8i47MfU9n5tEZKT5FQa08o4BiAcFdndYCPoEtNYoVExg4jhmnonSJqCyLSASWHxY+Grg37XUDFx3dDGWOwh90wUSL7zwAt70pjfh9OnTuPHGG/Gd73wHwWBwWb8bDAZx4MCBBc+75ppr0NPTg/7+frz44osLDr7+nd/5HXzkIx9pOI0VErOWuyiJVhLXHTUD1x01C9ceNQPX3daQTALdnRovnW0chtss0RjXHQDEE8C+c07Llkx4dHhA4aUB4MdHL/zIb49lqghaImaocm+LRG+LRGdSLDqQeCnlmsbwpMb21MYeUsx1t3kpAMfSQDQgUKhoOAt9zE3lUWMVYGe7hW2ta1dVwb9rqRm47mi1bKpA4v7778fP//zPI5fL4Q1veAP+7//9vwiHwyt2/R0dHejv70epVFrwfIYPRERERES0WXksgau2W8iVJManhsrmK82vmKBGsaDATXts3LTHDCofzmiMZDWqdY1qHea7A1Rq5qhvn0fA5zGtjIpVzLQgShc0jg4pHDwLTO/ECgCRgJnDEAsKBLym7U3QJ5AIiZnTLWkqLLJFjUePuHj6pIu6C2xPCbzrVV70tLA9Dq0/SgOZZbTfUho4NuxiIq+wp9OCLQEpMfV94wZuRERrZdMEEn//93+P973vfXAcBx/4wAfwV3/1V7Dt5T+8wcFB/P7v/z6q1Sq++tWvLniZEydOAAB6enpW5D4TERERERFtNNGgQDRoYWe7aWWSLWrkyhqjOb2sAdi2BJIRCSmA0ezGmk0R8gnUplq4zGUJLDqYt5mEEOhMCHQmLu73tdaYLGqcnTBDiIcmzSyLySLQP778125Hm0RXQuDHR1zc889VvOaAhZ4WCdsCLGna4NgS8HsEtqUELG7qrlsjGYWjQwr7uyVao1s7WJosajx+zJn52e8RuKLXQjTI9UtEtJRNEUh84xvfwHvf+164ros/+IM/wCc+8YkLvo5oNIq/+7u/Q7VaxW//9m/jmmuuaTj/W9/6FiYmJrB3717s2LFjhe45ERERERHRxuWxTEuf1iiws90MYB6Y0JgoNG5WWwJojUp0xM2R9NNHEe/ukDg7oTCW03BcwHH1JW/sSwG0hCVCfmAgreeFBxcjERLoS0kkwhJaa+TLpjWSz2NavPi9AgNpM2R6JW5vvRBCIBkWSIaBq7ZbDedpbYZsl2umqmKyqDFZMOGU0oDWgG0B1/RZ6EyYjetXXa7wvx6u4cEXXMz0vzlHS0TgdVfauGWfBa/Njd215LjTbdk0MlNBo9c2FTCWBJ4/7aJ/wqxvrw287WUe3Hq5BbmZB45cgEpd46cnHezusFgFRAuq1jXGchpjOdMTLOgTCHoFlDZVa5W6RltMznxmEm1WGz6QGBkZwS/90i/BdV383u/93rLCiFKphDNnzgAA9u/fDwAIh8N417vehb/+67/Ghz70IXz7299GKpUCABw9ehS/9mu/BgD4/d///VV6JERERERERBtbMiyRDANKSeQrQL6sIQXQFhOwF5g94LUFdrabaotpSpmWQtW62SC1LMCWAuWaaSF0LksAYb9AOGDaBbVEZucc9LRonBxVGEwrnBsTCACtEQkpzYDoutIoVmarHzyWQFtMoDMuG454FkIgGsS8o6C7kxKtEYGjwwrjuYWrBwRMiyS/xzw3Gzm7EELAa5uN6VhQoGsZrca3tUp87K0+HB9RqNYAR5nX2FGA6wITBY1HDjn4+o/r+OZTdezrsrC9zYRBfW3youZXNFOupPH8GRfPn3GRKWrEggLxkEAkYNpdBbxm7bZOzepoVgBTdzR+fMTFd59xMFlcfFH6PcDNey3sapf4t6cd/J8f1/HTEy6SYYHBSYWRjDbhXZvEjjaJZFgg5DeVRanoxp4fslxKA0eGXIxkFHxeMVX5A3QmJHye9ff4ldJb4nVplnzZhA+lGlCqahTOaXO40PttouBiaFJhb5e1psPTidbShg8kPvOZzyCdTsO2bRw/fhzvfve7F7zcK17xCvzKr/wKAODxxx/HbbfdBsAc1THt05/+NJ588kk8+eST2LNnD17xilfAdV384Ac/QLVaxW/91m/hXe961+o/KCIiIiIiog1MSoFY0GxUX8zvBrxAwAuYLXwjEjBH6z/lSJSnqiA6EgIt4cU3Or22wL4uC70tphJjOKPgKCAWENjTuXBrlWpdo1wDooEL7wfvm2rZ4rgSk0Uzh8GWs4FJwDN7nXVXY2BCoX9iZao4NgpLCuzttBY9//ZrbTx62MXDhxw8fdLFT0+a0302sK9b4opeC3u7JFJRAbEOj8x3lcazpxR+eNDB0SEThElhAqyzE0u3uYqHBLa1CmxvlehISPimAh8pgZpjvqp1PfPnYlVjJKMxnFFIFzSUMq3DLAGkpsK0zoTAzg6JvpRsCDwKFY0jgwqHBkxgki0BYT/wlhts7GyXiIcEogGBums2Uit1oCsh4Jm6jut3Wvjnx+t4+CV35r7v6pCYyGs8fszF48caK2CSYYFXXmbhFftsRALr73VbadmyBsqzL/bJUYXWiER7XMDvEfBMvbbnticrVU11SjwkEPTNnleta0zkNaJBseQmdc3RSwZb6YJC/7hCvmLCQKWBvpTEzvbF35NzKaWRLWsUymYNSWle23jo4gbeb2YjGYWXBtyLakuYKWk8ccxBJGDeh9GgCS4XCvaJNqINH0j827/9GwDAcRx87WtfW/Ky04HEYhKJBH784x/j05/+NL7+9a/jgQcegN/vxy233IIPf/jDuPPOO1fsfhMREREREdGFsS2BPV0W4nH7gsKCoE9gb5eFne0SubJGMrx4O4zpIc+Xej9TUYFUdPHLeCyBvjYLva0aJ0YUzk7Mr+LYiry2wKsP2Hj1ARvlmpldcWJE4WC/wvNnFJ47bapkIgEzmyLkE5DCDNEO+83GaDwk0B5b26PysyVT3fHwSw6yJcCSwDU7JK7us3Cgx0LIL6C0qcLJlzXKNRN85cpmo3k8pzE0qfDCnMe4XMmwQE+LhMcy4YejgJGMxhPHZ0MBWwKdCYFq3dxmpT77+60RgZ+7ycIrL7PnHcXvBxYMEPxegXe80ovbr51t6zStUNE4M6aQLWsUK2aD/cnjLv71CQfffsrBdTstvPqAjb6UCZUKFVP9NDRp2reN5zRqrgnzpmeMWNJ8BX0C8eDs6xwPCsRCApEA1nXrKA1gLK8wlm883e8RiPjNZ06m1HgEfcRvKr5ODzqAx5n5fAj6BNqi5rFOh1PlmkalZqqugj4TRnXEBbQ2wVWxCgxn1Lwj9AHg1JhCsQJc1iOX3PAeySqcGFYo1xuvYyBtouOupMTuDtkQsuRKGumCmqoIEpDSrI9iRcOSQHfLxqt8Wo7jwy5Oj1/Y+/hcGua9mitrID31d0ZKoju5NaqNaHMTem6JANEqSafTSCaXUcNLtIK47qgZuO6oWbj2qBm47qgZNuu6yxQ1Xjrrztvso1nFisbhQYVjwy6OjyicndBYakfDYwEdcYHOhERXUqArIbGrQyLgbdzMc5WZYSKmgg1bYl71RS6bRjTWuO60NmHSQwddPH3ShatMpcCrpioBLma4cc3RGEibNi/1qc1mV5mj3r22CcymW2UFvKat2GJHxFdqGgNphWPD5msgrRD0mqOtY0GBXe1yzYZTO67GM6dc/PCgi2PDZqO2t1VAAOgf1w1hXNhvHuf06+Iq8+W4WPRocykw1RLLbPK7ylzW7zWtwra1SvS0mBk267GyZikLrb3VEPIJ9LbIhvZhNcfMhxmYUMiUzv/ZFPQJHOix4PMAx4cVhjJLb8rb0gQZnQkTLq4HNceEYkqbEMy2sGQl3lzZksbxYXdZz9XF8tkCnQmBtphc1ZZOm/XvWlofGEjQmuAHGTUD1x01A9cdNQvXHjUD1x01w2Zed64yR82bjVeNXEljNDd71HTQK5AIm4qAfKVx5sVWVHfNhrXWZvM5X54drj2S1RhMKwxOmiP0p1kSuKxb4todFsp14KWzLo4OKdSc2ev12UAyYtqBdSUl9nZKpAJZpFqTUEqjVAOePeXioRcdnE2b697XJXHr5Tau2i7nteGhRmcnTEurx4+58FjAvi4L+7sltqVMK65zA6O5yjXzemZL5numaF7zuT9XndmKilLVhBnTfB6gPSbQMdXOqiMusT1lWlStV2sVSEwTAKIBAVdjwYqK85FitlLnQtgSCE+3KJqaCeSxgGLVvLeVxrwArlwzFUbVunndXQUEvSYY8XuAumu+XGUCSp/HVNJkigrpoka+pBHwCcQCpkVWumDan537qD2WmFkvUkwFZEpj+oIawMCExlj+0qoiLlTQZ0K2sN98WRIz1VfxoLioUHTaZv67lpqPgQStCX6QUTNw3VEzcN1Rs3DtUTNw3VEzbMV1V65pCJg2OefKFM0MgdGsuuANwK2iVDXtkM5OaDx32sXhwdlZDpYEdrZLtIQFlDbhRr6ikc6bGSDTz6kUGn6vQLk6swcJnwe4eY+FWy+30ZlY/SqDzcZVGkKsbquluqsxlDatvwbSCsNTMzeypdnLSAG86jILP3udZ13Ot1jrQGI9EUBDOCCn5gdFAsB4fqqdES1ICmB3h4Welov7bNqKf9fS2tnwMySIiIiIiIiINrOljhg3vfQt7OmUyJVMr/hCxRwxXqxysw4wRxHv6rCwqwN49QEbhYrGi/0ugl6BPV0Sfs/Cz69Spn3SkSGFl85UUNcehP3m+npbJG7cbS352tDS1qKSxGMJbEsJbEs1bsqWpgaCD2UUHjnk4qGDLn5y1MWrD9jY3yWxvW12XShtBoZzoPDaO/cTTOmFZ3HQfEoDR4ZM+6hd7Y2t6uquaUvlKjOsnjMpaK2xQoLWBJNVagauO2oGrjtqFq49agauO2oGrrvlK1VNO5NKXSPkMy1JyjWN4yMKNadxK8DvMQN0U1EBn0cgUzQVApmi3tJtoaZt5aPUNzutNZ45pfAvj9cxljNrXQggERJTw6LNxnjYj8aB2iHzfrlym7Vg9dJK4dqjleC1TSssAEgXZqvEfLbAtlYzO6TmAHXHDL0/MzgB4U1Aw8xhmQ4uqnUzmyZb0uhKSrRFN95cFmo+VkgQERERERERbULBqRBirnhIoDUqcGLY9EpPxQTaonJer/GQT6A7aaoE0lMzGXIlDZ/XzLJwFTCSXbpHVDIsoBRQqQPV+vy+7ETrgRAC1+6wcNV2iVOjCidGFU6OKEwUNFJRiYDXVHNkS2ZexfBAY3s0n13HdTstvHK/jR3tbN1F61PN0RjPz/8UrjoaR4ddHB1uPD2XU4jGzOWPDLk4PWZmUkzkZ8OMyaKLE1NhdqVuQvCaoxEOmLkcYb+ABqAU4LWB1ijfH2QwkCAiIiIiIiLaQjyWwL5ua1mXldIEGK3R+ed1JyWODLnzBt8KAHu7LHQnZzefXGXaSKUL5utihuUSrSZLTrf2Wvq9obVGoQJkShonRxQePeLg0SMuHj3i4oZdFn7uZg9ilzBMmDYvpTWODSk8fdJFzQH8XlOd1ho1beA6E2JNWpldjKqjZyqI5irXNc6mG0+fHjB/rlREY1+3bBhMTlsTAwkiIiIiIiIiumDxkMCNuyyMZjXG8hqTBbMBdaBXIhluPBLWkgLJsEAybH4uVDRGswrjeQ3bAgIeASmBocnZo2+J1iMhBCIBIBIwm8i3Xm7j7ITCN5+q48njLl7sd/Gm6z3Y0ynRGhGr2s6J1r9CRePYsMLRIRdPn3SRKS5+WY9lPj//P9d40JfafNUEY3mFzFGNne0SidD8Cj7aOhhIEBEREREREdFFEUKgPS7QHjdHjtddLOvo17BfIOy3sLO98fSuhMSL/S5Ktc2TSrSEBSIBgcFJPW92x0YQDQh0JkzrIlua4GiyoDGcUciz0gUA0NMi8cuv9+LZUwr/59Ea/uHR+sx5YT/QEhFoiZiAojVijoiPBc2MilwZKJQ1cmWNfNlUX1TqGjUHcBWwJ2XjtddoRALrZ/PWVRpnJzR8HqAjvvk2zi+V1hov9it846k6+sdn3yMtYYE3XGPhxl0WkuHZNkcjWY0z46ZV2DOnFJ45VcX+bonbrrBxeY9ct1UTF6PuahwedAGYar1YcPYrGjDzW6anHZ87bFtrDUeZ36ONjUOtaU1w8Bw1A9cdNQPXHTUL1x41A9cdNQPX3ebnKo0TUz38y9X1MXviYgYLeyyBvZ0S7VMbtkqZjcfRrEK5DlRrGhc6L9xriwVDDSmAiN8EH36vQLqgMFm4sOcuGRboSkhoDVQdwHHNDIWlNsJNz3izUag0UHeB6tTMkMmiRrG6Hl69tVWuaTx32sVYTmM8ZwbLj+cVsqXlX4fHAnweE0iUa4BtATfssvCaAza2tc4PAGqOxuFBhYP9Lso1s6GrYaqYuhICHXEJx9VITw2rn5xqnZYtauzrtvDmG+wlg0SlTFueI4MujgwqHBtWqExlLn1tAi/fZ+P6nRYCrAbBQFrhHx+r49CAgtcGrt5uYVeHxO4O05LpfAOgB9MK33nWwZPHXWgNhHzAdTst3LLPXtOqifUwTN0S5jPPtqY/W8xnWmtEoi81f/aRUhoTBfO+iwYFOuLrtwXWVsdAgtYE/9NAzcB1R83AdUfNwrVHzcB1R83Adbe1OK5GvqJRrADFqkaxYo4iv9CN/Et1IZtzUgCdCYkdbefvle7MeSB111QeTG/kSwFICfhs0+6qJSLgtQWU0ijVgLqj4bEFPJYZGHvuRmfdnZrXUdYoVoF8WaN6TphhS6AlIrGtdeng4WKVqqbv/ERBIVfSy2rH1ZOUiAUFRrPm9zZLC6+6YzZLTUBhwoCAz1SgRPwCkeDU9wBmNlFdpfHowRx+csKP4yNmkvbOdolXXWZBCGB4UmMgrXB4UKHmXNj9sS0g6AVyZaAzIfCfbvOiOylwNq1x6KyL8bx53xUqGv0TCuWa+T0pgB1tEns6JTIljZ+eMPMQpAC2tQrs6bTQ2yqmqqAEMkWNF/tdHDyrkC9rdCcleloEtrWa62iJnH+Tfi3UHA2PNf99dCF+ctTBVx+qQ2vgpr0W3nKDB/HQxV3fRF7hiWMunjjuYmjSvAn2dUm88Robe7vkqj9n6yGQOJ9EyHwmagBambku9TmfqR7LhBLRqaA24F1eBR+tPgYStCb4nwZqBq47agauO2oWrj1qBq47agauO9JaI18GsiWNgUmF0pyj8AXMBnvdMS1wps+xhOn5X6yiYcNquZazOWcJoD1ujtxdr3MDyjUzXLxY0YiHBBIhMa8tympxlRl0W6gAlZpGqWZ+nvtqtMckDvTODpV2XHP5qqNRrcMEUhWNUnV54cZmML32zowr/OBFc+S8486eLwSws03iqj6Jq7ZZSIQFpveq03mNwUnTXstnCyTC07NcBMJ+U0nx3WcdfPMpB1IAQZ8JKOYKeIH2mMDeLgt7OyV2dkj4PbNrplzTeOqEixfPuDg6rFCqLvw4pm93ID0bbkyfvqdTYm/nwgGF42ocGVQYzmi0x00VTzx0acFBpWbmOhwbVjg7oTCQNhUstmU2uaffGwt9DwcAucBtP/BcHf/4EwfxkMAvv96L7StYzdA/ofDAc+a1VxroiAsc6LVwoFciHhKYyJugSwigMy7QkZAI+y/tfb0RAomL0RoxYfGFBLCFiqkq8nqAVGTtPjM3MwYStCb4nwZqBq47agauO2oWrj1qBq47agauOzrXeM5sKEYCAt1JCd/UZqnjms13r202X4UQcJXGSEZjaNK0TZreVqo589sbBX1ipq3SYptzQZ/ZII0FBSL++T3PaWmFisbxYRcTBY2WsMCV26xlPYdKaVTqpqqk7pi+8o5rWhy5U0mF0qbd0WRBwVGr/UhWz7lrL1/WePaUi4AX6EhItEUFPJd41PepUYWvPVKD4wKX91i4vEeip0Ui6MMFtbxRWmMgrTGaUShUTYDk9whc1iPRETdBg9amcufkqMLRIYUjQwojmdl3XzQAtMUkWqMCrgu8MNWGai6vbcKTgEfA6wGUAhxlXn8pZr+EnPuzCWpqjpl/MR1o2ZapEGmPSZRrpkJpsqDn3eY0WwKxOQFFPCRQrGg8esRFR1zg/3u7F8nw6rRWGs8pPPC8g6dPuvOCo3O1xQRedZmNW/ZaFzU8erMGEtNaIxIhn2ltpjVgSVM94bHNZ0mpairV8mXMq7roTAikonJq5gU/8y8GAwlaE/xPAzUD1x01A9cdNQvXHjUD1x01A9cdrQbHNa2gMlMBRjIsEPAKVGoaR4cUjp8db9ic81gCO9okupPro93MRjdZUIgGV6ffu1JTVSFVU00ghdlwzJY1ciWNSn19b4tt9o1hwFQ7HR1ycXRI4cy4wnjOvF6AaQN1zQ4LfSmJ0awJE8dypsKmXANqdbOZbFmmSklrwNXmu1ImmFJam5+1ef372iT2dlrY0yXRlVh43VXqpq3WZNFU8jR8n2mtNnv5HW0Cv/IG3yVXJizHdPAzPTOkNSKQjAgoBQxnFAYnNZ4/7aJYNfNIrt1hYX+3xP5ua9ktpLbCurtUtgTiIYmrtlvnvzA1sJt9B4iIiIiIiIiImsm2TDubRLjxdL9X4MrtFvzaghUwMyG8NpAIC3gsBhErJbFKR5QDpmplode2d+p7qaoxklUYyeqG9l+0dmJBgRt22bhh1+xppaqG46JhcPH+7rW7T36PgD8u0B5f/DI1x4QUxQrQ2ypgr9FnghQCvS0CvS3z3zdXbDOb43XHtNJ66KCDx4+5ePyYC6CO1ojAtpTEthbzvbdFIrQGIcpm5ChgPK8AMJC4UAwkiIiIiIiIiIiW0BKVSCa56bQZBX0CO9os7GgzswWqDlCtm4qZgfT8dk+2xIZuAbVRXEybobXmtQXaYgKINfuezOexBW7ea+PmvTYyRY0jQy4ODyicGFF4+oSLn56YvWxLWKAzaYY/RwICfg9QKtvweOuwpZmN05kwVWP94wqnxxQmixqtUYGOuGkbFvKbodGXOhictgYGEkREREREREREtOX5vQJ+LwAItMWAbSmJ/nGFTFEjGRZoi0kEfQI1xwwGnyxqnBlXW2bANm1M8ZDAy3bbeNlu83OlpnF2QuHMuFm//eMKB/vPXcceAM4F35ZnaiZHT4tEZ1zA6xGwJOCzgd2dFmJBhhXEQIKIiIiIiIiIiGgejyWws31+ZYzXFvBOtYFqj0scHnSRKTKVoI3B7xXY3Wlhd+fsaVqbmRz5shkYXy5mEY3GUHPMXIqhSTO3o6dFYntKoiUsMJ7TGM4ojOc1yjWNUhXIlTUG0wpnxt0FbrmO7qTAgV4LN+2x0JlYvVZttL4xkCAiIiIiIiIiIroIIZ/AdTtsDE0qHBtWqLsMJqiRwOzcGaXNoPWaA1TqQN3VkMKso5BfYDw3v03YmtxHIRD0zbbKynk1ojETGPS1LRwcRAICO9oXPi9b0hjNKjgu4CqgUNE4NKBw8KyL7z7r4LvPOtjVLvHyfZZ5bmwT9IV9ApEA1mweBzUHAwkiIiIiIiIiIqJL0JmQaIkIHB1SGMlyyAQBYb9Ad1IiFRXw2gtvsDuuhiVn5y5kSxLPnXY3fLAVCwrEgo3VRTfvBZTWODGs8KNDLn560sXxkYXfKyGfeU9taxXobZXY3728dk/ZkmlD1ZOUSIQZaqxXDCSIiIiIiIiIiIgukdc27Wi2pySqdQ3HBeouUKxq81XBsjaaI36BRNgMGD6uJWoCFz2nwmMJdMQFyjVgIq+wEtvcAsD+bgu2BRwaYFXIuUyrL4muhDjvgOdzKwFiQYFr+iw8c2rjhxILkWK6XZSFn79F4+BZF5WaeZ/U6hr5ika+DGSKZs7FsWEAcCFQx852iau2S4T8Ao5rhsuXqmaeS74CnB5TmMjrqdsBrt1h4bVX2ItWcVDzMJAgIiIiIiIiIiJaIWG/QNi/8Ea0q0yP/lpdw1WAhgkbvFMta3x24ya1p9tCOGpjMK0wkNaoOgtvUnttgVjQDBB2FQANtEQE2uMCljTXV61LjGQ1ao6GLc3tTBY1xnPLDyosARzotdAalTOP9cV+F7ny8jfP/R4BKc1m8maTikjs75HwXELLoUhA4NodFjJFjYAXCHgFJvIaJ0fdprRzWi1hvxm2vRilNcZzGidHFZ477eJgv1q0ogIA2mICt+y10N0i8cIZF0+dMF/tMYGrtlu4arvEjjYJKVk50WwMJIiIiIiIiIiIiNaAJQVCPjMzYLm8tkBfm4XtKY2xnMbQpIIUAgEfEPQKxEJiWdfn8whsa228XE+LCSoGJ02//6BPIOg1QUmlBlTqGnUXUApwNdDbIhta5wS8AtftsDBR0MiVNLJljXLVhCyu0g2VHWG/wPZWibaYmaVwdEhhcLK5O+xSzAZBkYAJdQJegROjLtIF3XC51qjEWHbx8CYREjjQuzIb3ueGWkGfQFtM4MiQuqAAaSOTwjzmtpjETXts1ByNEyMKrgIsab6CPjE1fwMNIdBrrzAh3kMHHTx7ysX9zzm4/zkg7Aeu3Gbhyu0W2mMmwJPCVGQMTJr3ltcW6EoIdCUkOhKLt9uii8dAgoiIiIiIiIiIaJ0TczZoV5LPI7CjzTr/BRchpUAqKpCKLv93LGHaPiVCZpN9qfZEIZ9AMmyqBEq1+ZcTMMGIxzLtseZWEYR8AkGfQLZkKkMAs3HdnTTzHXyehTebr+kzg8qPjyi0RgT6UhJ+r8BkQuCF/vn3N+IXuHKbtapH3/s85jYAC45rWoKVahqTBY3Jokahoi+6tddG4LUF9ncvf512JSXe8UovfvEVGmfGNJ477eK50y4ePWK+lkMAaI2agCIRFvB5ZsOr1ohAa1TAcTWHcF8gBhJERERERERERES05trjZujzWE5jcFJhsqghAAhhjn7fnpJonwpg9nQChYpGpqjhsU2bK58t4PegIQgoVjWqdY2wv/Ho9lzJbNqnYmJZLZU6ExId8cY5EImwxPU7BZ477aJS1/BYplLlil5rTTelbUvAtgC/VyAZNqdprVGumcc/ktUY5XB1AKbSoq9NoK9N4i03ejCeU3ihXyFfNm3TXAVEg0BXQqIzIVB3gMGpaonBtFmXz59Ri4Y9X7i/hvt/P7K2D2qDYyBBRERERERERERETSGlmXXRHj9/5cdS8zmmhXwLt7CKBgWiwQsLDRYaSh30Cdy0xzrvwOq1JoRA0GfuXyoKpBMCRwYVyjWNeEigPSbhsYGBtGpoR7XVtEYlXnNg6bXWHjdDsafVXTOUvuaYGTC5kmmfNpFX2NN58dVFWxUDCSIiIiIiIiIiIqJlWm9hxEKSYYmX7Raou2hoTZWKSpSqGsMZhYm8Rr6ydcOJ5fJYAvEQYJo4NXrtFZ41vz8bHQMJIiIiIiIiIiIiok1GSgHfAsUAQZ/AznYLO9uBal0jXzbzJ6bbEuXDFlIpG1pr5CtArqxRq2sEvAIhv5nXUaiYMKNYwZIzQIjOxUCCiIiIiIiIiIiIaAvyecS84d5eJREPCQACifD5r6PmaBSrGpUaUHNMQKGUCT5CflNh0D+uMJxRYHRBDCSIiIiIiIiIiIiI6KJ47akB4qHFL3NZj4W+Non+cTO8vFTVDCe2KAYSRERERERERERERLSqAl6BvV1mCLTjauTKGuM5jfG8RqW+cDwhxWwrKdocGEgQERERERERERER0ZqxLYFkWCAZBvYCyJc1To0qjOUVABNE9LZIbE9JjGQ1ToyomVkVAkAkICAl4LqAq4G6w1kWGwUDCSIiIiIiIiIiIiJqmkhA4MrtFvJlicFJhe6kRNhvZlt0JwVSUTOHIuAVaI1OtYg6h1IalTowOKlwdkKxsmKdYiBBRERERERERERERE0XCQjsC1jzTvfaArs65p8+l5QCQR+wu8NCb4vE6TEFVwF+L+D3CAxlFDLFxVMKKQCtwdkWq4yBBBERERERERERERFtGj7P7LyKaZ0JiaFJhWPDqqG9ky2BnhaJnhaJugv0jysMZxausJiuzKg5jC0uFgMJIiIiIiIiIiIiItr0OhMSrVGBchVwlYargFhIwGOZoMFrA/u7LexslxjJaIzlFbJFDa8tsD0l0ZUwlxvKaPSPq2Y+lA2LgQQRERERERERERERbQkeS8ATBMx47IV5bYHeVoHeVomao2FL0xJqWndSzIQTdGEYSBARERERERERERERLWChAdoAIAQDiYshm30HiIiIiIiIiIiIiIho82MgQUREREREREREREREq46BBBERERERERERERERrToGEkREREREREREREREtOoYSBARERERERERERER0apjIEGrrlqt4rOf/Syq1Wqz7wptIVx31Axcd9QsXHvUDFx31Axcd9QMXHfULFx71Axcd7TahNZaN/tO0OaWy+UQi8WQzWYRjUabfXdoi+C6o2bguqNm4dqjZuC6o2bguqNm4LqjZuHao2bguqPVxgoJIiIiIiIiIiIiIiJadQwkiIiIiIiIiIiIiIho1TGQICIiIiIiIiIiIiKiVcdAgladz+fDJz7xCfh8vmbfFdpCuO6oGbjuqFm49qgZuO6oGbjuqBm47qhZuPaoGbjuaLVxqDUREREREREREREREa06VkgQEREREREREREREdGqYyBBRERERERERERERESrjoEEERERERERERERERGtOgYS1EAphS984Qu45ZZbEI1G4ff7sW/fPtx1113IZDLzLn/ixAm8973vxfbt2xEIBHDZZZfh7rvvRr1eX9btfeADH4AQAo7jLHqZZ555BnfeeSe6uroQDAZx7bXX4q/+6q/A8Seby3pcewCgtcbrXvc69PT0XMzDonVsPa65f/iHf8BrX/taJBIJeL1e7NixA7/6q7+KgYGBi32YtA6tx7X3j//4j7j11lsRiUQQDodxww034LOf/Sxc173Yh0nrzHpcd3NprfHGN74RQgh873vfu5CHRuvYelt3Tz/9NIQQi37dfPPNl/JwaZ1Yb+sOANLpND760Y9iz5498Pv9aGlpwdve9jY8/fTTF/swaR1aT2uvr69vyc+76a+vfOUrl/ioqdnW07qbdv/99+P1r3894vE4fD4f9uzZg9/+7d/G5OTkxT5M2mw00RTXdfWdd96pAehgMKhf85rX6DvuuEOnUikNQO/evVsPDw/PXP6FF17QiURCA9A33XSTfvvb3z5z2Z/5mZ/R9Xp9ydv7n//zf2oAGsCil33wwQe1z+fTUkr96le/Wr/1rW/VkUhEA9Dve9/7VvLhUxOtx7U37SMf+YgGoLu7u1fksdL6sB7X3G/8xm9oANrj8ehXvOIV+i1veYvu6enRAHQqldIHDx5c0eeAmmM9rr17771XA9C2bevbbrtNv+lNb9LJZFID0K9//et1rVZb0eeA1t56XHfn+uxnPzvzO/fff/8lPV5aH9bjuvvSl76kAegbbrhBv+td75r39YlPfGIlnwJqgvW47k6dOqX7+vo0AN3X16fvvPNOfeDAAQ1A+/1+/eSTT67oc0DNsd7W3m/+5m8u+Dn3rne9S7/uda+b+X/HE088seLPBa2d9bbutNb685//vBZCaCGEfuUrX6nf+ta36o6ODg1A79q1S4+MjKzoc0AbEwMJmjH9D/R9+/bpkydPzpyey+X0m9/8Zg1A/8Iv/MLM6dddd50GoL/whS/MnJbNZvWtt96qAejPfOYzC96O4zj6rrvumvkQW+yDrFKp6M7OTm1Zlv72t789c/rg4KDev3+/BqD/6Z/+aQUeOTXbelt7WmtdLBb1+973vpnLMZDYXNbbmvve9743Ezw888wzM6dXq1X9y7/8yxqAvvHGG1fgkVOzrbe198ILL2gppY5Go/rZZ5+dOT2dTs/c9uc+97kVeOTUTOtt3Z3r4MGDOhAIMJDYZNbjuvu1X/s1DUB/8//P3n3Ht1WdfQD/3SvJQ97yjO3snTgkBEjIAAJlb1rKhra0jBYoLX0LhZeWFkppC7xllFVaNi0FEvYIOwmE7L3jxHYc770ka9x73j+uroYt2XLsa8n27/v58MGxZFmSj+695zzneZ733x+YF0kxJxbH3cknnywAiJtuukl4PB7f9//0pz8JAGL27Nn9e9EUE2Jx7IWiKIpYsmSJACCeeuqpvr9QiimxNu7q6upEYmKiiI+PF19++aXv+x0dHeLMM88UAMRPf/rT/r9wGvIYkCCfRYsWhb1Ar6urE5IkCYvFIux2u/j8888FALFgwYJu9y0uLhaSJIkxY8YIVVWDblu5cqU49thjBQAxYcKEHg9k//rXvwQAcdlll3W77YsvvhAAxAknnNCPV0yxItbG3ttvvy0mT54cdF8GJIaXWBtzV155pQAg/v73v3e7zel0+narFxcX9+NVUyyItbF31113CQAhdwX/97//FQDEeeedd+QvmGJCrI27QC6XS8ydO1dkZWWJGTNmMCAxjMTiuFuwYIEAIKqqqgbmRVLMibVx9/XXX/s2lnR9HCGEmDNnjpg4caKoq6vrx6umWBBrYy+c+++/XwAQ559/ft9fJMWcWBt3S5cuFQDEWWed1e221atXCwBixowZ/XjFNFywhwT5ZGRkYNq0aSFrp2ZlZSEjIwNutxv19fX44IMPAAAXXHBBt/tOnDgRRx11FA4dOoTt27cH3Xbeeedhw4YNuPLKK7F+/foen4/+Oy688MJut5100knIyMjA119/HbImHg0tsTT2mpubceGFF+LgwYO49dZb8f777/fz1VEsiqUxBwBWqxUzZ87E4sWLu90WFxeHcePGAQAqKysjfYkUo2Jt7N17773Yt28fbrnllm63tbe3AwDMZnPEr49iU6yNu0D33HMPNm3ahKeffhrZ2dl9fGUUy2Jt3Kmqim3btiE/Px95eXn9eGUUy2Jt3L355psAgF/84heQJKnb7Zs3b0ZxcTGysrIifo0Um2Jt7IVSXl6OP/7xj0hOTsZTTz3V55+n2BNr406WtWXmioqKbn1f6+rqAAA2my3yF0jDFgMS5PPee+9h9+7dyMzM7HbbgQMH0NjYiLi4OGRnZ2Pnzp0AgKKiopCPNWPGDADodiA755xz8O233+KVV17p9SDU0++QZRnTpk2DEMJ3Pxq6YmnsybKMK664Atu2bcMjjzyCxMTEI31ZFMNiacwBwD/+8Q/s2LEDs2fP7nZbW1sbdu/eDQBsrj4MxNrYM5lMmDx5crfns2fPHtx3330AgB/84AeRvTiKWbE27nRff/01/vKXv+Cqq67C9773vb68JBoCYm3c7d27Fx0dHZg0aRLuv/9+zJo1C1arFQUFBbj++utRUVFxJC+TYkysjbuNGzcCAObNm4empiY89dRTuOGGG7jxaRiKtbEXyu233w6Hw4G77roL+fn5ff55ij2xNu5OOOEEJCUlYdu2bbjhhhtQVlaGjo4OLF++HDfffDMkScJtt912JC+VhhlueaOI3HXXXQCAc889FwkJCb5duqNGjQp5f/37NTU1Qd9/9dVXI/6dR/o7aHgZ7LGXmprap3FKw080jnc9+eMf/wiHw4G5c+di/PjxA/KYFJtiYez94he/wNq1a7F27VpYrVY89thjIXdR0fARrXHX1taGa665Bvn5+Xj88cf7+rRpiIvGuNu0aRMAYOXKlVi7di1OOukkFBYWYsOGDXj22Wfx7rvv4osvvvAtyNDwE41xV1xcDAA4ePAgFi9eHPRYjz32GM444wy8+eabSE5OjvyF0JATC9d4u3fvxn//+19kZmbi5z//+RE/Dg0d0Rh3mZmZeP3113HNNdfg2WefxbPPPuu7raCgAMuXL8dpp53Wp9dBwxMzJKhXf/vb3/D666/DarXi/vvvBwB0dHQA0MqMhKLvKtfLPRyJwfgdFNuiNfZo5Iq1Mbd06VI89NBDkGUZDz300IA/PsWOWBl7zz33HNasWQMhBGRZxr59+2C32wfs8Sm2RHPc3XLLLSgtLcXzzz+P9PT0fj0WDS3RGnebN28GABx33HE4ePAgli9fjo8++gilpaW4/PLLUVNTg8svv7xbiQkaHqI17lpaWgAAl156KaZPn44NGzagtbUVX331FWbMmIHly5fj+uuvP+LHp9gXK9d4jzzyCIQQ+PnPf46kpKQBe1yKTdEcd3PmzMH3vvc9mM1mLFiwAOeccw5ycnJQUVGBBx98EI2Njf16fBoeGJCgHj3yyCO47bbbIEkS/vWvf2HatGkAtBIPAELWwQykquoR/+7B+B0Uu6I59mhkirUx98Ybb+Dyyy+Hqqp44IEHcPLJJw/o41PsiJWxJ4TArl270NHRgVWrVmHKlCn4+9//zgyJYSqa427p0qV48cUXcdNNN+HUU0894sehoSea4+6BBx7A/v37sXz58qBSJUlJSfjnP/+JgoICbNu2DStXrjzi30GxKZrjzul0AgBycnLw8ccf45hjjkFKSgpOOukkLF++HFarFf/5z3+wZ8+eI/4dFLti5RqvubkZL7/8MhITE3HzzTcPyGNS7IrmuDt48CDmzZuHt99+G6tXr8bq1avx/vvvo6SkBFdffTU+/fRTXHTRRUf8+DR8MCBBIQkhcPvtt+OXv/wlTCYTnn/+eVx22WW+2/WUUofDEfLn9e/3J/V0MH4HxZ5YGHs0ssTimHv00Udx2WWXwe1247777sPtt98+YI9NsSPWxp4kSSgsLITVasXixYvx6aefIi8vD5999hm++uqrAfkdFH3RHndVVVW44YYbMGXKFPz1r389osegoSfa4w4ALBYLJk2ahIyMjG63Wa1WnHLKKQCADRs2HPHvoNgSC+NO34l83XXXIT4+Pui2wsJCnHvuuQCAL7/88oh/B8WeWBh7gd5//304HA6cd955bCg8jMXCuLv77rtRUVGBv/71rzjuuON837darfjHP/6B8ePHY+XKlVi1atUR/w4aHhiQoG4cDgcuvvhiPPjgg0hMTMTSpUu7NbQsKCgAAFRXV4d8jKqqKgDha9NFYjB+B8WWWBl7NHLE2phTFAW33HILfvGLX0CSJDz11FO4++67+/24FHtibeyFkpGR4Vso0Wuv09AWC+Pu/vvvR0NDA9LS0nDdddfhqquu8v23e/duAMCf//xnXHXVVZysDhOxMO4ikZeXBwAsUzdMxMq4y87OBoCwfcDGjRsHAKivrz/i30GxJVbGXqC3334bAIIWp2l4iZVx98UXXwAAzjjjjG63JSQk+IL/ehlFGrkYkKAgra2t+M53voNly5YhOzsbX375ZchSDUVFRQCAXbt2hXycnTt3AgBmzZp1xM+lp9+hqir27NkDSZIwc+bMI/4dFDtiaezRyBBrY87tduPiiy/G3//+dyQlJeGtt97CjTfe2K/HpNgUS2Pv4YcfxmWXXYaSkpKQt+u7Od1u9xH/DooNsTLu9LrE69evx6uvvhr0X21tLQDg888/x6uvvooDBw4c0e+g2BEr4w4AbrvtNnz3u99FWVlZyNsPHjwIQNu1TkNbLI07/WcrKipC3q4vDObk5Bzx76DYEUtjT6eqKj755BMkJibi7LPP7vfjUeyJpXHX3NwMADCbzSFv17/vcrmO+HfQMCGIvFwul1i8eLEAICZOnCiKi4vD3nfFihUCgDjhhBO63VZcXCwkSRKjR48Wqqr2+DsBCADC7XZ3u+3FF18UAMTVV1/d7bbPP/9cABCLFi2K4JVRrIu1sReopKREABAFBQWRvRgaEmJxzF166aUCgMjOzhbr16/v2wuiISPWxt4FF1wgAIg//vGPIZ/rtGnTBADx6aefRvDqKFbF2rgL56STTuJ4G0ZibdwtXLhQABB/+9vfut1WVVUlUlJShMlkEocOHer9xVHMirVx98wzzwgAYsGCBd1uczgcYvTo0QKA2LVrVwSvjmJZrI093fbt2wUAsXDhwshfDA0ZsTbu5syZIwCIF198sdttbrdbTJ06ldd6JIQQggEJ8rnrrrsEAJGXlycOHz7c431VVfUdaB599FHf91taWsSJJ57Y7fvh9HQga2trE/n5+cJkMomlS5f6vl9VVSWmT58uAIh33nmnD6+QYlWsjb1ADEgMT7E25v7xj38IAMJqtYqtW7f2/QXRkBFrY++jjz7yjb1vv/3W93273S5+/OMfCwBi7ty5QlGUPrxKijWxNu7CYUBieIm1cffSSy8JACItLU1s2rTJ9/3W1lZx5plnCgDiJz/5SR9eIcWiWBt3ra2toqCgQAAQv/vd73wLfW63W1x//fUCgDj99NP7+CopFsXa2NM9//zzAoC4+eabI38xNGTE2rh7+umnBQCRm5srtmzZ4vu+0+kUP/3pTwUAUVRUxLkFCUkIIcIkT9AI0tDQgDFjxsBut2P27Nm+VK5QHn74YeTm5mLLli046aST0Nrairlz52L8+PFYtWoVamtrcc455+Dtt98Om6alkyQJgFYKItR9P/74Y5x//vnweDxYtGgRsrKy8MUXX6C1tRU33HADnn766f69cIq6WB17utLSUowfPx4FBQU4fPjwkb1IiimxNuY8Hg/Gjh2LyspKTJo0CfPnzw/7GHfeeSfL1A1hsTb2dL/+9a/x0EMPQZZlLFq0CGlpadiwYQOqq6sxfvx4fPHFF74a1zT0xOq4C2XJkiVYsWIFPv30U5x66qmRv0iKObE47oQQ+OEPf4iXXnoJZrMZixYtgs1mw8qVK9HQ0IDFixfj448/RlJSUv/fAIqKWBx3APDNN9/g7LPPRmtrKyZNmoSioiJs2bIFpaWlGDt2LFasWIGxY8f278VTVMXq2AOAe+65B/feey/uu+8+9qYbZmJx3Akh8IMf/AAvv/wyZFnG4sWLkZ6ejo0bN6KiogKjRo3CF198gWnTpvX/DaChLbrxEIoVS5cu9UU5e/tv//79vp/bu3evuPTSS0VWVpZITEwURUVF4sEHHxSdnZ0R/V5EENFft26dOOecc0R6erpITk4Wc+fOFf/85z8ZUR0mYnnsCcEMieEo1sbcxo0bI34+3DU8tMXa2Au0bNkysWTJEpGSkiLi4+PFtGnTxF133SWampr6+7IpymJ53HXFDInhI5bH3QsvvCAWLFggkpKSRGJiopg9e7Z46KGHhMvl6vfrpuiK5XFXUlIifvKTn4iCggIRFxcnxo0bJ2699VZRW1vb79dN0RfLY0/flf7444/3+3VSbInlcffqq6+Kk046SaSmpoq4uDgxceJEceutt4rq6up+v24aHpghQUREREREREREREREhpOj/QSIiIiIiIiIiIiIiGj4Y0CCiIiIiIiIiIiIiIgMx4AEEREREREREREREREZjgEJIiIiIiIiIiIiIiIyHAMSRERERERERERERERkOAYkiIiIiIiIiIiIiIjIcAxIEBERERERERERERGR4RiQICIiIiIiIiIiIiIiwzEgQUREREREREREREREhmNAgoiIiIiIiIiIiIiIDMeABBERERERERERERERGY4BCSIiIiIiIiIiIiIiMhwDEkREREREREREREREZDgGJIiIiIiIiIiIiIiIyHAMSBARERERERERERERkeEYkCAiIiIiIiIiIiIiIsMxIEFERERERERERERERIZjQIKIiIiIiIiIiIiIiAzHgAQRERERERERERERERmOAQkiIiIiIiIiIiIiIjIcAxJERERERERERERERGQ4BiSIiIiIiIiIiIiIiMhwDEgQEREREREREREREZHhGJAgIiIiIiIiIiIiIiLDMSBBRERERERERERERESGY0CCiIiIiIiIiIiIiIgMx4AEERERERFRlHk8nmg/BSIiIiIiwzEgQURERERE1E9fffUVJEmCJEkYN25c0G369yVJQmlpadBtzc3N+OUvf4m//OUvg/dkiYiIiIiihAEJIiIiIiKiKHjrrbcwZcoUPPLII3C73dF+OkREREREhjNH+wkQERERERENZ3fccYfv67S0NN/X77zzDurq6qLxlIiIiIiIooIBCSIiIiIiIgP9+c9/jvZTICIiIiKKCSzZREREREQ0Qnz11Ve46KKLkJ+fj7i4OCQmJmLKlCm46aabUFFR0e3+1dXVuOWWWzBu3DjEx8cjLy8P3/ve97B27dqQj19VVYWbbroJkyZNQkJCAsxmM/Ly8nD++efj22+/7Xb/gwcP4rrrrsOkSZOQmJiIuLg4FBYW4uKLLw77OwBgzZo1uPrqq33PKzc3FxdccAE+/fTTbvctLS319W84/vjjoSgKHnzwQUybNg0JCQkYN24c7rrrLjgcjm4/u2/fPlxzzTUYO3Ys4uPjfc/vsssuw+7du3t6q4OE6iEhSRJefPFF333+8Ic/QJIk/P73v8cVV1zhu/8tt9zS7fHeeecd3+2zZs2K+HkQEREREUWbJIQQ0X4SRERERERkrP/85z+48sorEe7yf8yYMfj6668xevRoAMCOHTtw6qmnoqamptt9ZVnGM888g5/85Ce+79XV1WHRokXYv39/yMc3mUz4+OOPceqppwIAdu/ejYULF6K5uTnk/ePi4vD+++/jtNNOC/r+n/70J/z2t7+Fqqohf+7WW2/FI4884vt3aWkpxo8fDwA49thjkZeXh/fff7/bz51//vl45513fP/ev38/Fi1aFLakktVqxbp16zBz5kwAWrDn5JNPBgCMHTs2qHm1JEm+r0tKSjBu3Lig7wW65557cMIJJ/jep7y8PFRUVECW/XvJrrjiCvznP/8BADz44IP4n//5n5CPRUREREQUa5ghQUREREQ0AvzqV7/yBSNOPvlk3HrrrbjuuuuQlZUFADh06BDuvvtuAIDH48HFF1/sC0aMHTsWP/3pT3H22WcDAFRVxc9+9jPs2LHD9/h//vOffcGI8ePH4+c//zluuOEGjBo1CgCgKAoeffRR3/3vu+8+XzBi2rRp+NnPfoaf//znmD59OgDA5XLhhhtuCAo8LFu2DP/7v//r+97cuXPxs5/9DCeeeKLvPo8++igefvjhkO/Bhg0b8P7772Px4sX4+c9/jgkTJvhue/fdd4OCKb/5zW98wYhZs2bhF7/4Ba699lqkp6cDAOx2O55++ule3vXw7rjjDhQVFfn+vWjRItxxxx1YvHgxTjnlFF8Qpbq6Gl999ZXvfp2dnXjvvfcAaEGeK6+88oifAxERERHRYGMPCSIiIiKiYa6zsxNVVVUAgNGjR+Pzzz/37dC//fbbcfPNN2P69OmYO3cuAOCtt97C3r17AQCTJ0/Gpk2bkJycDEDbkX/77bfD7Xbj0UcfxbPPPuu73+WXX459+/bhww8/RE5ODgDgyiuv9AUMDh486HtOgV+///77mDhxIgDA6XTiqquuQkZGBmbOnIn29nakpqYCAO68807fz9x444144oknfJkDDzzwAO666y4AwL333ovrrrvO93OBbrzxRjz11FMAgLvuugvjxo1DZ2cnAGDnzp2YPHkyAC2bQpIk1NbW4pNPPkFCQgIAYMmSJbjmmmu6vYa++vOf/4zq6mpfUOfUU0/F73//e9/tP/rRj/C73/0OAPDaa6/hlFNOAQB8+OGHaG9vBwCcfvrpvoAPEREREdFQwIAEEREREdEwl5CQgOnTp2P37t0oLy/H9OnTce655+KEE07A4sWL8fHHHwfd//PPP/d9ffXVV/uCEYC2UH777bcDQFDPhhtvvBE33nij79+NjY1Ys2YN3nrrLd/3Avs0zJ0719cnYsGCBTjvvPOwZMkSLF68GG+88Ua317Blyxbs27cPgFYu6a9//WtQGaPbb78dzzzzDMrKytDa2orPP/8cF110UbfHue2223xf5+bmYtq0adiyZQsAoK2tzXdbYPAD0DIVVq9ejbfffjvk6xloP/zhD/H73/8eqqpi2bJleOKJJ2CxWPD6668H3YeIiIiIaChhySYiIiIiohHg+eefR2ZmJgBg7969ePjhh3HhhRciOzsb8+bNwzPPPAOPxwNAK9+k+93vfhfUlDk7O9t3W1lZGex2u+/f69atww033IApU6YgMzMT55xzDv75z3/6bg8sv3Tvvfdizpw5ALT+E8899xyuueYaTJgwARMmTMBvf/tbNDQ0+O5fXFzs+3rSpElISUkJen0mkwmzZ88Oef9Aeo8MXeDjKIoSdNtnn33ma2o9atQofO9738OyZctCvp6BNnr0aJx++ukAgIaGBnz22WdwOBy+/hfp6ek4//zzDfv9RERERERGYIYEEREREdEIMH/+fBQXF+Pll1/G22+/jW+++QZOpxNCCKxfvx7r16/HBx98gHfeeSdoYT41NRWJiYlhH7e9vR1WqxWPPvoofvnLX0IIAavVigsuuACLFy/GmDFjcOmll3b7uaysLKxfvx7Lli3Dm2++ic8//xyNjY0AtMbPf/zjH/Hyyy9j48aNyMzMhMVi6fU1BjbsDtc0Wi+9pAvMsgh022234W9/+xsAbfH/0ksvxcKFC2EymXDzzTf3+lwGwo9//GNf9sprr72Gjo4OdHR0AAAuvfTSbq+FiIiIiCjWMSBBRERERDRCJCcn4/LLL8ctt9wCp9OJDRs24PPPP8f9998Pl8uF9957D2vXrkVBQYHvZ37zm98ElS9SVbXbIn5TUxPuuOMOCCEQFxeHnTt3Yty4cQCAXbt2hX0+ZrMZJ510Ei655BIIIbBz506sWrUKDzzwAMrLy1FWVoZnn30Wv/nNbzB27FjfzxUXF6Otra1bdsO2bdt8/9Z7QRyJ3bt3+4IReXl52LlzJ2w2GwCth8NgOf/885GVlYX6+nq8/fbbaGpq8t3Gck1ERERENBSxZBMRERER0TC3cuVKFBUVISkpCfPmzUNbWxvi4+OxaNEi/Pa3v8X48eN99y0vL8eSJUt8/37uuefQ2trq+/ff//53pKamYv78+b6my3v37oXT6QSgBRnS09N993/55Zd9X+sljpqamjB//nykpKQgPz8f27ZtgyRJKCoqwk9/+lOceeaZQc8HAGbPnu0LStjtdvzmN78Jyoh46KGHUFZWBgBIS0vzNYE+Elu3bvV9HR8f7wt8CCHwyiuvdHs9R8pkMvm+drvd3W6Pi4vD1VdfDQBobW3Fe++9BwCYMmUKjj/++H79biIiIiKiaGCGBBERERHRMHfssceiuroaLpcLJSUlmDVrFs466yxYLBZ888032Lt3LwAtmDB//nxkZWXh7rvvRnl5OYqLizF9+nScd955aGtrwxtvvAG3241169b5dumPGjXK97vsdjsWLFiA0047DRs3bsTq1at9t+lNoDMyMpCZmYn29nYAwAknnIALLrgAmZmZ2Lt3L5YvX+77mcWLFwPQSjDdfffduO666wAATz75JNauXYvjjz8eO3bswIoVK3w/84c//KFbj4m+CHw9ZWVlWLhwIRYsWIAVK1YEZWH0t6l1amqq7+vnnnsOzc3NmD17Nq6//nrf93/84x/7sjV0P/jBD/r1e4mIiIiIokUSgduKiIiIiIhoWFq9ejXOOOMMXxCgK0mS8Oijj+KWW24BoDWoPu2004KyIwJdeeWVeOmll3zlmy655BK88cYb3e5nsVhgsVhgt9shSRIaGhqQkZGB2tpaLFmyBLt37w77nC+++GK8/vrrQf0g7rzzTvzlL39BuGnMbbfdhocfftj379LS0qAMkK4/t2TJEl8w4/nnn8cPf/hDqKqKRYsWYc2aNd0ePzk5GXa7HaqqIjMzE7W1tZBlGV999RVOPvlkAMDYsWNRWlrq+5nA519SUuIrZ7V06VJcfPHFQY9/zTXX4MUXXwz63vHHH4+1a9cC0HpelJaWdmvOTUREREQ0FLBkExERERHRCLBw4ULs2rULv/rVrzBr1iykp6fDYrGgoKAAF198MVasWOELRgDAvHnzsGPHDtx0002YOHEiEhISkJWVhUWLFuGll14KCkYAWmmm+++/H9OmTUN8fDwyMzNxzjnnYNWqVfjud78LQAsGLFu2DACQk5ODdevW4W9/+xvmz5+P3NxcmM1mZGZm4pRTTsELL7zQLRgBAA888AC+/vprXH311Rg7dizi4uKQnZ2N888/H5999llQMOJIybKM5cuX41e/+hUmTpyIuLg45Obm4pJLLsH69euxcOFCAEBDQwO+/PLLI/493/3ud/GHP/wBBQUFsFgsGD16NKZOndrtfgsWLPB9ffLJJzMYQURERERDFjMkiIiIiIiIYlRDQwOmT5+Ouro6AMALL7zAkk1ERERENGQxIEFERERERBRDOjo68Nvf/hZmsxlLly7FwYMHAQDZ2dkoKytDYmJilJ8hEREREdGRYUCCiIiIiIgohqiqioSEBLjdbt/3JEnCyy+/jCuvvDKKz4yIiIiIqH/YQ4KIiIiIiCiGyLKM448/HlarFenp6Vi4cCHefPNNBiOIiIiIaMhjhgQRERERERERERERERmOGRJERERERERERERERGQ4BiSIiIiIiIiIiIiIiMhwDEgQEREREREREREREZHhGJAgIiIiIiIiIiIiIiLDMSBBRERERERERERERESGY0CCiIiIiIiIiIiIiIgMx4AEEREREREREREREREZjgEJIiIiIiIiIiIiIiIyHAMSRERERERERERERERkOAYkiIiIiIiIiIiIiIjIcAxIEBERERERERERERGR4RiQICIiIiIiIiIiIiIiwzEgQUREREREREREREREhmNAgoiIiIiIiIiIiIiIDMeABBERERERERERERERGY4BCSIiIiIiIiIiIiIiMhwDEkREREREREREREREZDgGJIiIiIiIiIiIiIiIyHAMSBARERERERERERERkeEYkCAiIiIiIiIiIiIiIsMxIEFERERERERERERERIZjQIKIiIiIiIiIiIiIiAzHgAQRERERERERERERERmOAQkiIiIiIiIiIiIiIjIcAxJERERERERERERERGQ4BiSIiIiIiIiIiIiIiMhwDEgQEREREREREREREZHhGJAgIiIiIiIiIiIiIiLDMSBBRERERERERERERESGY0CCBkVLS0u0nwKNQBx3FA0cdxQtHHsUDRx3FA0cdxQNHHcULRx7FA0cd2QkBiRoUCiKEu2nQCMQxx1FA8cdRQvHHkUDxx1FA8cdRQPHHUULxx5FA8cdGYkBCSIiIiIiIiIiIiIiMhwDEkREREREREREREREZDgGJIiIiIiIiIiIiIiIyHAMSBARERERERERERERkeEYkCAiIiIiIiIiIopRDpfAna86UFLLRsNENPQxIEFERERERERERBSjNhxQ8NY6N5Zv8UT7qRAR9RsDEkRERERERERERDGqulkFALTYRZSfCRFR/zEgQUREREREREREFKOqmhiQIKLhgwEJIiIiIiIiIiKiGFXVpAUiGJAgouGAAQkiIiIiIiIiIqIYxZJNRDScMCBBREREREREREQUoyqZIUFEwwgDEkRERAZo7xR4b4MbQnDSQERERERER0YIZkhQdGw66MFf3+nknJYGHAMSREREBli21oVfv+zAllIl2k+FiIiIiIiGqBYH4HR7v2ZAggbRa9+48dwXLlQ0ctzRwGJAgoiIyAB1LdpFW0MbL96IiIiIiOjI1LT4v3a6gU4X5xc0OCqbtMyculY1ys+EhhsGJIiIiAzQbGedVyIiIiIi6p+aFgkAkJKo/bvFwfkFDQ69VFhdK8ccDSwGJIiIiAzQ1M6ABBEREdFw9N4GNz7Z6o7206ARQs+QmJpvAgC0dHB+QcZTBVDTrI01BiRooA2rgMSKFStw+umnIycnBykpKVi4cCFef/31Pj3G7NmzIUlS2P/27Nlj0LMfOZo7BCoame5FRMNbUwcDEjT4/u+9Ttz7piPaT4OIiGjYEkLgj0s78diHzmg/FRohalu1DIlpBd6ABOcXNAga2wG3tx0iSzbRQDNH+wkMlFdffRVXX301zGYzTjnlFJhMJnz++ee49NJLsXPnTvzhD3/o9TGcTid27dqFjIwMnH322SHvk5aWNtBPfcS5900H1hcrWHVfSrSfCo0QLo/ANY/bcclCC747Py7aT4dGCAYkKBo+2uxGeyfwu4uj/UyIiIiGp8Z2gRa7QNywWU2hWFfTqv1/ar62p5jzCxoMta3+r/X+iEQDZVicQmtqanDdddchKSkJK1aswNy5cwEAe/bswZIlS3Dffffhggsu8H0/nO3bt8Pj8eA73/kOXnnllcF46iNSaa2KulYBu1PAGi9F++nQCFDdLLClVEF+hsSABA2aZm9AopUTBhokQmjp1E4P4FEEzCaeY4mIiAbagRptp3Bzh4AQApLE8y0Zq6ZFgi1ZQnaqNtYYkKDBoPcuAViyiQbesCjZ9MQTT8DhcODmm28OCjpMmzYNDzzwAIQQeOSRR3p9nE2bNgEAjjnmGKOeKkHbUQIADW08oNHgqG9Tvf/nmKPBoarCF5Bo5oSBBkmHE+h0a4GJZtYWJiIiMsSBam1u4Va08y6R0WpbgFEZEtKsDEjQ4NF7lwD+NRWigTIsAhIffPABAODCCy/sdtuFF14ISZLw/vvv9/o4mzdvBsCAhJGEEL5AREM7D2g0OBq9Y44BCRosrQ6tCRjACQMNnob2wK857oiIiIygZ0gAvM4j43kUgfo2YFS6zIAEDaoab++S7FSJGRI04IZ8QEIIgV27dgEAioqKut2ekZGBvLw8NDU1oaKiosfH0jMkKisrceqppyIzMxMpKSk45ZRTsHz58oF/8iNQm8PfFIcZEjRY9EBEPRsx0SBp7vCPNZZsosHS0O5Pq27kOZaIiMgQB6sV39ctzEgkg9W1CihCQl6GjPQkBiRo8NS2ApIEFI0xoaFNQFE57mjgDPmARFNTEzo7O5GSkoKkpKSQ9xk1ahQArddEOIqiYPv27QCAH/7wh6ivr8dJJ52EMWPG4Msvv8SZZ56Jhx9+eOBfwAgTmObF3eo0WPTgV6tDa3BNZLSmgMkpJww0WJghQUREZLygDAkHz7dkrKpmbbyNypCQmsiABA2emhYJWSkSRmVIUAU3FdPAGvIBiY6ODgCA1WoNe5/ExEQAQHt7e9j77N69Gw6HAwkJCXj33XexZcsWLFu2DDt37sRrr70Gs9mM22+/HevXrw/7GE6nE62trUH/OZ3OI3xlw1NjwAIJd2/SYAksD1bPVEMaBIEBiVYHuJuEBkVDm//rRtZ5JSIiGnBtDoGaFoE4s/ZvZkiQ0aqbtDE2Kl2G2SQhKZ7jjgZHbQuQnyEjJ1VbOmbZJhpI5mg/gf4ymUwAAEmSerknoKrhJ+dFRUWorq5GR0cHJkyYEHTbpZdeijVr1uCRRx7Bk08+ieeffz7kYzzwwAP4wx/+EPS9O+64A7fffnuvz224a2pqAgCUVkkAtL9ZRZ0DjY0dUXxWNNzp466qQYYefz1Y0YyEKD4nGv6amppwuEY71mUlC9S3SyivakJqYrSfGQ13FfUuANoGjcM8x9Ig0c+1RIOJ446ioampCZWHAcCMaaMEtpVLqKxrR2MjF+nIOAcqtHlFkqkNjY1ASoIJjW0eNDY2Rvup0TDmVoCGdhNmjXHDanIBMOFgRStGJfF4Rz2z2WwR3W/IBySSk5MBAA6HI+x99Nv0+4aTm5sb9rbzzjsPjzzyCDZs2BD2PnfeeSduu+22oO/Fx8cjPj6+x987UthsNriEC0AnAKDDEwebLXxmC9FAsNlsaHN2ANBqvbqQApvNEt0nRcOeu8wKwIlxuWbUtyuQ4tJhsw35pESKcR2eZt/Xdk88bDZGwch4QgDvb0/CGbMtyE3ncY4GT6QTXqKBtLU+GUAn5k2Jx7ZyFzyyFTYb5/tknBZXJwAXpo5Nhy1dhi2lHa0OAZstLdpPjYaxww0qBNoxJice40aZADjQKZJgs8VF+6nRMDHkZw0pKSlISUlBS0tL2KBEVVUVAH8viSORl5cHALDb7WHvEx8fj9TU1KD/GIwIFlQ6hyWbaJAE1jrkuKPB0OwtTzc+RzvNsrE1DYaGdsAaB1hM7CFBg2dfNfCnZU488ynLlBLR8HfQ2z9i7ngt65+lc8hoVU0qTJJAdqpWFSTNKrGHBBmuqkk71uVnSMhmySYywJAPSEiShJkzZwLQ+kB01djYiOrqamRkZKCgoCDs4yxduhRXXnkl/vWvf4W8/eDBgwCAwsLCAXjWI5e+MJyeJLEhDg2ahjYVad5kHAYkaDDoPSTGZmun2WZOGmgQNLRLyE6TYUuW2KeJBk1Vs7ZAsv6AEuVnQkRkvAM1KswyUDRGC0i0sqk1GayqSUVWCmCS/QGJNgfgUTj2yDjVzdr4ykuXfcGwuhb2qKOBM+QDEgBw1llnAQDefvvtbre9/fbbEELg7LPP7vExGhsb8e9//xt///vfIUT3A/uLL74IADjjjDP6/4RHsIY2AVkCJubKDEjQoHB5BFodwJR8bdJQ38qTKBmvqUPAJAOF3jJN3MVEg6GhDchOlZCZIgVlJBIZqbpZ+//+KhXN3ClMUeDyCOw+zIAYDY4D1QrG5WjBf4DXeGS86maB3IDqTKlWbey1MRhGBtIzJEZlyMhIlmCSmSFBA2tYBCSuvfZaWK1W/N///R9Wr17t+/7evXvxv//7vwCAX//6177vV1VVYc+ePb5STgDw/e9/HzabDVu2bMG9994bFJR49tln8eabbyInJwc33njjILyi4auhTSAjSUJ2qpZm6GZUnwzW6C1bMjlPhiwxQ4IGR3OHQLpVQnqSNmFgySYymssj0OLQzq/MkKDBVNMi+b7eVOKJ4jOhkUgIgV+96MBFD3bgcAMDsWQspwc43CAwIVeG2SQhKZ4lm8hYnS6BxnaBnDT/OEuzMhhGxtMDEnnpEkyyNr9gQIIG0rAISBQWFuKxxx6D3W7HiSeeiNNOOw3nnnsu5syZg+rqajzwwAOYPXu27/533nknpk+fjjvvvNP3vfT0dLz88suIj4/H73//e0ybNg0XX3wxZs+ejeuvvx7JyclYtmwZm6f1U0O7QGaKtnsT8C8WExlFD0DkeMuY1PMkSoOgqUMgI1nihIEGjZ51mJ0qIzNFht0F2J0cd2S86hb/1xtYtokG2csrXfh0mxYIO9zIgAQZq7wBUIWW7Q8AaUkSWrhLnQxU3awd13JT/d9L5/yCBkFVs4BZFsjyrt1lp0qoY7UJGkDDIiABAD/+8Y/x0Ucf4YQTTsCaNWvw9ddfY+7cuVi6dCl+85vfRPQYZ599NjZs2IDLLrsMLS0tePfdd9HQ0IBrr70W27Ztw6JFiwx+FcNfY7uKzBQJWSna0GPZJjJaY5t20tQDYfVtPImS8ZraBdKTJF9KNXtIkNH0HUtZKZKvjASD/jQYqlsk5KVLSLMCGxmQoEG0vUzBg+84YfLOaJt4zCODldZp59eJeVop2HQ2FyaDVTRq4yuXGRI0yKqbVeSkArKsByRk1LWKkCXuiY6EOdpPYCCdccYZEfV4eOGFF/DCCy+EvK2oqAj/+c9/BviZEQA43QJtDsCWLMHmjbIyIEFG0zMkMpMlZKVK2HyQAQkyliq0CUJGkj9DgiWbyGh6f5zsNAmyd3GusV2gMDOKT4pGhOpmYNIoGWlWCSt3eWB3CljjpV5/jqg/2jqBX7xghyQBt58fjwfecnJeQYYrrfcGJLwZEqmJEkuFkaEOVGuB/nFZ/u/5AxLReEY0UlQ1qRif7f93TpoEt6JttMtI4nUe9d+wyZCg2Kfv1MxKkX1pXw3crU4G0yenemaO3QV0sIwJGai9UwtKpCdptYXNMncwkfFqW/0lm/QMCZ5jyWh2p9a7JD9DxjETTfCowNZSZkmQ8V77VkZFo8BdFyVg4VRtj11jO495ZKzSOkCSgPE5/pJNrQ5AUXmdR8bYX60d18bn+McYM7DJaHanQIs9ODMnO1Ubd3UtHHc0MBiQoEGj71S3BZST4E4mMpo/IBEYCOO4I+M0e3crZSRJkCStbBMDEmQ0vaZrdoqETJZsokGiNzwclSHhuInaovCGg2xsTcbbWQHEW4CLF1hYpo4GTWm9hAKbhIQ4bcylJnozYdlHggxyoFpFmlWCLcn/vXRmYJPBQvUuyU7Vlo/Z2JoGCgMSNGj0Wv5ZgT0kOHEggwVlSHij+vVsxkQG0tOn072prGlWiRMGMly9niGRJsHGPk00SCqbtDGWb5MxvVBGYhwbW5PxhBDYVyVhWr4JZpOE9CQJksSABBmruUOgpBaYUWjyfc+/MBytZ0XDmRACxdUKJo+SIQVUyElLYg8JMpZ+fZeT2j1Dgj05aaAwIEGDRg8+sIcEDaaGNhXWeCAxzh8Iq+e4IwO12LXjW0ayPyDBlGoyWl2rgEkWSLf6MyQY9CejVTRqk9ICmwyLScKccSZsLVXg8nDskXGqmwWa7RKmF2rXdSZZQrpVYkCCDLV2vwcCkq9EGMCFYTJWbatAqwOYlBe8bMem1mS0am8GbG6a/3u+kk3MkKABwoAEDZrA0jlJ8RIS41jfmozX0C58pZp8UX2eRMlAgSWbAG3SwAkDGa2+TYUtCZBlf1nERgZfyWB6yab8DG3MHTvRjE43sOswsyTIOLu94ytwp3pmCgMSZKzVe7VydIEBCb1kE6/zyAjFVdo5dlKeKej7CRbAYtKydoiMUKWXbArqIeEt2cQeEjRAGJCgQRNYOkf/PzMkyGgNbQK2ZO1Qp489phmSkVpCBCRcHqDTxeMdGaeuRSAzRfs6IU5rqN7ABq9ksMpG7bg2KkM7zx4zUVs02ciyTWSgnXpAYrR/kS4jmfMKMta3+zzITxcYk+VfQuFOdTJScbV2rOuaISFJWqk6jjsyyoHq7hkSWb4MCc4vaGAwIEGDRp8k6Ds3M5NllpMgQ6lCqyesZ0j4ekhwwkoGavaWbEpP0k6xnKyS0YQQqG8TyEz2jzHuFqbBUNmkIi1RwBqvHeemjNKOe+UNnKyScXYdVmGShW+8AUBmsrY451F43KOBd7hBxaF6geMmBI8vXuPRkehwRjZeiqv1DInuy3ZpVonN1Clida0qDtVHdm3W5hD4cqcHx040ISXB//04s4Q0q4RaVpugAcKABA2ahnZ/LX/Au1jSJqCqPKCRMVrsWlBCz4xIS5RgMbFkExmr1aH9X8+QSLVq/2YfCTJKs13ArQCZyf7v2ZJl7hYmw1U2qshL9/87zSpBltgjjIy1q1zBhGxtcUSnb3hqYgkTMoBerilcQKKV13gUoW/3enDsHW2+0nM92V+lIj1J8s1lA6VZJbTweEcR+v3rnbj6sY6I7vvRZjecbuDC4yzdbstJldhDggYMAxI0aAJ3qgPaIrFHBVoY2SeDNHrPufpFnF5bnSWbyEjNdsAkAymJ2r85WSWj6bVcAwMSmSkSmtoZ9CfjuBWBmhaBvID6wiZZQkYSS+eQcRraVNS0CEwZFTzG9PKczAwjI6ze64EkAceMDx2Q4KYTitT+KgVCADvLew5ICCFQXK1gUp4MSQoTkLALCMGxR72ratLOnZHMR99e50aCBTjz6O4BiexUiSWbaMAwIEGDJrCWP+BfJGbTTTJKU4eejeMfd1mpEjMkyFAtdgnpVsk3eUhnOj8ZTC9DF1iyyZasBf31jB2igVbbIqCK4PrCgLdHGBeFySC7DmsLIVPyugYkOK8gY6iqwLf7FMwolJFmDb4tldd41Ef6dVl1c89jpqZFoL0TmByiXBOgjT2PCnQ4B/oZ0nCkl/eqaOw5mFBaq2BTiYLTjrIgOaF7ICw7TYbdGXnZMaKeMCBBg6JrLX/AP3FgPX8ySmO79v/ANNesFBn1bdxNQsZpcWjNNXWcrJLR9J1KelNr7Wvv4hwbW5NBKr2T2sAMCUDvX8JxR8bY5S1zMrVrhoR+zGMJExpguypUtNgFFk41d7stKR4wy8yCpcjpC8M1LT2fJ/dXeftHjDKFvJ39S6gv2rzj7nAvPb7eWe8GAFw0v3t2BKBlSAAsgU0DgwEJGhStDkBR/ZMFQFsYBphaTcZp0ks2JQeOOwkuD9DGXcNkkBY7kJ7kH3OcMJDR9FquXTMkANbyJ+NUNnkDEunB389MkdDmAJxujj0aeLsPK5AkYFJe8Pf9xzwGw2hgfevtH7FwSveAhCRJSPWWziGKhB6QqGrq+VhVXK0FX0M1tAb8GdgMhlFvVFX41j4O95AhoaoCb69zIy9dwvzJoQNh+oYnnmtpIDAgQYMi1MKwfjBjhgQZxV+yKSAgoUf1eRIlAyiqQKvD39AaYECCjOcPSPi/p5eqY+kcMkploza2umVIsJY/GWjXYQXjsmVY44K/r88xmjjuaICt3utBvAWYOyH8TnUuClOk9LFS00vJpmJvhsTkUaGX7Di/oEjZXVrFEqDnDIm1+xVUNQtccJwFJrl7uSbAv6mYa3g0EBiQoEHR2N69lr+/hwQXhskYesmmrKAeEjyJknFaHQKqkEJmSHCySkbRSzbZAgMSrKdOBtMzJEL1kAB4nqWB1+YQOFQvMKOw+xTW10OCAQkaQIoqsLlEwdHjTIi3hF6gS7NKbGpNEfNlSDT3liGhwpYsBfXgDJSWxIbqFBm9XBMAHG4IP14+2KSVa7pwXuhyTUBghgTHHfUfAxI0KHwZEinMkKDB09gBWExASqL/e3ofE9Y9JCM0e2tXB2ZIsIcEGa2+VSDNCsQHVJPwlS9hLX8ySGWTigQLkN6lySs3nJBRdldoJUxmjO6+Uz3NKkGWGJCgvvl2r6fHHcOHGwQ63cC0gtDZEYA29niNR5HSF4fbO4H2ztDjRgiB4molbLkmAEhN5PyCItMaEJDoqan1xoMKRmdKGJ8T/njnX8PjNR71HwMSNChCBSTSEiWYZEZXKXKq2rex0tQhITNFgiQF95AAgPpWnkRp4DXpAYmA8nQWk4SkeO5gImMIIXCoXkVuWvAlnX9RmOOOeieEwN5KBUJEPl4qGwXybTKkLpuG9WAYN5zQQNtV7g1IhFgclmUJGUkSAxIUMadb4Ppn7Hjo3c6w99lfpY25cGVzAC0g4fIAnS6OPepdYMZ0dZgsiepmgQ4nMKmHcaf3kNA3QxGF09YlIBHqWq/FLlBSq+KoseGDEYB/LYVreDQQGJCgQdGo1/IPWKSTZQm2ZIn1rSkiFY0qZv9PG77Y4Y74Zxo7goNggL+HRB1PomSA5vbuGRIA6wtT39z6vB3PfuaM6L6ldSqqmwWOnRg8gUhPkiBJ7CFBkVlbrOCCv3RgxS5PRPcXQqCqSUV+RvcSJnqZRC4M00DbUqo1tA6VIQGA8wrqk/o2AbcCHKwJv0lpn7eO/5RR4RfpmAlLfRG4W706TB8JXyAsL/y4y2DfHIqQPuascYDDFTqYsP2QNuZm9xKQSE/SshEZkKCBwIAEDQo9Q8LWZXE4M0VCA9O9KAIHa1S4FWDTQSWi+wsh0NTub66p8zViYskmisB/vnbhnD+1w+mObLzoGRLpIQISnKhSJFRV4JOtHny2LbKF4dV7tWPiwqnmoO+bvLuFOWGgSFR4S5bsqYjsmqypQytjMiojRC1/7p4jA6iqwNr9CqYXyL7eTF3ZUiQ0sUwdRUjPIDxUH3rHMKAtDEsSMLGH0jlp3rJ1zISl3ngULfPBGqf9uyZMhsTOcu37ofrl6HwBCWZIUC/0DImp3uzCwyHKNm0r0+YTR43rOSBh8m4qZhYsDQQGJGhQNHYAZlkr0xQoM0ViOQmKiB7Z76nuYaAOJ+BSpG4ZEskJWl8J7tykSGwuUXCgRkV5D/WFA9W1hs+QYECCImF3AUL03uxQ9+1eD2QJmD/Z3O22TO4Wpgi19fEcq+8oLszsPpXQs2EZkKC++HqPB4992Bl+YbhaRVOHwLxJ3Y91OluyhBY74FY49qh3eo+lTjdQG2aj0v4qFaMzJVjjQwfBAPgCZMyEpd60eXtGTPZm3FQ1hR4zO8oVmGVgan74xWFrHBBvARoZhKVetDq0/08v0K7ZQjW23lqqwGICpvfQL0fHTcU0UBiQoEHh9mgHLlnuUj4nRYbdBdidvICjnrV5L/IrGyMbK1tLtSj/2Ozgw5wkSchIltDUwZMo9c4XCIswILG5RIFJFr6Jhi7VKqGtE1D62AeFRh59QaOuVfS6qOZRBNbs92DWWBNSErsvlmSmSOyXQxHp67Hu6z1aBs/8yd0nrglxWt8cNlSnvnh5hQtPLnehpiX0cW/t/vBjTmdjCRPqg8Cg6aG67scrl0egtFbtdk3XVRpLNlGE2rwLw1PytflpTUvo8+SOQwomj5KREBc+ECZJEjKT2TeHetfuvcabXujNkOhyrSeEwLYyBdMKZMRbwo85XWYKMyRoYDAgQYPib1ep+OL3yd2+b+MuOoqQvqMk0t2bn27Tek2celT3nXQ2XrxRhPRdw5VhdjAFcisC6w94UFSIbjvp0qwShPBPRIjC0Y91QgB1YRbmdNsPKWjvBBZNDb1YkpMmo70TcLDRJvVCPzaFSuMPZeUuD9KTJMwaE3rsabvnOO4ocvr1nd64uqt1+xXIEnDsxJ4yJNi/hCIXFJCo737sK6lV4VF7bmgNMCBBkdPHSGGmjAQLUNXUfdzVtqioaREoCnN+DZSRLDEAS71q7RKQ6LqecrhBoKlDYFYv/SN0WSky7E7OL6j/GJCgQWOSQzU+9AYkuIuOeqGfSOvbRK/1/BVV4LNtHozJFJiYG6K+dRIDEhSZvpQK23FIgd0JHDOu+9jiZJUi1R7U7LDncfftvtD9I3TZqdq4q2PPHOqFfqyrahK9ZnLVtarYdVjFoqmmkNd2AJCZIvM8SxETQvjOsztCBCRUVQv4zxwth8wG0+nlwjj2KBKBJQ3LQmRI7KvUvtdbhgSbWlOk9I1OaYkSctNl1IRoar3TewycObr3xWFusqNI6ONuTKYMa3z3bNitZZE1tNZlslcYDRAGJCiq2PiQItUWsEhXGWI3SaAtJQrq2wSWTBeQpO4T14xkCXYnIm5UTCOXvjhcGUFAYu1+7WLumAnd78v6whSp1qCARM/jZfVeD6zxwOwwDehy0rRxVxumJACRTj/HuhWgtpfMnFW7tdI5J84Iv1NdLyOhskwdRaCpQ8Dh0r7edbj78WpPpYoWO3B8iF45gTKYeU19oPcxlCSgLESGxP4q7bpucg8NrQEgnQEJipB+jZdilTAqXUJ1iOszPSgbSYaELVlGp5vlr6lnrQ4BSdJ6aRbY5G7ZsL6G1hFnSGjHvHr2kaB+YkCCoiorRRuCnDhQbwIX6Sp66SPxyVZtseTkGaFPknpKf1MHxx31zJ8h0ftYWbPPgwQLMLOg+216k2teuFFv2jv9X/fU2Lq9U2BLiYLjJpphMYXeMZydqh3reltgJgo8x3atLdzVqt0eSBKweFoPAYkUCYrKBTqKTOA5dtfh7hkSev+Ieb0EJPRdm03MvKYINLSpSIoHRmVIIXtI7K9WYTEB43IiK9nETSfUG32MpHozJNoc2vVcoB2HtHE3pZdSYYB/fsEsCepJm0MgOQGQZQmFmXK3bNitZQrSrMC47MiWh5khQQOFAQmKKvaQoEgFZUj0sFtdCIFPt7lRYJMwJS/0fWxM6acIeBSBDqf2dW9ZOZ0ugU0lCuZOMCEuxHpJvk073VZF0IuCRrbAY12oVH7dhgMeeNTw/SMAf4ZEHRtbUy8Cx11PfSQ8isA3ezwoGi0jMyX8NMI3WeV5liKgl2tKSdQCqF2PWWv3KzDLwNwJPe/e9M0rOO4oAg3tApkpEsZmySirVyFE8LjZX6VgXI6MOHPPTV5ZsokipZ9rUxO1QBgA1ARsPhFCYGe5gqn5vY87gHNaikyrQ/jKHRbaZLgV/xzD5RHYfVjBrDGmkJUlQtGv/9jYmvqLAQmKqixOWClCrQHNgHuq57+jXEVlk8BpR1kQ7pyawYs3ikDgTvW61p57l2wpVeDyAAumhN69me+ddETalJ1GrsCd6qGaHepW7/X2j+hhlzozJChSbZ0C8Rbt654yJLaVKWh1ACdM73mnup6JyMkqRUI/Ny6ZoQ3CwMbWHkVgwwEPZo01ISm+58USNrWmvmhsF7AlyxiTpTVoDdwg1+EUONwgem1oDQAWkwRrPNDi4LijnunXeKlWCXlp3s1KAZtPqpsF6tsia2gNBAYkOL+g8NodWlYOABRmav/XN5/srVDh8kRergkIWMPjNR71EwMSFFX+Wq88iVLP2hwCBTZtvFT2UD7n061uAMDpc8IvlugXb02csFIPuqZQ95QlscZbTiJcfetRGXKvj0EE+MedJPXc1HrNPg+yUyVMzA1/Kcem1hSpNofwlYfoqUTdygj6RwD+yWojJ6sUAT3z9fTZ2rjaGdBHYvdhFe2dwPzJvS+WpCYCJpkBCeqdqgo0tgtkpUgY4y1Tciigj0Sxt3/ElF4aWuvSEiW0sBQs9SKwZFNeiAyJHYe8/SMiaGgNMEOCIhOUIZGpHe/0zSd9bWgN+LNgWYqY+osBCYqqOLOENCt30FHv2hwCuWkyMpKksLvMhRD4ZJu2SDenh5Mqd5NQJPRdTKPS9eyG8MepNfsUpCQC0wtDn1bjLRKyU6Ueg2lEgD+df0ymHLaptaIKlNSqmFHYc3q1NV7ylkDhsY7CU1WB9k6ttFyatecMiZW7PEhPkjCrl92bNk5WqQ8qGgUsJmDRNDNMcnCGhB7wnz+p5yAYoNXHzkiSuDhHvWqxCyiqdqwam6VduwU2tt5fpX0dSYYEAKQnSexNR73yNbVOlJCXro2twGu9vjS0BvznWm6yo3CEEGhzCH+GhM0fkPAoAp94N3P2JUPClixBkpghQf3HgARFXWaKzB101Cs9sl9gk8LuMj9UL1Baq+LUWWbIcvhFOu4moUjok4apBdoFWrhAWHunwPZDWnNhc5jmwgCQnyEzQ4J6pQckJo+SUd8m4PJ0P05VNQm4lciaz2WnysyQoB61dwJCaDs2C2wyKsIEJOpaVew6rGLxNBNMPZxjgYAMCZ5nKQIVjSpGZUiwxmtZXzu9i3KKKvDmt26kJgJHj498xzDnFdQbvVxwZrKEMd6ARGBjaz0gEWmGhC1F4qIw9arVIZAYp23KzPVueArMht1RriDeAkzMi2yZjmXqqDcOF+BRgeQEbbwV+DIkBO5+rRPrihVcOM+CjOTIl4bNJgnpVokBCeo3BiQo6mzJEjMkqEdOt4DTrS2W5Ntk1LSEXqTb5k05PGZiz7vo2EOCItHmTaueVuAttxQmILHhgAeKChw/pedJa75N6rUXBVFbpzZZLciUIUTocktl3kWTMREEJHJSJdSyqTX1IHDHZmFm+HOsXkpiXgQ71fVFEk5WqTdCCFQ0qijw7tqcOdqEqmaBxnYVy7d4UFav4qoT45AQF1mzTVuyhMYOHvOoZ/rcMzNF9gUkyoICEop2LrZF2OQ1WYbdBdidPOZF4qUVTmw/pPR+x2Gm1QFf6Zx0q4QEiz8gIYTAjkMqphWYYOlhg1MgbrKj3vgbqWtjJTlBQnqShA83ufH2OjdOmmHGfZcl9Plxs1K4hkf9x4AERV1WioQWu4Bb4QGNQtNPpClWoMCmLdLVhChl4ktzHd3zoS0tUYJJZnor9azNW8t/Wn7PGRJr92vjbn6Y/hG6fG8fiZ4aFRO1ObTJgl4qrDrEeNHrXI+NMEOizQE4XDzeUWiBk9UCmwxVIGS5sJJabdz11LdEl2YFzDIDEtS7Fjtgd8IXkJjhrZ2+s1zFM586kRgHXH1iXMSPZ0uR0OZAyKAaka7RF5CQkBAnIS9d8pVs8igCuytUTMqTe8y4DsSF4cjVNKv40zInnv/SGe2nMujaHAJp3oVhSZKQmy6jukkbMxWNAi120es8NlBSPGAxcdxReP5NJ/7vFdokeFTg2IkmPPqjxIgDYIEyUyT2gaV+Y0CCos7GtH7qRWvAYkm+d8JaEWKRbme5Vsdf3+kUjixraYYcc9QTfZEuO1WCLTl8/4cNBzxIs0qY3Et6tT52K5s47ii89k6tzqteW7gqxMJwqXcX59hejnUAkJPGxtbUMz34qmdIAKH7SOgBifERBCQkSYItRfKVRSEKRw/2+zMktP8/86kTeytVXLIwrk+lJPTsHG46oZ7oxyY9kDAmS8ahehVCCLyz3o2mDoGTiywRP15WKuezkdpfrX3mR+JntNXuby4MaH3qqlu0cfevz7UATV+aC0uSNkdpYlYYhaFf46UGjLuz51pw4nQznrrOGnH2YVeZ3uA/M/+pPxiQoKjLSvHWPuQuOgqjLaCchJ463bXGtaIK7CpXMLPQFNFuJi2ln2OOwmvtMu5CZUi0dwrsOqzi2Im9jzt97IYr/UQEaOMuOVFCXoY2XmqaQ2dIWEzAqIzej3XZ3kWSOja2pjB859gEbdccED4gkWYFMpIiLWHC3XPUO/3cmu8de9PyTZAkYMMBBRYTcO3JkWdHANq4A8BSEhHaVOLB66td0X4ag67Re2zS+92MzdayCetaBZ5c7kSatY+ZOb5xx2Neb4qrtMzikdgEXO+JqMtN18bd4x858Z9v3Jg/2YQzj448EAZ457QjMLhDkdFLEAeOu2tPicc/brQGfa+v9DU8ZsJSfzAgQVGXmcKJA/Ws1aH9PzVRQkGGvss8+IK/pFaF3QUUjYlsV0lGsoSmdk4aKLw2fdxZtcyc2tbuddW3lCpQvCmvvdFLNoUr/UQEAO0OgZQE9JghUVanoiBT7rGJui4nTXucWmZIUBhde0gAoY9TJTUqxuWYIEkRBiRS2Fy4Lw7WKLjk/9pxsGZk1VXvmiFhjZcwIUf7+sJ5FuSm9226qo/h8nqeayPxyPtO/O6/nSOuxJU+79Qz9fXs6kc/cKKiUeBHJ8f3abFOD4TxmNc7vWH4SMuQ6HQJuDxAmjUgQ8K7seTJ5S5MzpPx+LVWxJn7tkickcxzLYWnr6P0J/gQir6Gx4AE9QcDEhR1+gUcd9FROIEZEr6STV3K5+jNNotGRxaQsCVLaLGDvUsorODMHK13Sde66hsOeAAAx/bSSB1gySbqnUcRsLu0MZedqvW66ZohoagC5fVqROWaAH+GRC0zJCgMffdcYFnErhkSrXaBhnaB8TmRTx0yU9jktS+e/cyFbWWqry/RSKGXQ9QDEgAwd4IJZhn4yXfi+/x447xjVC8xRuEJIbCnwrtbfYQtDje2C5hk+Or56wGJpWvdyEiScFUfsiMAwKbvFh5h7+ORKPaWbGpsFxBi5LxfoWr553k3jWSnSnjmBitSrX1fNNYbqneyVxiF0LWp9UDxbyrmuZaOHAMSFHW+6Cov4CgMX33rBAkpiRJSE7uXvdlRrv070gwJPbW6eQSmC1Nk2hzaZNUa589u6DruNhxQYI0Hphf0fjpNTpCQZmWGBIUXWMvfJEvISZW6NUGvahJwK5E1tAb8GRLsIRE5IQQ2l3hQHaJc1nAUuHsuMU5CVoqEw12C/iW12qJl3wISrKkeqaYOFR9udgMA6lpHxrjTVTSqMMv+fjcA8OvzE/D2HUkRH+cCjWdAImKVTcL3+R9p87CGNgFbsuQrtxk41n7ynTgkJ/Rt8Y4ZEpERQqC4WjufuBWgYwT1tQ61MLxomhknTjfjHzdYfRsC+ooN1akngVmwAymLGRI0ABiQoKjLZP056kXXC7gCm9xtUXfHIQVpVn+Pid5k8OKNetHqEEhOkCBJAb1LAsad0y2wtVTB3PGmiErnAFpgo2u5MSJdu74w7F0IyU2Xu2XlHPKWIYl0oc6fIcFjXST2Vii49kk7Ln/Ejj++2RntpzMofOdY787Mgky5W58mX0PrvgQkWFM9YsvWuOHU4hEjLnhY0agiL0MLwupSrRIm5UXe2DVQcoKWYaYH0Sg8PTsCGP7Xwx1OAUX1v8aGNtUXNAWA0d4MicxkCZcv7lt2BOBfFG5gOdgeVTWJoCDESMrMaelyrgW0cfePG62YXnhkxzsgYE7LTXY9Otyg4tcvOXzXPCNFe4im1gMhM1U7ZrLsOvUHAxIUdaw/R71ptQenuObbZNQ0C3i85ZbcisDuCgVFY+SIa1vbkrXD30i6EKa+ae8USA0Yc0BwQGJbmQK3Elm5Jl3XsUsUKDBDAtBqC9e3BfcuKavTxuCYCEs2WeMlJCeMvF3XR+KRDzpx0YMdWLNfa6Y7UoKHXXfPFdq0cRdY/uFIAhJ6bXbuGO6Zogr8+2sXMpMlWEwjb3Jf2aQGlWsaCONyZJTWqSOqHMyR2H3Yf4xrHMaBw/ZOgVN+34YnPvavhDe0C18QAQCS4iXceVE8/nJ1IqzxfV+4S4iTkBTP411v9OyIwkztPR5Jja0DyyMOJH0cc07bs3c3uPHeRjdW7/VE+6kMKmZIUCxjQIKizhoHJFjYQ4LC67Z70ybDo/p3/B6oVuF0R94/AmB6K/Wu1SF8F28FIXqXbDigTaqOi6ChtS4/Q4aicrc6hdZ10pCrN6Ru6R6QGNeHUiY5aTLHXC/sToGnP3FhQq6MN3+VhKn58ogp6dfe6S9PByBkY+uSWhWyFHlmDgBksaZ6RFbu8qCiUeCShRZkpUioG0H9XlrtAm0ODHhAYnyOjDYHF0p6E5ghMZw/p8XVClrswBc7tIVIh0vA7vQfo3Q/WBKPxdMi32TSVWaKNKzfx4GgN7SeN0l7n5uGeUaJGpCVo5dHMyog0TjM38v+2l+lHe8Oj7DSufo6SnLCwD6ujX1gaQAwIEFRJ0kSMlMkLgxTWK2+E6l24svP0P6v717ta0NrgCWbqHdtAQGJUP0f1h/wIM4MzBrbh4CELXjsEgVq1wMS3knDKO+xLrCXQVm9Vm9dvy0S2akSMyR6Ue4tUXT6bDNmjjYhI1kaNjs3XR6BVbs9YXeLB5anA/yLw4GNrUtqVRTYJMSZIx93/snq8HgfjfLvr10wycCli+KQlSqNqAwJ/Zyq92kaKHrAln0keranUuuDBQzvnf36ONhbqaLFLnzHpMAMiYGQmSJzXtGL/d6G1sdN0q6dh3OZoc+2uXH07W2+Upu+TSdH0Li6J9xkF5libzCsa0nK4aa9U/iqSwDauLPGI+LywpGKM2tz45F0zUIDjwEJigmZKSNrAkZ90+YAEuPgWwjpuliyo1wLSPRlYdiWxN0kFJ4Q2q7NwF1MBTZ//wePIrClRMHscaY+LdDpY7drc2wiwF+yKdk77vLStfFS1eQ/Px6qU1GYJfdpYpGTKqPVgaASPBSs3LtgoNcRz0iS4XANj/ds6Ro3rnvajhW7QpcpaHP4y9MBwKQ87T3YXKqdWxVVoKxOxficvtW41vuXjJTm4EeirE7Fqt0KTikyIy9dRnaqjPpWEbSrdjjTAxIFmQO7UKKXFiut49gLp9UucLhBYP5kbaf6cN7ZX+oNSAgBbDro8S3cBvaQGAiZydoGu5Hy+T0SxVUKslMlX7bdcC4z9O0+D5xuYO1+7dwbqqn1QOAmu965PMIXmDzcMLzfp1uft+PaJzt8/9au8QZ2zOkyU2RuOqF+YUCCYkJmiozGNl7AUWiBpXMAYPIoGZIEPPCWE+9tcGP7IQVZKRJy0/q+c3M4XwjTketwAqoIrreZn6H1f9h00IP1xQrsrr6VawICe1Fw3FF3XesL56UHL+gqqsChehVjI+wfocv2HhtHWrPcvtB3MI7J1AMS2nvWbB/675leluWrnaEDEl3PsbPHmZCdKuGDjW4IIVDVJODy9K1/BKAFJDKSpKA69RTs233a3+TCeRYAQFaqBI8KtAyDcRcJPThvRMkmgBkSPdlbqR0Xjp1gQpx5eGcyBY6D9QcUX4mRgQ5I2FIkKKq/eTEFU1WBAzUqJufJvo1hw3kedrBGG2c7vRvnWgzrITH8gzv9VVanwuM9DAz3kk37KlXsKFd9zaxbHQPfP0IXuKnY7hQ4WKP08hNEwRiQoJiQlaJNwPTaikSBukb2x+WY8NiPEmGWgV+/7MDOchUzR5sibmgNAGl6hsQwThWmI9cWogHYpFFa/4crHrXjR0/aAfStoTUAFHQpN0ahbS7x4LEPO+F0j6zPZ1un9v8Ub3m6PG8ZEz0gUd0s4Fb6VscfAHK8O9VrR1Bt+r7qliExjILWB7yLIit3hS7b1PUca5IlnH20BeUNAtsPqUfU0BrQSnIWjZGxu0KBWxn676MR9J4wk/K04LaeVTIcgocvr3ThsQ87e7yPHpwf6IBEQaYMs8yARE92ewOV0wtN3p39w/e9KqlVkW+TkGaVsOFAQIbEQJds0neqD+PgTn9UNgk4XMCkUSZk6Ivow3gepgckdh3WPmtGZUikJgJmeXhnOfVXsbdUmCRpmXnhSlgOdR5F+AIEe73H+HYDMySyUiS02AUe+aATJ/++Hec80OELwBFFggEJiglsihOZpnZ/tHsk6bp7EwBOm23B+3cm4ey52oLwvMl926luMWl1D5neSqH46rwm+Mfdz86IxzPXJ+K2c+Nx7jFmnD3XjGP7mCGRniQhMS64FwV19/QnLjy53IUfPmEf1oskXfkaz3mPd1kpEsyyf/e+vng5po8ZEjne5tjDYZHTKIcaVCTG+ReE0/Xdm8NgsURfFKlsEr5JuU5VtfJ0Xc+x5xyj7dj/YKMbJd4db+Ny+z5tKBptgsvjr91MwcrqVJhkf3+h7JTh81n91+dO/ONTFxw9lD0rrlFgltGnDNdIWEwSRmfJDEj0YE+F9t5MK5BhG8bNmPWScxNzZRw70YSd5arvnGpLGdilEP3xWIY4NL2p8KQ8GamJgEkeHufYUNo7BWpatNe2p0KFWxFodQhI0sA3F5YkCRnJwf04nW4xbBfdj4Q+9maPNcHpHh7n2FDq2wT0P/vuCn/vEuMyJLRj3tOfuGCN08rivfmty5DfRcMTAxIUE/SU2eF6MTxQrnjUju8/3IEO58h6n8LVPsxIlvF/P7Dik98m4wcnxfX5cTOS5WGx+9VILo/As585UT/CGuL6dzH5vxdnlnDSTAuuPy0eD11jxf/9wNqn/hGANmnIz5BR2cRxF44QAjsOKUiMAzaXKLj0/zpGTApw191zJlnCvMkmrNqt4JOtbhzyBiT6miGRzQyJXpXXqyjMlH2ZdsMlINHUoaKxXSDfm521sksfCbtLK0/X9Rw7a4yMMVkSPtzs9mVY9DVDAgCKxmhB2x3cMRfSoXoVhTYZFm9PmCzvZ7V+iG/QaWhTUd0s4FGBHYdC/+1b7QLr9iuYP8U04M02AW28Hq5XmZ0Txp4KBblpEmzJMjKTtdK5Q30B06MI/GlZp29HOoCAknMmHDvRBEUFvtiuHQezDOghAXCzUzj7vYFprfSuVtJvOLxXTR0qPt7iDvpeife8mRgHuDzAwWoVrXaBlARAlgf+eGdLltDk3cBT06xi4f+24f/ecw747xmq9lepkCXghOnaRsbhujGstsX/edp9WIHTrR3/AjfYDaRzjzHjtKPMePRHifjsnmRMGSXjg03uEZfhTkeOAQmKCXp0dTjXL+2vmhatbEJJrYoHlvWcAj+cuDwCnW4gJTH8fcb0scGrztblQrisTkXzEF98GmhfbPfg4fec+N//dA75iWpf+Eo2WQf+Ai7fJqGqafimC/dXVZNAQ7vABcdZ8IdLE1DZJHD1Y/YRcXHb1qntnkuK93/vgSsSYUuWcNe/HVi1R1tE6XPJJm+GRO0w3RHWXx5FoLJRBGWeZAyT+tb6osjFC+KQYEG3xtZds3J0kiTh7LkW1LUKfLTZDWu8v/RXX8wc7Q1IhFmUHslUvSdMwOd5uJRs2hXQN2RTSei//ec73HArwJlzLIY8h/E5MjwqcLhheC489YfLI7C/SsW0Au3zaUuR0OnWApRD2a7DKl5a4cLzX/hfSGDJOb3M5n5vpphtgEs22fQNdpzPhqRn6Okl6jKSpCEf9AeA575w4RfPO7A94Dx3wLuR5juztDG3o1wxdKe6LSBD4s01bnQ4gWc/d2HNvtC9o0aa4moVY7JlTPRmeg7XxtaBG492Vyj+jH+rMb9vzjgzHv+xFWfMscAkS/jufAtaHcBn2znuKDIMSFBMyOQFXK+2lWoXNqmJ2oXGx5vdvfzE8GBUvU1Au3hr7tCaqbfaBS56sB23Pm8f8N8zlG08qI27Fbs8+HTbyLm4MHLc5WfIcLp5vAtnm3dCN2uMCZcujMPVJ8ahoV0M291MgdodAknxwbvnctNlPHRNIjqcwOfbPTDL8O12j9RwWeQ0SlWTtpN7dGaIgETH0B53enbDtAIZx08xY9NBxXd8A/zl6UId684+2uK9j7aY15c+TbrcNAlZKRIzJEKoatZ2LgYGJLJSh0fJpsAd6pvDBCQ+3uyBSQZOndW3XkyRGsfG1mGV1KhwK9pxARg+vQ9KarWxtv6Av1+OXnJufI6M6QUyrN6Af3ICEG8xKkOCYy6U4moFeemSb1E+I2BX/1C2r1J7DZsO+udJeqnE87zlD3cd1s69aQZsdAK097K9E+h0Cbz5rQuZyRKsccCdrzrQah/an+v+crq1sm2T82QUeK/zhuucQs+QSIrXskL0IJVRPSS6Ov84C8wysGztEI9u06BhQIJiQmaXHhLf7PHgr+90QlVH9gk00LYy7YL6bz+0wpYs4Xf/daBymJ5MA7WGaC48UDKSJagCaLELfLTFDbsTWLtfwfYyLpzoNh70IM2qBcLuX9o5YnqYtDq0/ycbkOKa723e+eEm94h5P/tiR0BAAvDXVh8JNZnbOkOXp1s41YxbztJWUQoy+54RZo2XkJzg3zlVXu+voU1AWX333hy+ptZDfPemvigyMdeEk2aY4VGBb/b6F0182WAhshCn5JsweZT2nhxJuSZAy7SYOdqEfZUqXJ6h/V4ONL0E25jAgIR3g85QL5O467ACSdJKs2wuUbplBLbaBVbv9WD+ZH9z24E2ngGJsPZUehtaB2RIAEDDEF8cLvX+raubha9heqn3czYuRzt3zh2vveZMA8YdN9iFp6gCB2pUTMoLPs+22LUsxaFsf7X2edpS6p8/HqzR+gMtmGpGmlXCznLV4AwJ7X19b6MbVc0Cly6y4M7vJqCqWeCPS0dOZYVQSmpVqEI7HxVmau//cM2cq/Fe5y+aZoZb8Y9Jo8ZdV7ZkGUtmmrF6r4KqpuH5HtPAYkCCYoJvAtYm0OYQuP1lB577wjWidmT3ZtshBdY44PgpJvzpigS0OoD7R0DppjbvwrBRGRKAVuv1rbVuWLz9iZ/7kjU3Aa0h254KFcdNNOO2cxNQ0yLw+Ecj470xMkNi9lgTJAn401tOLLirDT991u4Lxg6m7WVKTC56bfce6yZ6J63Z3h3D9UN8x3Ao/17lwtZS/3mu1RE+CHbjaXG4fJEFly86svIm2akydh1WccFf2nHafe248K/tQ373/0Ap9wYkRgcEJPQeEs1DvGTTwRoVFhNQYJN8tZMD+0j0lCEBAOfM1cbb+BzTET+HojEy3Aqwj42tg+hN6scFBCTiLRLSrMMgQ6JcwfgcGQunmtFiF92CAl94yzWddbQx5ZoAf0CilAGJbnYf9mdOAcMnQ0IPPgDAhgPaca6kVoU1zt84XS/blDnA/SMAIN0qQZZC95AQQuC3rzlw/dMjMxO7tFaF0w1MzfefS2ze82zLEN7B3+HUSj4CwJaAbLCDtVpfqjizhJmFMvZUKGhzGLdTXZ/TPvuZE5IEfO/4OFx8vAUnF5nx7gY3Pts2MiorhKL3LpmUZ0K6VUJS/PANSOgZEifN0I5z6/Zrx8HBypAAgIvmWyAE8M76kTvmKHIMSFBMSLNKMMnaBdwTHzt9za2f/sTJOuvQdpVsP6Rg5hgTTLKEJTMtmFEoY+cIKIFgZIaEfvG28aCCLaUKzp5rwQnTTVi+xTNsL1T6YluZAlUAcyeYcMlCC2aPM+HlFS7srRj+466t07hxd/wUM778fTJ+d3ECjp1owpc7PHjqk8FNbXW4BK58rAMPxVjDO1UV2FmuYMZo7VgHDJ8dw101dwjc+2Ynnlzu/9u397B7TpYl3HNJIn54cnzI23szJktGi12gpkVg4RQT7E7gP19zsgDAly0SmCFhMUlIScSQ7yt0oEbx7QwuzJQxKU/Gqt0eXwZqb8HXi4+34KQZZpwx58jL6hSxj0RIpWGa1GelyEM6I6zVLlDeIDCjUMbR3t3oXcs2fbzF2HJNgHaNl5LIDIlQdh5WYI33H/N8vfyGeAC2pFb19WDacEDxfW9cQMm5eZO8GRIGBCRkWYItWQqZIfHOejfe+NaNlbs9IzI7dluX7FfAn4k4lBtbH/T2xTDJWhm+6mYVbkXgUJ3q61cwc7QJnW5AFQYGJLzBnUP1AounmVBg08b8Hy5JAAB8OEJKPYeyv0obe5PytPekMFPG4WFaZaK2VQvA6oHXdcXeDAmDmlqHcuIMMzKTJby11s11POoVAxIUE/QLuJ3lCl5Z6ULRaBlXnxiH3RUqvtw5eFkStS0qrn2yI+ZKWRyoVmF3ajurdXnpMupaBZRhVtaqulnFFY90oNib/tpuaA8J7RD4wlfaguBF8yy49uR4qAJ48avBWyDeU6Hg/Y2xd6G4yds/Yu4EE2RZwv+cp703n+8Y/plLRmZIANrn94oT4vDcz6yYUSjjzW9dg1pHt75Vq11+sCa2FghL61S0d/oXMAEga5j2P9B35evHOkALhBlRJgwA7rssAf++1Yqv70vGMzdakW+T8MpKFzpdw+t9PRLl9VppBb08mG6oN9zsdGllS/RFEUCbKNa1Cuyu0MafXp4uXCAsK1XGMzdYfU1Ij4Te2HokbKLoi0P1asieMNmpEuqGcAB2t3fTwoxCU8iARKtd4Js9xpZrArRyYeNzZF+GRH2ris+3u4d8eZj+8igCOw4pOGqMydevyOYrnTt03xtV1erEzx5nQmGmhA0HFdidAtXNwtdPBACKxphw1FgZC6YYEwyzJUvdAjuHG1Tc96Y/q/1A9cg7FurlcGcFzGXTfb2ahu6408s1nejdkb6lREF5vQqPCkzwnntnBFzTGlayKSDA9v0Fcb6vc9K0MkX7R3CG4v5q7VyrHwcKbLLWO2yInwvaOwXszuDXUNsikJ0mY3Smlgmiz50Gq2QToG3oOe9YC8rqVWw/NHLHHUWGAQmKGZkpEqqbtcaSd1+cgJ+cGoc4M/DU8sHLklizz4PVe5WYS2vc7t1VclTARVxumgRFHdqTh1BW7PJgU4mCDzdpfwM9QyLZoB4SgFbSIj9DwrxJJhw/xYTpBTKWrnEN2s7Yxz9y4tcvO2Jux9SmEg/iLdqiAgBf3deRUBNSH3dJCcb+HkmS8OPvxKPTDby6avCOO/Vtei+B2BpzoY51esmmumF2rCv3ZmFVNGoTCqdbCxIZFQTLSZMxd4IZZpMEi0nCD5fEobFd4O11gzfu6lpV/DEGe9EcqlcxKkN7XwJlJMlDeqGktE6FEP5FEQA4fnLwArGRQX9dTpqMnDQpKEOiqYM9Jcrq1JA9YbJSJbQ5MGSDhXpD6xmjTchNk1Fgk4ICEnq5pjPnGFeuSTc+x4T6NoH/ecmOk3/fjpv+6cDPn3MM2fd2IOyvUuFwAbPHBZTO0XvmDOGd6jUtAg6XVqrr2IlmlNaqvrJNgT1w4swSXr8tGVecEBfuofolM0UKamqtqFop4g4ncMUJ2pgvrh7+19FdbStTkJksBQVg9Y1hQzlDQv9bfv947W+7uVTBAe/3JgRkSOiMamqtf4azUiScXBQcbJsyyoSSmpF7zi2u0jJF48zae1SYKUNRtV4zQ9k1j3fg588Fl4CrbRHITZMgyxKmF/rH3WCWbAKAU7zZj6v3Dv9NjNQ/DEhQzNBPpBfOs2DOODNy02RcfLwF2w+p+Gbv4OwkqfVGkWMtvXtrWYiARLr28a1pGdon0670izh9YdLIner6mAO0cSfLEiRJwrWnxMPuAl77ZnCyJKqatEWjWMrM8SgCW0oVzBpj8l3ApSdJiLdoWSzDzfYyBW986/97tzkEkhPgKxtkpDNmm1GYKeGVVa5uO12MovdjaOoQMbU4rO+kCUzpT4oHEuOGX8mmwM/7wVq1x+bCRrj4+DikWSU896Vz0DLtPtrsxisrXVi+JXaC/kIIHG5QMSaz+yVxujdDYqimnB+o0RdF/J8n/bOln2ONDPoHKhptwv4qFU63wPItbpz423b8+a3h3wcrHEUVOFSvBvWP0GV5y+fEWtmmL3e4sbmk98WFXeXauJvhbZh89HgTDtSoaO7QAq8vr3TBJAOnHWVcuSadvhD9/kYPZo8z4dRZZnyxw4MfP2Uf0nXr+2Ort8lpqIDEUG5qrWfCjMsx4biJ2mt741vtXNOfHjh9ZUvWAor64u9zX7iwqUTB1SfG4ZoTtSCIfmweKZxugb2VKmaNNflKZwFaFiIwtHs1HajW+jSdMENrXr2lVMHB2uCAxOhMyXdtZ9RO9dGZMswycNmiuG6bKyaPkuFRY299YzDYnQKHGwUmj/Kfawu92bBDuWxTZaOKXYdVbCpRfNeonS6BFrtAjrdfzvSCwMycwX1+c8aZkBgHfMOABPWCAQmKGVPzTchIknDbuf762D/5TjwspsHLkqhpjs2AxLYyBTlpEvLS/R/Z3HTtZFPbEhvP1e4UWFfc/5NOsbfO4/ZDKoQQAT0k+v3Q3QQGJC44zr9T6syjzchJk/DmGtegjrtYary4r0orE3bMBP/FjCRpY7C6KXYmDv9d7cKb3/Y/cPToh5347WudvjIZbQ4xaLtJzCYJPzo5Hs0dAkvXDs5CbeBCV3kMBcK2lylIT5JQmOl/7yVJQlaKFHOLc/0V+L4fqFZ8fUuMKtnUlTVewpUnWHCoXuCzbYMzYdADYesPxE65ivo2AbsruKG1Lj1JgtMNOAa3xcuA0UuyBZZsykjWdqzvGISgf6CiMSZ4VODh95z45QsOuBXgo83+XhYjTXWzgFvp3j8CALLT9DJ1sXNsrmxUccu/HPjdf3sPIu06rKAwU0Kqdyfw0eO9pUxKPfjta53YWa7ix6fEGVquSff9BRbcclY8lv06Ca/8PAmPXZuIHy6Jw8aDCq56rAOtIzAosdkbkJgTEJCIt0hIThjaTa31uZueIQEAX+7oniFhNF8/jjatrO6LX7lQYJPwq/PiMTpLRpzZv/lqpNhTocCtBG+sAwIyc4ZwJuL+agUTcmVYTBLmjDNhV7mCPd6ydRO8gTBJknzZ5qkGLQznpsv49HfJ+NkZ3TN/pngbiY/Esk0HarRNf4FlJwu9G1AqhnC/yPXe7C+705/poZdnyknTXt+0Av9xL9WgzJxw4swSjptoxpYSZdA229HQxIAExYxfnx+Pz+9J9h1EASDfJuP8Yy3YeFDBrsPGnzRqvZO/WFoYtjsF9lepQf0jACDX+z7VxEi64X++duGax+0RNzw+WKPg8kc6ujWPLvbuGmruEDjcIAzPkLCYtEX3wEUBi0nC2UdbcLhB+LJTjOLyCF+t2bK62Bl3G/X+EeODx11+hoTKGCrZ9OTHTjzxceSNmXeWK/hqZ/dF/72V2mvaVqbvGh7cepvfnW9BRpKE5790DkpN08CFrvIYuSB3eQR2VygoGi0H7aADtDr2dcMsGyzwfS+uUtHmreU/mJOGK0+IQ7wF+NcXg9PcXA8qbTwQOzumykM0tNZlDPH61gdrVEgSguqnA9qi0MFaFe2dWtBfkuBrBGuUmYXac3hphQu5aRLOmWtGU4fAjvLYOP4MNv18H2rcZXtrgdfHUN+c5750waNq5Ul6yqqzOwUO1qq+xTcAvj4Sf1rWiXc3uHFykRm/OMfgAeeVmSLjpjPjfc9HliX85qIE/OyMOOyvUvFRDGVrDZatZQrGZEm+cjm6zGR5SDe11pvEj8uWMSZLQnaqBI/38NL1GGgkf7aJwIYDCurbBM4/1oKEOAkmWcKEXNnXd8BIQnSvLx8t20JkvwJDv6l1h1OgslFgorek7ZxxJrgVLRCWnSoFXc/5AhIGXuONypB9fWECTfFmB+yrjJ3NIINFf8162WEAKNADEkM4Q2Ldfv/fUi8bpm9SzfH23gs8Dw/WZqdAC6dqn4cNMXTNT7GHAQmKGbIswRrf/WB5yUKtJuM7642fNNR6F7zq2/wL4dG267ACRe1+EZfr3UFXEyMZEvri2t4IL3aWrXVjc4mCTwP6dbTaBWpbBKzeeeq2Q0pAhsTAn0jjzBKeucGKv1zVfbvKOcdo4+6DjcaeRGsDFllLYyggsfmgB5IEzBkfXFIhL11GhxMx8flQVYGGNoGqZhFxPejfvubALf9yBNVRbWpXfbtK9FIGbQ4xqAGJxDgJV50Yh8pGgQ82GX+sC8qQiJGAxP4qFS5P92MdoDV5bewY+g3oApXXq5gySkuxL67xl2wazElDZoqMc+ZasK1MHZSScfoCa3mDiJlzl/66Q2VIZAzx3ZsHqrX+SIlxwWOqaIwJQgA7yhXtWJeAkIsYA2nWWBPMMjA2S8artybhisXaLs5QAeKRQA9IhMqQyEqNrZJNDW2qr6ShEP4eEaHsqVQgRPBCyJRRMqxxwKF6rWzGg1cnGj7eenO1t3TO2v0ja6GkqUNFaa2K2WO7l8uypUhDdmEYAEpqFcSZtY0zkiT5yjblpklICjG/NEqmN6DY2Kb6yhMG9kuZmCujslGgI0ywQGvE3f/z44ebPZh/Z5svUy6afA2tuwYkfEH/2Lge6KuD3oXgyd7d93rWUac7uHcTAJx9tAXTC+SgfhKDZVyODItJy34fad7d4IZZBuZO6J4hcbhh6B7v1hV7oJ9GD3gDnLVdMiQm5ml/9wQLfOWXB9OCqXofiegfgyh2DauAxIoVK3D66acjJycHKSkpWLhwIV5//fU+PUZrayvuvvtuTJs2DYmJiSgsLMRPf/pT1NbWGvSsqTdHjTVhfI6M9ze64TZ4MSpwgSRWyjb5+keM6xKQ8JZvipWGTPrC+sEI3ze9ydH2gAyEYu9Fs37hvr1MQZvD2BPpwqlm34VJoKLRMsZmyfh4i9vQ+uqBJbdiJUNCCIGNBxVMypO7NV8b5W1GFwuNrZs6hG/3WyR1QBvbtXqbbsWfEQEAewMu0LeWabU4BzsgAQBXnRiH5ATgyY9dhi+8B+68PRwjJZv0mvazxnafrGWlShAiNnbRtXcK3PgPO3aWR3aB3d4pcOLv2vBcQBaCyyNQ3SIwIVfGuBw5qGTTYI+7U7zND1fsMn5hTm+mDgAbY6Rs06FIMiRiYNz1laIKlNapmJjb/fOkLwrt8Ab9B2PM2ZJlvHZbEl7/VRLybTJmjzMhzQqsHIRx98V2N178anBKf0aqLGA3d1fZqbFVsunFr1xwurVzFBB83daV3j8icNHNbJKwYKoZmckSnrrOGpWdml1lJMuYmi9j3X4lpsaF0bbp5ZrGdz8uZCZrAYmhWkattFbF2Gz/DnG9bNNgZkcA/oBEXavAJ1s9GJ8jY0q+/znopWMOhukj8ee3O3Hen9v7nd2w8YAHbgX4Ykf0g27bDikYmyUjPSn4sx9vkWCNH7pBfz3TRd99P2usybdIPKHLuJs11oS3bk9GdurgL79ZTBLG58jYX2X8dddHm914Z31s1LncV6lg7X4FZ8wxB73vSfESMpKkblUahorKRhXlDQJLZmrHOL0nTY03kKlvWo0zS5hWICMrNTrn3CmjZGSlSGxsTT0aNgGJV199FSeffDK++uorzJ07FyeeeCI2bdqESy+9FPfcc09Ej9HW1oaTTz4Z999/PzweD84991wkJyfj6aefxty5c3H48GGDXwWFIkkSLjjOgsZ2gVUGTlyFEKhrETB7PxWltbGxWLKtTIEkaQ0hAyUnaBdxsdJDQp84R1IXVV8YBhBUEqnYuzB86iwzUhK1BcrBWizpSpIknHOMGXWtAuuLjRsLgQGlWMmQqGwSqGkRQf0jdKMytA9ILAQk6gIW1Q9F8N59u8//dwxcTNbTeRPjtIUWuwtwK8bXVO8qzSrhh0viUVavGp4R1tCmNT1LsMROhsTmktA76AAgO4aavG4rU/DVTg/e2xDZ32jjAQ9qWwS+3Ok/fx1u0Grajs6UMSlPRnmD8I3nwR53C6aaYTEBKwZhp3pdq/AttsZKCnd5vfa+hwpM+xpuDsHFkopGAZcHGJ/b/XXNKDRBkrRz7GD2yykabfIFuc0mCYummbGjXDV84f3vHzvxwFtO/Pvr2MnGKKvXGqHqQf5AWan+Bc1oa7UL/PtrFybkyvjFOfGQJW1xMZzd3uyJ6QXB4+7haxLx8d3JIT9n0TJ/shn1bWJENRjWr7lnj+t+nrWlSFBUoCUGMmD7yuURqGgUQb0ijpukvcaJeYMckPBm1n2y1YP6NoEz55iDylDqi9fFIco2CSGwcpcHbY7IM87D0TfWRXsxsMUuUFqrYtbY0H+HjCRpSAb9AX+pHH2MJSdIvuDThLzBz4ToyZR8GRWNoseSewPhz2914t43Og3dzBepV1dpgRE9mB6oMFMask2t9f4RZ84xIz1J8pds6pIhAQB/vToRj19rHfwnCW0tZcEUM/ZVGX+dR0NX7FwV9kNNTQ2uu+46JCUlYc2aNfj444/xwQcfYMuWLcjNzcV9992HTZs29fo499xzDzZt2oRrrrkGe/bswRtvvIFdu3bhl7/8JSoqKnDTTTcNwquhUC44zgJJAt5aZ9xksrlDazBY5F0Mi4UMCVUV2FKiYFKuHHJHWW6ajJoYqauuT5wjed/0heGkeG3RpKEtOJgxeZQJRaNN2FmuoKlj8BZLujpnrrdsk4EldPSsnLFZMpo7BFpioMHiN3u0C51jJnRP6dcbq1fFQGZO4GJNWQS7/Ffv8U/KAgMSepO3s462wO4CNnn7Z0QjEPaDJXFIswJPLncGlZUaaHVtKrJTJYzO1BbDo83uFPhsmxszCuWQu8d8C3QxEIDVL6rD7W7sSm/gvLNc8U3QygPKBE3MkyGEf9dxcsJAP+OeJcVLOG6SCeuKjW08p6gCje0CR401ISdNwoZYyZBoUJGZLIU8x/pLNkV/3PWVnsI/MURAIjlBwqRc2RuQAJKjdI49aYZ2jlm129jNJnoWzJ+WdWJNjJToKatTUZApw2zq/t6nW7X+VrEQkPj31y60dwLXnRqH5AQJE/PksBkSLo/At/s9yE2TfGWndAlxUlTOqT05frJ2vb92f2wciwbDlhIFCRZgan7344K+kD4UG1sfqlehiuCMo8mjTHjs2kTccNrg9CvR6U2tV+7WFw0tQbfri9ehNnBVNArfRqWd/eyvo/dD3HRQgdMdvb/pjkPhN5sAWs+NIRuQqNICy4EZlnrZpq4ZEtE2eZTe2Lr78U5RBX7w945+9xNrbFdR0yLQ4Yx+A+0Wu8C7G9yYOVr2/U0CFWbKqG0RUf1sHCm9f8S8yWZMzJVxsEbL9NM3qWYHZESMzzFhemH0gmMLp2m/e82+kXOepb6JrSPlEXriiSfgcDhw8803Y+7cub7vT5s2DQ888ACEEHjkkUd6fIzW1lb84x//gNVqxSOPPAKzWZskybKMBx98EBMmTMC7776LAwcOGPlSKIxRGTKOn2zCVzs9hi0M6Av7x03Sdg7GQkBi/QEFNS0CJ87ovjAMAHnpki89L5pUVfhKwJTVqb2Wm9EXhq84QduxoDcS3l+tTZQKbBJmjTGh061dUEdrEjsxz4RpBTI+2eo2bHFYb0o+zzsxjoWyTcu3umEx+ReLAum7OQeivm1/BWYH9ZYhIYTA6r0ejMuRkZMm+SZIALCvUluQ/M4s7fV+7Z1EpgzywjCgBUGuPSUeFY0Cy9YaEwgTQvu8ZqXIKMySUdmoRn0n0ydb3ehwAt87vvsuJsB/cR0LGRJ6eboDEdZl1gMSdqd/geCQNwg0Jkv2lW/YUuodd1E43p00wwyXB4Yu1jZ3CCiq9rc8doIJ+6vVqGceqKrAoTo1ZP8IYGhnSOyu0MbapDC7g4vGmFDZKNBsj17Q/4TpZkiSsWWbWuxAeydw7EQTEizAL553RL1Mg6IKlNerGBtm3EmShKwUCfUxsKPwtW9cGJUu4VxvX63ZY02obBIhdzu+stKFykbhu7aLdcdONEOWRk4fCUUV2FqmoGiMCZYQgTCbdyF9KDa2LvFuEOiaEXb6bAty0wZ3uUNvai2EVi5qSpfgz5gsra57cYiAxMaDoTfO9JXdqfVXkyStn8Hm0ugtBvZUjhMAMpJkNHaIIVk6rbhGwYTc4MDyFYvj8L35lpBZ5tGkN7YOFSjYdVjF2v0KXl/dv3nHngr/Y+ubu6Jl2VoXHC4tOyIwQ0lXYBu6ja3XFXswNktGXrq2qanFrs2PalsE0qzaBoBYsXCK3kfC2PPsmn0eOCLsJ2mUT7a6sengyLieGEjDIiDxwQcfAAAuvPDCbrddeOGFkCQJ77//fo+PsWLFCnR0dODEE09ERkZG0G0mkwnnnXde0O+iwXfhPAvcCvDhJmM+6Pri5pgsGfkZUkwEJPQFye/Ot4S8PSdNazBsdPplb5rt/lr+bgU9TvYDF4bP0HtFeC9WD1SrmJCr1X89KuDCNVqLJYCWJdFiB77eY+y405vvRTsg0dShYs0+BQunmpFq7f6++zIkmqI/cehLhkRJrYqqZoGFU02YOdqE/VUqnG6tVvL+KgVT8mXfmNP/1tEKhF11YhwykiQ8/YnTkJ07bQ7A5QGyUrQMCbfiD4xFy9K1bsSZ4Vv06krfbRsLO4b1gERlk+j14tfuFNh5SAu0AloTYcDft6MwU/btYD9UH50eEoA/+Lhip3EX0nrQOitFxjETzRAC2FRi3O/rdAlc+WgHlq0NXcu4vVPgpn850NQhMDvcQomeITEEF+i+3OFGSqI/67MrfbeqENE71tmSZRw1xoSv93gM6xGml6RbPM2MB69JRItd4Nbn7VFd/Kpq0jJyQ/WP0GWlSlEPwLo82m7tOeP9C9j6omLXLImmdhVPfeJEvk3CD5cMjYBEqlXC9EIZ64qVIds3QbetTMHmXo6nB6pVdDgR9ng3lDMkSnvoyTLYrPESEr0fga7lmgCtXN14b++orvTMweSEnpvH90afS5w4XTu3fxvFsk3bDykwycCMgvDnWacbcMRG24GIdTgFKhtFt5JgU/JNuP+KxJhaFAa05wWEbmytZ8aX1an92nC2u8I/Zo28vuuNogr8e5ULGUkSzj469Lyi0KY3to7+ek9f6P0j9I2Mk3L9GVc1LSKoXFMsyE3X5jmr93oMu+7aV6ngh0/Y8fhH/cvw6Y9Ol8C9b3Tithcdhve8HW5ia8QeASEEdu3aBQAoKirqdntGRgby8vLQ1NSEioqKsI+zc+fOsI8BADNmzAAAbN++vb9PmY7QqUdZYI0D3l5nzBWLvsiUk6pdKJbVqVGdoLR3Cizf4sbscSZMDFOHUm9aFO0+EnXe925UuvZ8eiplErgwPDVfRpxZq2nbatf6FugppYGpvSmJBj75XpztLdv0/kZjdqtXNwvYkiXf6y6Jcu+SL7Z7oKjAGXNCZ+UkJ0hITYyNHhL6Yo0tWfKV5AhHLxO2aKoZRaNN8KhaY+vDjQJ2FzBllAnZqTLybZKvnnS0AmFJ8RJ+cmocqpsFPto88ONObyyclSqhMFN7jdHsI1FWp2J9sYLTjjJ3a6Kuy06JoQwJ785gIXrPpNtSqsCjAhfN04OveoaEv378uBwZpoCrsZQoNHwdl2PC2CwZK3cZN2Go8/7tslIlHOsNwBrZ2HpvpYKNBxXc92anr0SWrrxexeV/68CXOzy44DgLbjsvdEmP1EQJkjT0Gm7WtKjYfkjFidPNiDOHHk+B59hoBv1PnGFGe6e/h8xA0xcbCjNlnFJkweWLLNhZrvo+i4PFrQhsLvHg4y1uvLJSu44d08PiaXaqjPrW6DYY1gNxegkaADjKO2669pF4/GMn2hzA/5yXgHhLbC3E9WT+ZDOaO0TIRbqBVFanGrp7845XHLjmcbsv0y6UnvpHAFoPCSD2MiQi2ZihZx8OdgPrcPQsia7lmnST8mQcbuy+qWHDAQUFNgnHTzajuFpF5xGOGf3a5Jy5ZqQmAt/uG/jFYZdH4L43HSEDKzohBLaVKZiaL4ddoNczEYfaedZXZjjGekWEk58hISne3zcvUODu9f5kjO31ZkiMSpewyaBzeiRW7vKgvEHgkoWWsOcj/fxbVj+0xp3eP2LeJG2erq8R7a9WUdeiIict9s6/C6aaUdMisMWgTK29ldq4+3iLe1A2mzS0qWjtUmJ76Vo36tsErj0lLmQGIoUXG2ftfmhqakJnZydSUlKQlJQU8j6jRo0CoPWaCKeysjLovkfyGGSspHgJp8+xYPshFQcjLJXRF3rJppw0GeNytHJB0ayT/9FmNzrdwPfCZEcAWtQZiP7uZr2J0nxvWt7BHhbpVu/1LwxbTBJmFJqwvUxBcU1wvevcdNl3Uo3mYkmBTcaxE034bJvHkP4ONS0qctMk/4VRlDMklm/xwCwDpxSFH3d56XJMlGyqa1UhScDR47XSIz2V1Vq91wOTrF3AFY3W3usdhxTfhbmeUj9nbGAgLHrj7vxjtff/WwNqbupZBlmpMkZnRn+HkL6DPVy5JkBbKJEkxEQJk9qAvj2hakAHWl+sTRwumh+HNKu/VFh5vYp8mwyTLCHOLPnqD5tk+HZWDraTZppR1Wzcwpz+t8tOlTA5T0aaFdhgYEq/Xg7D4QLu+a/DN0nZXqbg+//XgeIaFf9zfjz+fGX4BVSzSQvADrWFki+3a+PuO7PCH8enFmhlQ4DoBv1PmqldN3y5w5jdlL5+Ld7g60XztA+YUZsMmtpVvLzShU+3unGgWkFZnYpHPujEKb9vx+WP2PGL5x144SvtmDdzdM8ZEh4VUe0rpQeA9Z3zADBplIwEi7/UJqD1K/nvN24cPd6Es44OvZkhVs2fpPeRMG43775KBef8qR1/WtZpyON7FK0EmFsBbv6nw3d9JoTAp1vd+Os7nbj3DQdeWqGNu3ABCf3vrPd1iwU7yhUcc3sbPt/e8+e1pFZFepKEjKTYWNqYXmDCrDFyyF4dgLaIKETwBq76VhUltSqOnWjGjNEmKGro3eyR0DNGJuSZMH+yGTsOqQN+LNlcouDVVW789Z3wu5K/3qOgrjV8FiLgz0RsjLFAWG/0puThyiLGGknSNr/tq1KDFm07nAKbSxRML9BeR39q/e+uUFCYKWHxdDMqG4XhZaVrWlS8s97VbRH6lVUumGTg8sXhL6jHxci8u6/0/hHHec9d+vjbVqrA7gJyQvThi7bvzbfAYgJ+9aIDje2h3+8d5QpO+l3bEWWG6euClY0Cuw4b+/cUQuCKR+y45G8d6PD23XMrAv/63ImMJAnfXzA0MkRjSeyN2D7q6OgAAFit4bvHJyZqM6329vYjfpxIHsPpdKK1tTXoP6czeqlDw5G+SGfERFLPMshN1zIkgOj2kVi6xo0Ei3+Hfih6hkR1tDMkvAtN873pgz1lSAQuDANa6nirQ9uZD2iTXZ2+gzPajRAvPt4Clwd4b8PAjjtV1eo95qTJSIqXkJMmRfXCqMUu8O0+DxZMNSE9Kfx7PipDRlVTdHduAlpmji1JwoQcGaoIv6juUQTW7vfgqLEmpCRKmDlaG1c7yxXfZE9PZQ6cqEdz3GWnaimu6/b3vGP98+3uPpdsa/AuMmWnSL7a+b1lmBhFUQXeWudGfobkazAaisUkISNJipGSTaqvBFNvwfH1BxRY44EZhTKKxsjYXaHA5REob1B9C6SAf0KRnCCFrHU7GE6MsGzTmv0e/M9L9j731dEXN7NSJMiyhKPHm7HzkIK9lQpW7/Xgk63uXvsP9YWe6TR/sgmr9ylYttaNtfs9+METHXC4BJ74cSJ+8p34Xt/v9CR5yJVs+my71gcoXP8pAIgzS5jmXYCIZtB/RoGMsVky3lzjMqRXx+FGfwN5ACgao/2+DzcN7HjTvbrKjfuXduKW5xw454EOnPHHdjz9iQsWM/CzM+Lw0DWJeOFmKz77XTLmjAv/98ny7lavjeIxr9GXIeEfH/pGkh2HtDJHiirwx6WdUFTgNxf2/nmKNcdMNMMkG9vY+vGPnPCo2jVk112VA6G6WSubOmWUjPo2gZ89a8fGAx5c/bgdtzznwHNfuPDvr93YX6VizjhT2J4K+t85lhaG31vv9r13PSmtU2OiXJPu0WsT8eqtSWE/D/7G1v5xt9EboD92ogkzC7Xbj7SPhN5TY1y2jIVTzVAFsG6Ag26V3kzpFbs8Ia+FappV3P6yAymJwLWnhG8srmeTDLVeTXp518mjYmfc9WZKvozmDhGUbby+2AO3opXGHp8jY20v845wnG6BgzUqpheYcPR47Xre6CyJv3/kxB2vdGL5Vv/YPlij4Js9Ck49SKwUzQABAABJREFUyuwrMxxKXrqEeAtQVjd0mi27PAJf7/FgTJbke205aRKSE/xZULGYITG90ITfX5KAyiaBnz/nCDl/eH21CzUtR1YZIHAj7Cdb/T8vhMCHm9wDGhiraBQoq1dRWqvi/qXaJoP3NrhR2STwgyVxSIyxUm1DwdDaxhKCyaQd8CK5AFbV8IMx0sfp6TEeeOAB/OEPfwj63h133IHbb7+91+c23DU1NQ3I40zOBGxJJry33okr59sxkPOeinoZZlmC6mxGVqIEwIRdpW2YkWPcBVJxDZCTCqR22Z1YWg9sKTXjzKNUuOxNaLSH/nmtsocZpVUdaGwMHywz2qFq7f3KsbYj3WrCvgoXGhsd3e7nUYA1+0yYUQC4HU1odADjM7WffWutE4CErIQ2NDZq95+ULeFzmGCGA43h3oQeDNS4mzcWSIo34b9fO3DWzHbfuFt7QEJTB7BkuvAtUPZFYwfgVsxIT3ChsbETBekm7K8GGhoaB3RsR+rDLRLcigmLJ7nQ2Bg+mGpLlOFWZBw83ARb8iA+wS6qm02wJQGZVgcAE3aWtCLd0v3zur0caO804+gxbjQ2NkIGkJNqwtZSFxrbBCRIyIxvQWMjMN4G6KdG4W71jcW+GKhxd9RoGW9tkLHjYBMKMrrfvr0cuOk5M65cqOKm0yK/2Crzfl4taIPVeww5UNV5RJ+x/vp2v4TaFhOuPUlFc3PP71uG1YSaZhWNR/JHGSBCALUtJsweI7CpVMLu8vDvm9MDbCs14ehxAq0tTZiYJeObPTI+3dgMp9uEnGS377UUpMkAZCTF9+/19WfsTbIBiRYTPt/WiYuP6Qh7v5e+kPHFLhmnTm/GvImRnx/La7XXaFa1z9WMURK+2mnCBX/x/647zlVwwTEDc87dUy7DYpJwz4VOXPOUCX9a5oDbA1jMwN+uVDCnoC2iz3dKvAnVzYjquOuLDqd2nj1mvOjx+gEAJufI2H5Ihqza0dgY/m/em/4e8354goQ/vGXCEx+04KenDmxwtKRahjVOgnA2o9Fb8fM7M2U8t0LGp5uaMb8PYzgSO8pkyJKE35ynorxBQotDu0Y4boKASQ6eaPc0pJLM2nG6pKIV2QnRWagrq9KeQ7wUfI05OVfGphIZ2w404b1NMr7dJ+P8uSpGpx7ZOfNIDdS5dlq+Cev2u1FX3xhUPm8g7KkEPt1mRrpVoNku4bWVzbh43sD+PXeVan+nc+e40TxFwnMrgCsfs0OWBC46RuCS41WkJAIJFiDB4gl7faeogCyZUN3oDHkNP9iEAD7eYgIgYdVuN2rrGmHusm+hrhXYXi6hsd2E4ye5B+U4PRDjLicRAMzYXmLH4onaZ+vrndo5clJmO5ITtNs3Fdtxxoy+z+/2V5mQlQK47E2Ykas91lfbO3DM6IE7vh6o0MYdADy7vA2/Ptf/2B4VuPUlE5o6JPzpEgVJcnPYY4NZaI9zqLoNjbmxEZTYXApsKpVw7Uki5Hxs52Hgw01mLJikItXcMmjHvf6OvYJU7b3euLfFd/32xVZt3BWN6sCcMT3PO3qypxJQVDPGZDgx0dYJwIxvdnVg/lhjNjwJAazYqR0fHnnPjmMKtV4lz32qvZ7z53SisbHnrLT8dBMOVHuGzPXdf76VUNNiwo3fUYKe89gsE3Ye1r5ONh/ZmklPBuKYd/IU4JL5Ml5fC9zznxb8+pzg48Un3mP9N7ud+NGivj3/fRUmjErX+iN+vNmJHyzUfn7Fbgl3vm7CBXNV3HHewIzDVdu1z1C6VWDZWjeOKujEcytkJMUDZxd19Otaerix2WwR3W/IBySSk7XVMIcj/IWTfpt+3yN5nEge484778Rtt90W9L34+HjEx4ffFTCSRDooe3POMZ14eaULlR1pQTWQAaCiUcXSNS7sr1Jx/+WJIZvyhtPkaEdOmkBWZhqOklUA7ahpT4DNZkwtA7tT4Pp/tWH2OBNevDm43NhzX3cCcOGyE5Nhs4X/mE42ac+zzWXc84xEu0d7vpMK0zFplAP7KhVkZGR0C/Ct2u2B3WXHiTPjYLMlAAAWzVSBZe1oaJeQYAFmTsiASdZ+7qSjPHjmCzumFCbDZjuCFX8M3Lg771gHXvvGjSp7GopGm7CnQsHt/+mAWwH+9jFw3rEW/GhJvG8XZiSq7QqADozNS4TNFo9J+Q5sLnMDcemwpRiz46a8XsWD73bif7+X0G2X3NfFdphkD84/Pg0ZyeF//9g8JwAnHEiDzRad2qlCCDS2t2HiJBOmj40HYEdTpxU2W/fj7fa12vM9dY7/8zRrrB1f7fTA7pYxNkdCfm4aAOD4VAGLqQ1uBSjMTYfNdmR/h4EYdycWufHWBgf21iZj1sTuKaD7N2uva12JGb/tQ2TIrmif1wkFacjPMSE7tQ21bWbYDIwubTjgQVWTwHnH+j/HQgi8ucEOQMEVJ6X2+l7nZnRgS6kyYJ/pI9HUocKttGN8Xhxq2zw43CSFfd/WF3vgUuxYOD0BNls8jpvixktfO7CqOAGAG5MLE33jtWi8G1jlQHqSCTZbWr+eY3/en4XT7PhyhwfCkh5UNz7Qnqo2AAI7qhJx5nEJET92u8sOwINJozMQb5Fw5RIVjQ4nUhIk5KbL+Os7nVi5Lw4/Oi10+c2+OtTYhnE5EiYUpuGeS9y45TkHMpIk/POnVl+WVCSy0+3YW+UJeU6LBiEE/v21G1/tdONX5yVgWpdGoWs3u+FRHThzbiJstp5TxxfNdGPZBgcmFqT0eK0Rif6Mu0tOFPj3mg68uR64/sxUZA9gyYHqljaMzpKQmen/XH1/sYLnVnRgxb4EnHXcwF47lTe2Y3QWcM13+vc5HpvnBuCAQyT1+nc0ilNo55ixo4LHx7ypbvx3jQNPf5mAr3Z6MGecCX+8MiVsvxIjDcT5YNG0Tuw87EK1vfu8or9eeN0OSfLgmRuS8JOn7XhviwXXnRF+5/yRaNnrAtCJqaOTccJ0MxyeTlQ1C/zynHhMDdNIOJyMpDa0OWXYbANzHO6PHeUKalo6kBQPdDgllDSnYv5kbRzWtqi49km7rzQfAMybbB20z0p/x11KmoBZbkNFSxxsNq06w46KdmQmC8yZrJ1rctLacKCu79dmQgiUN7ShaIx2PZGRIZCf0Y5NZQN7ndfkcABwY3yOjI+2Abd/L9VXMuvRDzqxpcyFK0+w4LuLUnt8nDG5HgB2uBH6Gj4a/v3fDnyzR8H3FiVhQm7wZ0gIgb+/aIdJVnD391MGfR7Un7E3Z5L2Xle3+9/rjaXa+sfcKRmoc3jw1gYH9oSZd/Sk0nscmjs5GUdNMsOW3I5dlcbNLYqrFdS2diA5ASitl/BtSTJOmWXBh9vaMDVfxilzer9mmzTKjs+3e5CcmhGV81dfNLWreGFVO/JtEn56VnpQqdFpBQ7sPKxteBiff+RrJj0ZiHPt7y4VKG+y460NwBlzk7BkpvY8V+/1oNluhywBeyolWBIzIq5S4FEEDje2YeFUM0ZlSHjtGzcaXWkYmy3j6S87AKjYXTVw43B/nXbce/qGJNz0rAP3va2V17vhtDiMyY98TkR+QyfHLIyUlBSkpKSgpaUlbDChqqoKQPj+EABQUFAAAKiurj7ix4iPj0dqamrQfwxGDLxzj9EuRgPLNpXWKrjhGTtOvbcdTy534dNtHrzXx7JOeukcQCuFlBhnbMmmvZUKOt1amrheZxzQmlm/tdaNwkwJ8yb2fJGTlSLBJPv7X0SLXss/M0Urd9Xq8JeFAbSLt1dWunDTP+0wy8DpR/lPlIWZkq+h2YRc2ReMAIC548348K4knD47+rHTi701Ad/81gWXR+D2lx0QArjpzDiMSpfx71Vu/OCJDih9KGOkpxDqpbfGDkI9yw82ufHJVg9e+DK4OXybQ+CbPR7Mn2zqMRgBaI3RgOg2tm7vBDrdQFaK7H/fQpQdqmvVgpTJCcCsgBq2Rd76vJWNAlMC0q3jzBKmF+plTAx+Eb2Y560Puq44dDqx3gR2f5Xap3TUem8JEH3Rb3SmjHKDm7rd92Ynfv2yI6gO9Fvr3Ph2n4KL5llQmNn75Uh2qgy7E76andFQ6+s1JGFCrglldWrYsi/rvQ2bj/Mex/Xx99k27T0YHfCaA0s2RdPZcy1QBfBBmPNnXauKyibt9QY2QYxEfZtAaiJ8k6jMFBm/vyQRvzo/AVedGIcTppuxdr8yIPXLHS6Bikbhe19Pm23BU9cl4o1fJfUpGAFoDTfdipZ5EG3tnQK/fMGB+97sxKrdCr7/cAf++bkz6Lyjj69Tino/b559tBmv/NyKBVOi25TTJEu49ex4OFzAM5+6ev+BCHkUgcomEfRZA4DxOSYUjZbx6Vb3ETeNDcXlESirUwekpniBN0Bb0RD9HhJZXYKTemPrr3Z6MCpdwuM/Toz5xZyenDBd+6x8unVgy3JuOujByt0enHW0GbPHmXH+sRbsr1KxeYAbe+olF0dnyZBlCfdckoinr7f2ORgBaP2aYqVkk/73+MW52iLPil3+c84rK10orlZxzlwz7rssAe/cnoRLFw38IpxRLCYJ43Jk7KlQ0OkSaHMI7KlUccxEk28RdUahCfur1CMqj9jh9NfIlyQJC6aYUVKrDuh1e2WTiqR44GdnxKPTDbyx2g1VFXjsw0489YkLMwpl3HFh7wt0eg+JWOnVJITAjkPa+7QuRCm3DzZ5sLVUweWL43wNhYeKqfkmmGTgzTVutDkEqptVHKhRsWiqGZIk+XrqrDmCJui7K7T3alqBNobnjjdhb6Vq2DX717u153jP9xORkgj8/WMn3vzWBbsTuOrEuIiCvmOzey75G0v+/rETbQ7gV+d173s2MeCaIydMSb5YYDZJ+POVibCYgKc+8ff++HiLdqy/dKE2B9lwIPLxd7hB6580IVfG6bO1c8CnWz347zdulNWpiDNrDb/7Wt44nE0lCrJTJcwea8Kfr0qEomrZh9ecxN4RRyp2R2yEJEnCzJkzAQC7d+/udntjYyOqq6uRkZHhCzqEUlRUBADYtWtXyNt37twJAJg1a1Z/nzL101FjTRidKeGjzW4oqkB7p8AN/3Bg1W4PTp5pxuPXJiIxLvyCSigeRaunqNfdk2UJ47JllBoYkNgd0HTnyeX+lY7HPnSioV3gJ9+Jhyz3fDI1yRKyUyXDm0b1Rq/lbzZJvqbUej2/VrvAT5914I9LO5GbJuHVW4MnSZIk4aixemOm7hd2E3JNvb4Pg2FmoYxpBTLe3+jGX97uxL4qFT87Mx63nJWAd+5IwnXfiUNlo/BdIEVCb0auXzzok4dSAwMSe7wXjMvWuuAIWIh5Z70bbgU4Y07vE7q8DO15VkcxIFEX0CA3J1WrA9q1D4LDpdVSrmoWuPOiBFhM/nFUFLALcsqo4HF30bw4zJtk6lOGlREyU2RMypOxrrh7PVchtCZ0+kdDr2Ubifo2rQdCkjdePjpLRlOH6Hax5nQLvPCls9dmkr1p7xTY7+3VceerDhxuUFHTouLPb3UiO1XCby6KbEdJdqr2YuujWFPdH5DQeny4leBx19Dmn3xtKPYg3uIfa7lpErJSJLR7M8gDs6nG58iIM/vrxkfLd4rMSIoH3glTr3u7tyl3nBnYdVhFU5jmdKHUtwpk9bDz/ayjtYnIJ1v7X+e6pFaFEMDEgJ2NJxdFFvjqSg+YR7u+dVmdiu891IGPt3hw+mwzXrjZisJMGQ+968TVj9nxxXZtcX3lbg+KRsvI7aF2sk6SJBw70RwTmR/fmWXGrDEy/vuNC5WN3cfVm9+68MWOvh2LqpsFFBUoCPF3P+9YCzqcwJe99Ezpi0P1KjwqfNdB/RHt/j6Av5eALTl4fBRmSshM1jbuPHGddUAzWqLhmAkm5GdIeHeDO6g3VnOHwH1vOo54EffRD52QJeCWs7ST7aULtQWL/34zsIEPfTGt4AgzOgNlJkthm44OJiEEPtnqQXaqhCsWW5CdKvn6GzndAm9868aYLAkPXp2I7y+Iw9QCU0wcx/piwRQzKpsELnywA89/6YQQwLET/OesmaO1a4y+NrbWN9Pp/RABYLE36PbFjoE73lU0qsi3yThjjhk5aRJeXeXCT5914MnlWjDiyeusEQUqY62pdUWj8DUAX1sc/H45XAIPv9uJNKu2IW2oSU+ScNu58SiuVnHr83ZfkG/RNG18ZCRrjdjXFSt97iOxp0JFaqJ/09rcCdrGr20DHIDVrdrjgcWkXTtce3I8DtULPPyeE2lW4NxjIgtODsa8O1JCCHz3wXb85e3uZaYOVCt47Rs3Zo8z4eyju282mRQUkIjt42Buuozvzrdga6mCtcUKPIrAp1s9mJQn44oTtM9UX3o66etNE3NlHDfJhDSrtmn5iY+dyEmTcMNp8RBCy7jrr/ZOgX2VKo4er51vTphuxh8vS8CfrkgMm1VOvRsW79xZZ50FAHj77be73fb2229DCIGzzz67x8c44YQTkJSUhK+++ur/2bvr8Ciurw/g35lZj5GQIEmA4MHd3SkOLW0pderu9taN/uql7tSFUmihBUqB4u7uQQIkgUB8de77x+xussTDLlmS7+d5eIDdze5McjNyzz3nICMjw+c5l8uFOXPmQJIkDB8+3G/bTRUjSRJGddIjNUNg/QEXnvoxD0fSVDwxzoiPbrVgSDs9BrTSYdNhV5E3tUU5kyUghG9UuWEtGSfOCp9JW3/yrCTonahg9T4XNh1yYscxF75fZkf7BAVX9ijbybR2hBwUGRIxEflZDkB+Y+vX/7Div51OjOmsx6zHQtGuiCaOnhT5xn5YVRgokiThiu4GZFu1ppVtG8i4bbDB+9xVvQyQJG3VSVmluJuR16lx8TIk9pzQ3jsjNz9ol2sT+OQfG2LCJW/j+JLUdU90eVZKVwZPo8+YCG1FYP2aMo6m5W+PSxV49Ns8bD+q4s6hBlze3ffGoVW9/LHWPNZ33E3qbcC394b4ZOtUlq5NFJw6J3D0vAyGI2kq0rMFLuuggySVMyCRKRAdnt88Od7dXLngCqHVe50Y878cvDbbhru/yMOnC20VanIHaBeBqgD6tNAa2D/0TS6e/9WKzDzg+StNiChj4MczWX86s/JuHFLdv7O1wqVCx7rDqS70fy4bnR7LwsAXsrD+oAvtExTvDbkkST6r8wuu2jbqJUy/24KHRlduuq/JIGF4ez12HlNx4FThi/dtR7THrnCfn9aU46bhdJZaYsClfysdjHpUqKHd+Q66y3j445ziXb1ZyZMlXyyy4UiaiqfGG/HeTWZ0b6rD74+GYHIfPbYkuXDXF3no+2wWsvKAwW0vnZXCHpIk4YGRJjhcwOf/+qajWO0CL8yw4rlfrOXKQjzmWTleREDisg56yJJvtu2F8pSP8UeGRKhJm/QvKvPvYjmdpcJiACxG399bSZLw/i1mfHtPCFrGX1orhIsiyxLGdNHj1Dnhk5H4+b82/LDcgS8Xlz9rZ+8JF9bud2FMFz0a1tK+R81itWav87c4kJyu4o/1djw4Pdeb1VRRx06rqB0hFVo1WxE1wyRk5MKvmUMVcTBFxeFUFYPb6KDIEvq11OFgiorjZ1T8tcmBszkC1/Q2BMWCpYp6fJwRj4wx4kS6io8WaGOsc5P8eyTP9UJ5G1sXFZDo21I7v3pWIl8oVRU4eVYgNlKGQSdhch8DUjIElu5yYnxXPX68P6TEhsIFRZglGHTAqUpeXOdRcOJy3X7fifkflttx8pzA3cON3vJUl5qbBxowsYceq/a68NosbfK7YJZk96Y6pGUK77VtWaiqwJ5klzc7AtACEkBgGltb7QIbDrrQpbECi1HCdf0M3mzWK7qXvbHwxbjvLqu0TIFdx1V8u9Tusz1CCLz+hw0uFXhyvLHIwKsnU8dTrSLY3TLICEUGPvvHhvUHXDibIzCsvQ5N6sioGSph7f6y39Medo/TRrW1vnEDW+txMEXF2RyB+0cY0a2p9r0pb2BMCG0e4avF+dejW5O0+1lP03ZAq6AxouOld80dTC7NI+l5br75ZlgsFrz99ttYtWqV9/G9e/fi//7v/wAAjz76qPfxkydPYs+ePd4yTABgsVgwZcoUZGVl4fbbb4fdrl0YCCHw2GOP4fDhwxg3bhyaNWt2kfaKSuKJfD/xQx7+2aqlQ19XIFVqpPv5v8s4seGZ0K8Vnn8QT6gV2JPU7uNaytdTE0yQJeD9eTY890seZAl44SpTmS+ya0VIOJ0l4CimbEigCSGQlim8q5c9tTYPpajYfdyFmWsd6NZUwf+uNRVbjmRgGx0iLPkrNILV6M56GHRaat5rk83QFVhxH19TRs/mCpbscHpX75fGM+48vRzqR8uQpMCt1Mi1aeUkeicqsBi1C2shBL5dasfpLIG7hhnLdBFXu4YESarcm4e0DE/ZIW1768fISE5Xvb8H78614d/tTozqpMN9IwqXzosKlb2reJrFBu+ESld3veR1563S8pR8GNRGj1bxMlbtdZZ5ou50lvApweGZrDt2WkVGrsDj3+fhpo9ykXJOxb2XGdE8VsY7c2149hdrseWJSuK5CLxjiBG3DjJg2xEVS3Y4MaKjDoPalP0izrO6Pi2r8iZK0jILZEi4Jx0Pui+Gf1iuZRn1a6lDmEmCXgeMPO8i1RN8jQ6TCk3ydWykq9AKfn8b00Xb5j/WFz5/bjvigkkP3NBP+50qa9kmu1MgIxeIDi/++BJqktC3hQ7rD7rKfAwtjieY4o+V6p4MibM5lXvDejhVRYRFwvX9829IzQYJz1xhxuLnQ3HfCCNCzRL0CjCsfXCfS4vTs7mChFoylp2XabjjmAsOl/b7t+Fg2W8qj53xBCQKj7taETK6N1WwbJcTWXn+OaZ4AmFN6vrnnFI/RvYGVSrDmSxR7ORGx4Y6nzKIlzrPYozZ7uPe2RwVP63Q7gXnbnSUu2zOEvdK9PMXeVzVUw+bAxj0QjYe/96KeZudeGtOxQP+AHDsjChX/7KSeIInB8oxGRkIC92ZckPcJTj6tdSOaUt2OvH9MjvMBmBCt0tvhXpBOkXCLYOM+OPxEHRrqqBFnOyzQKZVfAUDEu6fXcMCGYIhRu38usEP51dAuw5zuIC4KO34cHUvA/q30uH5K0149RoTTGWcEAa0gGCj2rL3+FnZdrozQXs2V3AmW3iv8YQQmLHKgegwCZN6X7pjT5IkPDvRhJ7NFeTZgZbxss/q7m7u4ER5Fpwkp2tlwloUqIDQMl6BUQ+s2e/0yTw73xt/WHH5m9mwOcp+DFx/0AWbA+jlzvwJNUl4YJQRNUIk7yr7sgimgIQnG9KlAh//kz8JvmCLE0t3afez7YtY1AkAdWtIsBi0e4uC1QCCVb1oGSM76rFqnwvv/q3t6/D2ekiShK5NFew5oZY5K/mg93jnKdGqfY+a1ZUxrqsereppZcq2HinfcXTzYRfmbHTg/Xk277ZsOqydlzo2rDrXPsGg8u98/SA+Ph7Tpk1Dbm4u+vbtiyFDhmDUqFFo3749Tp06halTp6Jdu3be1z/55JNo0aIFnnzySZ/3eemll9C6dWv88ssvaNasGSZOnIiWLVvi7bffRkJCAj788MOLvWtUjMZ1FCTGyTh1TqBhLRkvXW32iRj3aaFDuLnsZZs8q15r18h/D89F+bt/2TBvs8ObvukPDpfAvpMqEuNkNKqtYERHHVbvc2HnMRU3DjCgeTkmSGvXkCFE5ZUx8dTy96Ts143UyuccTHFhqnvlxRPjTCWmUreMV7B2ajhal7O298UWYZHw7o1mfHSrpVCTMwCY2N0Ap1r0RF5RUjJUmA1AmLtXgVEvoW4NKWAXRvtPuiAE0KmRDuO66rE7WcWSnU58udiGejUlXN69bJPDBp1WeubkuaLHXEkXnv7iuaHyBBHrR8twqsDJswKHUlz4aokdberLePUac7Fjr0czHWpHSEVOVgULT/+B8+vYbjqk/b9jIwV9WuiQkZtfTqckLlUgPVv4rFSv757ImLPRgTGvZeOP9Q70a6nD3CdDcfdwI364PwS9EhXMWO3ACzMKpxKXZusRF3Qy0LKegvtGaqtVakdIePry8mUDRAdFySb3uIuQvOWADqVotUlnr7OjaV0Zn9xmxh+Ph2LT6+G4sqfvjVHr+u6+HX6aPAqELo218iVzzitfoqoCO4660DJeQYMYGQ2iZawuY51hb9+SUlKaL+ugh6hA2aa9J1w+E4aHUlQocv7CgguRH5Co3BXDSalqsftTp4aMu4YZ8e8zoVjxcpj3+uVSI0kSujRWkJwufDJcPcc7APh7U9lX+Hqyvor7fRvaXg+HS+uFUF4uVXiPBx4HT7kgSb4rky9E/WgZ6dmFy+ldLGeyBKKqSRmCRrUVtG0g458tDuTaBL79z45cO9AuQcG5HIHl5SjHCWgBiVAT0Pm8XnDD2+vRroGCLk0UPD/RhHFd9TicqmJ3csWu+zJztfIyRWUBVUQz94T4/pOBKbNSVgu3ORBhAbq4a9r3aK6DXgG+XmzDruMqxnbRV3pZTX9pWEvBN/eEYNZjoT6ZubXcZR53lTMgkZSmQq/kBws8hlfw/FoUz/E51l0mLMIi4ZPbLLi6V9lq95+vcW2tKkFlHesK2nHMBbMBmNTLt3zMliQXjpxWMbKT/pKY9C2JXpHw3k0WDGitK1T7vktjHWQJ5Trmeao+NI/z7cnXs7kO6w+4cPNHuUX2aUjN0DICdh5T8cuqsmeiecoj9y6wkPGqngaseTWsXKXrYsIlWIwIaInusvLc+4eagD/XO5CU6nKXDbQiwiLhyRLK28qyhKHt9ejb4tJZjHKru8rE1iQXGteW0dS9kKNbUx2EgE9/1ZIcSlERFSp5M5b6tNDhxv4GTJ1shiJLMBskNI+Vse1I+cqQ/bJKu9bMswMzVmtjc/MhF4x6oEUVyAwNJlXmKnPKlCmYN28e+vTpgzVr1mDFihXo2LEjZs6ciSeeeKJM7xEeHo7ly5fj4YcfBgDMmTMHdrsdd911F1avXl1iQ2u6+K7ra0CtCAnTbjYXWnlv0EkY0labcD2UUvqFnLcueIE6uF2bKmgeK+O/nU48OD0PvZ/OwppypJCV5FCKCrszfyXBnUONkCTt4vGuYeVrhO5piJySUTkn04K1/AGtr0XDWjLW7nNh3QEXruiur1IH7oFt9OjZvOgT/sA2OkSGSJix2lGmk17KOa2ResGLd0/vkvKuiCoLT7mm5nEyJrtX9zz8TR6y8oD7RpjK1ZSyTg2pyLrKKRkqevxfFr5YFNgOsGnnNWZuEJ2/yuWduVpq61MTSt6n5640Ye6ToUGd8l8zTEbTOjLWntdHYsthF+rWkFCnhuy9IF+xu/Qxcy5Hq6lecKW6Z1X+P1udyLYKvDLJhE9uM3sfDzVpN5rtEhT8sd5RrhtGIQS2JrnQPE6G2aCt3Pn6LgvmPx2KqFKap5/PE3zyx+q+ikrLFFBkraZ6mFnr4XPwlAtzNjiQbQUm9y75ZrxNfW2ljj9W7geKLEsY3blw+ZIjp1Vk5uU35+7RXMHxM6JMNe69zXFLyJAAgH6tdDC5yzZlW7VScqNfy/auSirK4u0OjP1fDj6cn3/MOXBKRb1o2S+Ndmt4AhKVWLIpK0/rc+Wpd1wcWZbKXAItWHmCsBsLBCE2HXZBr2irGf/Z6ixzRmhptfUHtdFK3v1bgT457861YfCL2UguEDjZf0pFfJRU5nIRpalfiX0k1CKC11Xd2C4G5NqBWesc+G6ZHQm1ZLx9gxmSBMxeV/YxcjpTxbajLvRO1BU6BpkMEn55KATf3RuCq3sbcFVPbSFIRUuHHU8vOehWXs3qau+z70TlnWePn1Gx67iKAa3zJ35DTVqw0lMqdHI5VkFfqiRJQst4rTFweTJ0DqeqqB8tFyo76imL6I+yTZ6fQ2ykf8adZzLyYBGlIi8mIQR2HNMWXnRtqp0fPBnKf7oXm43rUjXKs4SZJXx8qwXjuhoKPd47UYflu51lnlvw9CcsmCEBAG9cZ8ZVPfVYs9+FMa9lY+Ya36DDd8vscLi0vmSf/mNHbhkbYK/Y40StCMl7vKooSdJ6hgZDhoQno/OR0SaoQmv6/L/ZVpzJFnhyvLHUHgWvTTbjlWvMF2NT/aJpXQVD2mr3rwWzej0llsoy5yaEtgixUYF7Kr2i9SYsWCK3bQMFaZlambmyyMgVmL9FK89dK0LC98vssDkEthxxoU19xS/3FpQveO+IK2DYsGFYsmQJsrKycO7cOaxcuRITJkwo9Lrp06dDCIHp06cXeq5GjRp48803kZSUBKvVioMHD+LDDz9EnTp1LsIeUHlc3t2AZS+GeS9izucp21SWi/yUAqtePWpHyPjj8VAsfi4Uz03U6hr/trr8dWSLsvu4duL21N5tXEfBp7eZ8dntlkJlPErjqdGZUsxq9UBLPW9iGAAa1dJWq1uMKLJcTlVl0EkY11WPI2lqmcpKpGSo3v4RHsM76GF1AJe/mYM7PsvFruP+uzjf675gTIxV0LiOgh7NtHTd5rEyRnYs36qKOjVkpGUWLhX222oHMnK1Ru2BbIx4fiDMk3Y7e50dC7c5MaStDh0alrxPBp02qRzsujZVkJohvBfMGbkC+0+p6OCuz9ouQUGYWWvwVhrPSvWCJZtiwiW0qS+jRzMFfzweisu7F55U1ysSxnfVw+5EuZpcJ6drE6ntEvKP07JcsQk7zzZXdlPr6DDJe6PfuLaMQykqflxuR6gJGF3KjWrNMBnf3WvBAyOD+7g4totv+RIgv39EW09Aopn2+1WWsk2evh+lBSRCjBL6tdJh4yEXhryYjXf/smH/SRUfLyg6wJmRK/Dcr1rWzu9rHXC6BOxOLUjSxE9BnxpBkCHhKeNXWkCiKujcWBtX6w9q40pVBTYf1iaIxnbR42yOwJp9ZTsvHjtTcm39mHAZ7RMULN/lLFfN/Kw8gR9W2H2Oh06XQFKq6q3l7A/egEQlTJZk5GnB60uhHrW/jOigrcJ/bZYV2Vbg9iEGxEXJ6NFUwX87nWUu27Z0lxNCAANal35d1T5BQVyUhL82OiqUXVpSn5SKqBctw6QH9lVihsSWJE+PPd/vX79W2v+7N1WKvferaro2VeBwla884vEzapFZWv4s25SfIeGf44On786BSi7bdPS0QFae1r8jwiKhRZyMdftdsDkE/t7sQLO6MhLjqv55+MqeerhUYNbasl3v707WsnLO79sVapLwwlVmfHWXBTVCJDz9sxVr3Jm12VaBX1Zqgd8nxplwJlvgu2Wlz7OcPKviYIqK3ok6vzSybxAj4+Q5Uel9czzn+XFd9ejRTMGfGxyYtc6B3omK95q8qnlwlBH9Wup8sskTYmTUjpAKVQbwOJOlFvi3QGYefAISRfHctxQs23T0tFpsmbA5GxywObRSdJ7+OB/MsyHX5ts/gvyj6h9Rqdrq1lRBdJiEvzY5S12t7smQqF1EA67YKBmTehuQGCdj1V5XuW8YnC6BGavtPqmKntTsghc1fVvqK3QjW6uEDIm1+51+a2BWnPNr+QP5fSTuGGL0CVRUB1e4yx7NWF3y9z3Hpl301o7w/f5c2dOA2Y+FYFh7Hf7b6cTk93L8NrG/J1lFuFkrqwUAUwYZoVeAR8eWvWeJR2ykViostUBDdZcq8NtqO/QKkGsDPlvonwBeUU5nCoSZ4a1TW989UffXJicUGXhodHBP+JZHV3eTQ8/k8NYk7WLeE3DRKRK6N9Vh+xFXqTU304pYqS5JEmY8HIqv7w4psYfBsHY6KDLw96aib4zTs1V8/q8Ns9fl/9w9F3/t/FBrPMysraI6XYk9JFIzVN9jXR0ZuXZtZfT4rgaElCGg3LGRztsPI1g1qq2gTX0Z/2x1eIMJ2877WXZzrx4sS9kmz7gry/lgTGetrIRJr/VTGtRGh+W7XUhKLXxzMnWWFWmZAq3raQHS5budSEpV4VL909AaKNDUujIDEu5yAv4oQRXsYqNkxEZJ3qD+4VStt037hgou66Ad88ra+LwstfWHtNUh1w6sKmYcp2er2Hxehs6vq+3IdcfIPOWejp5W4XD5p6G1R/1obexVRmPrM+7f2Zqh1ScgERkqo19LHRwuIL6m5O1XN66rVtqruHPf+ZbscEKWtEbCpZEk7XNSMgQ2HCp/EMCzqjbeT6UnFVlCkzpypWZIeCa765/3u3tZB63cVXVa6DTU3UNjwZayjb3jZ7TzX3Fl4/xVtskbkPBThkSwBCQ8Da1b19O2p2sTHc7mCHyxyI6MXG2xhj8mwYNdv1Y6xIRL+HW1vdR5D6tdYNMhJ5rFFp+V2rO5Dl/fHQKzAXjs+zyczVbx+xo7MvOAmwYYcEUPPeJrSvhykQ2ZpZTIXlZEuaYL4VnoURnn2YKOntYWKZoMEu69zAghAIsBeOGq4ksPX+oa1Vbw6e0W7+JaQDsndmuqw/5Tqvf+AwAOpbhw1+e56PV0tjdw5ekf0aiUa+P27kVxW93B7h3HXLjslWzc+VluoTlCIQR+XWVHmFk751zV0wCTHvhysfaZ7B/hf1X/zoaqLUWWcFkHbbX6jqMln2RSM1VYDEBICde4vRN1SM8W5arzmpUncMdnuXjmZyv+76c87+O7j7sQYix8sV0RnpJNBSeGAa1PxSPf5uHB6XnYkuSfUlNF8a5UL5BdclUvPZ4cb8SNA6p+SvX5GtdR0KGhgn+2llzaJsXdELp2ROGLjMQ4Be/dZMELV5mQZ9dW25VXWqaKvzblr7hTVYG9J1xoHqd4L2x6J+qw+Y2wCl3U1XEHNQqWbVq2y4mT5wRuHWxAy3gZP66wB6zxtdZIPf/3p04NrZkroDWNvFRrqBelfysdEuNkfLrQjtV7ndh8WLugKrhKo08LHVRR+uTw6fMyS8ojMlRG92YKVu11+vTUOZKm4pmf8zDg+Wy8NceG//vJ6i0x4rn480dAQpK0EkmVVbJJVQXSMrUyax6NC/SSmdS7aq1gmjLIiFwb8MIMK4QQ2H7UhcgQyVuXukaIhFbxMtbsc5XaUD2/h0Tp425QGz1mPhKCBc+E4qqeBm99459W+E5CL9vlwGz36rH3brZAkrQsiUPuGxR/lcUKN0uQJZS5wV4gJKVpv0fVIUMC0GpYH0pRcSZLxSb38a5jQwUNayloGS/j322lNxnOyhM4lyNKrSc9pK32e7uwiAm642dUTHwrB5PezcUC9+IOh0vgu6V2xIRL6N5UwfoDLmRbhbchq78CYUB+oL0yGlt7AxLVpIeEx8Qe2ni4c6jRWy5ocFs9LEb4BNuLY3MIrNzrRMdGiremdWlGlSOj+3z+zpAAtFIaaZmizBkh/pbsnez2PV/UipDxy0Mh6Njo0qmTfqHqR8toGS9j0fayNVYvLXjtr7JNJ84K6BX4raSbVmIROFDJJZt2uHuxta7vWXih/f3pQhtkCRjVuWpd5xVHr0iY0E2PE+kCK/eW/DNZsFXLjB9byvemQYyMZ68wITVD4KmfrJi+1I6oUAljO+th0Em4Z7gRmXnA+/Ns2H3chZ3HXN57ZQ9PWV6LEcWWTy6vYGhsLYTAkdOqdwFFx0Y6PDrGiDevN5erJ0ZV4fm9G/pyNq5/PwcPTs/F6NdysHiHEyY98PECG3JtAoc91/ulLOhNiJERZtYWVjldAs/+nAeXCqza58Lfm32v/bYdcWHfSRWjO+lhNkioEaJVv/Dc5rRnQMLvqt8Ip2pljDvFbVYpNxGp5wRizqvlfz5vnfYCZVHsTq3G9Yo9hbMwjp9RMendHKzY40JUqIS1+13Yd0JrqLMn2YXEOMUvtetrF1OyafF2J9IyBYQAnvnJWq76o+VRVP+NmHAZN/Q3Vtsae2O7aGWXFm4r/oLf+30rIivHY0hbbQXykh3lbPCa7MLEt3Lw8Dd53hvc5HSBHJtWrqkgXQUbs9V1b3fBeow/r3RAlrQsjwdHmWB3wqeuuz+lZfquVFdkCQm1ZFiMKHcflmBn1GvN1C0G4JFv8/DfTifMBiAxtmCGlTZW/iplQiN/kqliP/cRHbSVov+6x3Zapnacm7HagcRYBfcMN8KlaiW7AC0gEWGRvBf8Fyo6XPb2D7nYzuYIOFXf0n6eyceezZUiG91fyoa312N4ex0WbnNi1joHdh9X0baB4nOe7JWoQ0au8Gk6XJSylmzyaFUvv0Zr1yYKmtaR8fs6O3Lc9YXTs1U8+4sVIUbgpau1G7YezbSyKp5SP/4qnaPIEmqGSUU2ZLxYPDfK9atNQEL72a0/6PIGYDu6S9SN6KhHZp7vtVhRvA2tS5morRcto3msjCU7nXAWKEGYlOrCtdNycOKslo331I95OJzqwoItTpw6JzC5jwFD22nHwxV7nN6Vek38eByoYZEQZq7kDIlqVLIJAPq10mPRc1rpQg+LUcKwdnpsP6qWWuN+zT4n8uzAgFZlnyxrWlfrWbdgS9GTzifS1WKPP8fOaAuq/Plz8jS2rqwsiRNnVRj11W/sFWdYe+2Yt7aYEiYF7XcHRhsWE5CvSNmmszkqps6y+ixAOpGuIjZS9lsPNkWW0Ki2jAMnK3eV+s5jLliM+cH/zu4Gz3an1jfr/Kz2qmxiD+0Y+GspzaZ/WemAUQ+M7Vr6IsSxXfQY1UmHJTucOJEucG1fgzfTfXRnPRrXlvHdMjvGv5GDy9/MwaAXsvHxAhucLoGz2Spu+yQXmbkC79xg9pbTvFDBEJA4l6tVTWhQYKHqlEFGDGxTPQJg57usgx63DTagVT0FO466MG+zE+0TFPzyoAWPjzMhPVvgxxV2HHIHYIvLCPOQZQlt6ivYedyFb/6zY9dxFRN76BFhkdwlGvPPu54KFwXLSHkWRjWqLZd5oQGVHb+jVKW1riejWV0Zczc6iq0TB2gZEkWtVC+oYyMFZoPvTfCf6x149y8bbvk4Fzd8kIt1B5xYtN2BJ3/Iw/g3snEwRcWjY4348BatydAPy+1ITtfq3fmrBqXZICHcXLhk0y+r7NDJwDV99Nh/Sg1Y+Zzza/mTNpGnV/IboBXF8/OqU8K4iwrV6luv2OP0uUkVQiA5XcX+ky5sP+rC9iMupGSocKkCy3Y5MOm9HJzJEjDogB/cq4o9Dcf8Ne7i3RdNM9fYkW3VtmfZbif6tdK5Gy0r6NxYwe9rHUWWWrkQVrv2O3T+mHttslanNNjL4VREQi0Fr0wy44w7S6ttA8UnmFSnhow+iTos2eksMSslrYgeEuUxuK02tv/erDVuf+rHPKRnC7w22YRfHgrBPZcZ0b2pgj/XO7D3hAu7jrvQPkHxW7pxTJiE9GxR6or8QPAGEQvckHZsqODKnno8NtZ00bfnYnhuogk1QyU894sVDld+HVYPz8re30upM3w6S2sGXpEbSEmSMLmPAVl5Wl3XtEwV17+fi1PnBJ6aYEJdd8mICd0McLiAX1c5IEml15Qtj86NFexOVgPWFyctU/X2lipKUqp2jVKWkmBVQWd3QGLDQRc2HXahXk3JmxE3vL025v7eVPKY85SyqRdd+vdsSFs9zuXkl8zZddyF697PRWqGwOvXmjHtJgvy7MD9X+Xhq8U2mPRaJqinpv1/O53elb2N/JghIUkS6kfLldJD4rS7TnN1nBQuakXquK7auPttTcnjbom7hFdZ+kcUNKqTHhm5hQNtaZkqxr6ejcEvZmP01Gy8Ncfq00j9+BkV8TVLXlBVXs3c/Rn2VdLk8Il0gdhI/+7TpWxYO20szS9DqbpVe7UVxC3jig+MjuiolW0qS0aO0yXwwNd5+OY/O35aod1HCiGQfFb1W/8IjyZ1tFr+JWWXB5KqCuw85kKrevkLBsPMElrGa8eDcV2qV9Z/fE0ZvRIVLNnhLDZ4tf+kdo4e3l6b3C2NJEl4/koz4mtKMBuASb3yJ9wVWcI7N5px3wgjHhip/WlYW8Z7f9sweVou7vw8D0dOq3j6chP6tfLfRL0nm8iTXVQZjp3WxnxpJSarC4tRwkOjTfju3hCs/18Ylr4Yiu/vs6Bdgg6Xd9cjNlLCF4vs2H7UBZO+cDZdUdonKLA5gLfm2BAXJeGpCSY8PNqItEyB9/+2wWoXeGeuFbPXOdCmvozEAsfQRrUVPHuFCY+OqVoLHoMFRz1VaZKkpRxm5gH/bi96NZ3VLpCR67vqtSgGnYSuTXTYfEhLz1dVgS8XazXmJnTTY8NBF65/Pxd3f5GHWesciI2U8eEUM6YMNKJ9goJW9WT8ucGBtfu17fA0tPaHBjEyth91eVO3j55WsWqvC4Pa6vDEOBOa1ZXx6UIb9p3wfypsWqZAuBnFNo2sjmqESOjfSoc1+wunm3qcOld835KC+rfSIdcGrD+Q/7N7f54Ng17IxujXcjDxrRxMfDsH/Z7NRruHs3D7Z3nQKxK+usuC0Z302Jrkwo5jLuxx/+ybl3CTUh6t4mVM7KHHmv0uXDctB58utEEI4Oqe2kWiJEl4YKS2Wv79ef7NkjhdTD36VvUUtE+ouqn8wzvocW1f7YaoqBqWV/fWmtDNWF188NEzyVTRNPsIi4ReiTqs2efC+/NsWL7bhfFd9RhXYGXUvZcZoQrgkW/y4HDBp6H1hYoOl+BSC5eo23zYiQe+zsXZADZST3UHEWsVCIQZdBJevMrsc+FalUSGynj+ShMc7sPP+QGJpnUVtG0gY/6WkkvUpWUKRIXmNwMvr9Fd9Ag1Ad/8Z8f17+fiwCkVj48z+qxiHtxGhzAz4HBpNycVaZxenN6JOggBrNwTmHISL86w4qp3coqsnSyEQFKaWi36R3g0iJEREy5h8XYHklJVnxIt8TVldGyoYOE239Jx5ztWxgwJQMtGBICfV9jx6Ld5uPzNHKRnC7x9gxmjO+vRo7kO940wYt9JFbuOqxjfVY/IEBlxUVp2xdKdTuw7oU3Q+Tto1CBaRkrGxW+4mZ7tCV7z2g7QMrUa1pLx+1pHsT8LIQT+2+lEg2i51BWb5xvRUbt2+mqx3SdT5/XZNmTlAQNba7XsP//Xjps/yoVLFXC6BE6kl94npbzyMyQufvkcz4Ibf092X8oSailIjJPx73YnHK7ijwNZeQIbD7rQranOu+q8KANb6xBhAWaucZTaY/GNP2zezAxPsCwjV+sT56/+ER5N3FmNpWUhBUpSmoocG9C6nu91zoRuBrSIkzGoTdW9vyjOlT0McKrFLzr5ZZX2+FW9yh4gCDVJ+OG+EPzyYAgiQ33HULNYBXcNM+KOodqf3x4Owc0DDdh2xIUtSS7c2N+Aa/r4NzAUGSIjwlJ0JmJWnsCrv1uLvZf3F092RgMGJApRZAm1C1QxMegk3D7UiHM5ApsPu9CwVtkytTz3L6oAnr/SDLNBwhXdtb5E3y+3Y/Rr2fh0oR1N68p49Rpzoa+/po8BA1pXz4yVQOOopypvTBc9dLK2krsonhXDtcqQhtkrUYFTBdbtd2LxDicOp6qY1MuAV68xY+6TIbihvwFPjjfi32dD8cfjod5UO0mScG1fA/LswLt/aZOzLfwYkHholNZr4PHv8+BSBWa40yuv6mmAQSfhlUlmuFTg/37K87nR8Yfza/mTxtOYtbgVSJ4MidIycwYUWIEJAGeyVHy9xI760RLuGmbAw6ONeGiUEZP7aKs1B7TS4ZcHLejSROe9aPtxuR17k1UoMtDUT6s3JUnCi1eZcP8II3Ynq/h1lQOxkRJ6t8i/YO/cWIe+LXT4a5PTm6HhD6nVOCvnsbFGPDfRhOv6Fb4g79dSh7o1JMxY7Sj29/z8ZuAVMaKDzl2WyY56NSX83+W+2QGdGuvQK1Hxlg7wR/8Ijy7uBt8Lzqt/PO1vG+ZvceLhb/IClj2R6j1XVK9xN6SdHuO6ajXUzw9IANrNep698M+koNNZ6gX9voYYJUzoZsDhVBWHU7UVcjcN8F2pZDJIGOme1Gvip3JNHn3cx7Xlu/3fj0kIgfUHXbA7tdWt5zuTJZBtrT79IwDt/NK5sYIT7pKAHc4LwE7sqYfNoWXMFCfZ2+y39O9bs1gZ9WpKmL/FiTkbHejVXMEvD4VgeIf8m8/bBhswsLUOJj1wQ//842//VtpE8b6Tql/LNXl4JpuPXeSSYZ6STVGh1WfclUSSJEzqpUdGrsC8Yo51m5NcOHVOoH9rXblX98dFybimt7a4yXOfsGa/Nh77t9Lhw1vMWPZiKG4aYMCRNBWLtmulw5xq2cZ4eUSHSYgMkYrNkDiU4sL0JbZSm91WxNkcAavD/5Pdl7ph7bSxV1LZplV7nXCq2jGpJEa91kj9wCkV20vosTh7nR3fLLWjc2MFQ9vpsOu41tfnhLt0U6yfa9uX1tg6PVsNaPbEzmPa53r6R3hc08eAWY+FwlJNMhQLGthGh1oRWrPp8yfl8+wCf6zXJnA7lHPhUe0aMprFlv41Rr2Ex8aa8N29Fjw53ohHxwZmhXqDGLnIkk0z19jx7VK7twxtoHjO79WlLOeFGt9V7+1nV9Zs6PYJOpgNwOhOeu81vSxLeHaiCUJoix0fHWvEjIdD0LRu1VxkFqw46qnKiwqVMaC1Dqv3uXAivfDJpqwTw4BvH4nPF9mhV+CdGGxUW8GT4024ob+xyJuDER30iAyRkJYpoJPzL7z8oUdzHa7ra8Cmwy58ttCOmWsdqB+tNVwEgDYNFNw80IDtR1V88o9/SzedX8ufNP1aaSuQ/ixiwkQIgd3HVejk0sshNK0rIzZKwpKd2kqmLxfbkWfXglD3jTDh1sFG3DbEiGeuMOPDWyz46FYLEtwNnbWMAQV/bXJgS5K2isCfmSySJOHOYUa8NtkEox64eaCx0Aro+0dqF4/v/e2/i7m0jKIzJKoDg07CpN6GIieJFFnClT0NSM0QxfYdOZ0lKlyuyWNgGz0MOkCRgTeuNyPUVHhM3XeZ9nOXpKInsStqcBsdws2+K/uOnlaxep8LZoPWoOyDC8zIOZTiKnLVYFElm6qLVyeZsOT5sCJT8kd21MOo134mRRFC4HTmhY+76/sZ0DJexktXm7yZQufzZEy0iPfvz6hWhIzEOBkr9jh9JuG+X2bHF4tsFxQEO5yqehtmLysi4JGUVnKT0qqqc+P8SbXzM8KGt9cjzKzVti74uzprnR13fp6LWz/JwYItThj1ZQtcS5KEey4zYmBrHb6714Iv7gwptEpWliV8MMWMf58N9Z5jAd/SPP5saO1R3xOQuMh9JM5kadeqEZaL+rFBbVxXA0x64OcVRR/rflyuXV9f0b1iqyifGG9CuwQFXyyyY84GB16cYYVRDzx9uQmSJEGWJUwZaIBBp2VSBKKhNaD9PjSLlbH/pKvIoMPLM614bbatyOPVhUpO1z6vOjZyLcmw9kUvxiho6S7t59G3Zekr+T3nyt+KWay3YIsDz/5iRZ0aEt67yewNcqzam38vHYiSTUDRAQmnS+DyN3Jw80c5pWZ1VJS3oXU9TkZ66BUJz080ITMPePrnPJ/v/bzNDmTlaYsfA11erXNjHW7oX/ge018SYhSkZRYuFzZvi/Y7NWdDyVnAF8rbJ4wZEmVi0Em4c6h2n1nW4EGNEAn/PBOKqZN9F9G1qqfg14dD8PdToZgy0Fjh3ppUcRz1VC1M6KatVp99Xk1/lyrwg/sGoiwX9A1raZPDs9c7sDXJhXFd9WWeFDXqJVzpLmfTpK7s94bPD482opG71mJ6tsBVPQ0+KWz3jTAiMU7Gx//YsCWpfDcRW5OcePNPa6FV13l2beVmdZwYLo1BJ2F4ez32nlCx97zsgHmbndh82KVl75Ry4pMkCQNa6XD8jMDaAy78uNyOZnVlDG1XttThyX0MsDm0iejmsYH5OY3rasD618KKnCRsVU/B8PZaA7PNh/1z8+rJamIgrLDLu+uhyMDPxTShO50lLrgmeKhJwktXm/DGdeZiS2S1S9BhfFc9BrXWIczsv5+TUS9hVGetL45nZZ+nRNXbN5jRup6Mj/+xY8mO0msjF2XlHidGvJqDP4ro/+It2VTNMiQAbTK2uPrAYWYJQ9vqsemwC4eL6BeTYwOsjrI3tC5OfE0Zvz8a6m20WJQ29RX8/KAFtwzy/yq6Pok6pGcL7ErWxsHBUy688rsVb/5pw31f5XkbbpeXp2mzJGkZGOdPtnjqGlenDAkgv49EuLnwAg6zQcLoznrsO6li6xHt+7fxoBNP/WjFsl1ObDzogsMlMLy9vswTJWO7GPDRrRZvFlZRZFkq1KOoTX0FUaHaZ/hzoYmHZ4LiYje2PpMlEBUmsY5/AeEWbWX51iMu7Dzme6w7naliwRYnujRRKry60qCTMO0mM2qGSnj0uzwcSlFxxxDfRU7R4TLGdtFjS5ILf7gXvNSr6f+fUbO6CnJt8GYpeRxOdWHVXm3fv1/m/950nv4YDEj4alRbQdO6Mv7d5iyy8bmqCizf7UTTOnKZvnct4xW0jJfx10YH8gqUILPaBZ7/NQ/3f52HUJOED2+xoGaYjF4FFuR5AxJ+zmKpFy3DoIO3H09B6w+4cPKcwLYjKhYXs+DmQu045kKYGahfhr5D1cnANnqM76rH8t0ub4mmncdc+PQfO0x6rSLApa6oxtbJ6Sq2JrlQI0RCrh1F3hf4y9HTKqJCpSIXeFHRxnfT443rzLi6V9lLeMWEy0XOu7Sup3j70dHFx+88VQt9WugQEy5h1lq7d7WPEAKv/G7FvM1ODGmr8zYnLIkkSeidqNX0lyTg5oHlq2N4dS9tZVNR9d8vlMkg4X+TzVBkQK9oB+qCDDoJb1ynPf/Yd3lFRvqt9qKbiX252I4vFtnx80rfk7F3YrgaTtCVheci7Y8CWRLZVoGps6yIsACPlLE5Un93865HvsmD1QHcc5mxTPUSAW1VlacGdCDr3JcUYLtvhBGyBLwz11aulU3Ldjmw41jhGxM2Ui9erQitzu3KPS5sOOjE8TMqjp9RsWi7A6//YcW5HOGX79vYLgZvzeviTJ1sxge3+H957eXdtOPuzDV2OFwCs9Y6EF9TQr+WOrx3swURFgmPfZ+Hb/+zFTtJfPS0ihd/yytUg95Tksdz01VQaqaAXgFqlKFxX3UzoVt+c+tcm8DMNXb8b7YVJ8+q3t/Xi1WLvn2CLiA3dZ5ydMvdq1A//kfrm9OzmYJF252Y/F5OkVmYpdmSpB3jLuugQ1qmwJ5k3/eorhkS2sSaVgawqPPdle7A1K+rtAm1p360wqAD5jwRgk1vhGPda+H437WF6wD7myJrxx4gQBkS7okST2Nrp0tg1jp7wGtan8lW2T+iCFf31sbdzyt9J+N/W+OAwwVM7n1h9c1r15Dx7k3atXqDGBlTBhV+v5sGGCBJwOx17oBEAFbVFtdHwnMf0CBGxoo9LhxK8W+9//zJbo69843vqsfZHIGPiygfsytZRVqmKFN2hMeEbgbk2PKzLvYku3DVOzn4eaUD3ZoqmPVYCFq5swVqR8hoWlfGyj3OgGWxKLKERrVlHCiiVJinTJpe0frS+btcmEsV2H3chVbxCoOwRXhqggl1a0h4fbYVz/ych4lv5eB4uor7RxoRXgWuiT3nbk+fFCC/ifzTl5sQZtZ6TAUqO+fYaZXZEeWkyNrClBohl/74q+6qX3ceqpZ0ioRxXfT4fJEdl7+Zg1Gd9TiXLfDjcu2i683rzWVOA+ydqMOvqxwY3EaHhrXKN8FbN1LG30+FelfT+VubBgpev84Mu1MUWdKlaV0Fj4wx4tXfbbj/61z0SdQhJkJGZq7WiG/tfifMBglLng/1qTG/3b0C8f15VozspENkiPbeaRmcGC5Jx0YK4mtK+HmFHfWjZVzZQ4/3/7YhLVPgxatMZa7N3K2pAotBW92eGCdjcDkaqxl0WmbORwvslZaG3Ki2gvFd9Zi51oFXfrch1AS41Pw/Qmg1HdsUKO3jcAnc+1UedDLw60MhaFygJnx+hgQv3ooyqZcB/2x14tppuYWeCzcDg9te2quZWsZr5XP+2uRAp0YKTmcJPDhKC9LFRWnlBR7+Jg+vzrLhwwU2TO5jwK2Djd4mx2mZKm7+KAfHzwg0qqX4ZPZsOKjdjGw+rE20NCpQEz41QytPV9ZgYHXSramC2CgJPy6348flduS450t+X+vAVe7MwAvNkKhsHRoqsBi1oNWw9jr8vcmJXokKvrjDgk/+seO9v20Y/Vo2pgwy4ob+hjI3N9582IXYSAkTuxvw9yYnlu12+vSYSkrV+v/4u058sJNlCbMfC4WhmNNdYpyCdg0U/L3JAVnSMggeG2v0+Z29WO4ebkTDWjLa1vf/Z9cKl2DSA0fdNaa/XWrH63/YEB0m4f0pZnRoWLFbOadLYOJbORjeQY/bhxReHHEmS5S7MXN10LqegrYNZMzZ4MCjY0wIt0hwugR+XmlHrQgJg9pe+K11lyY6zHwkBNFhUpGLPRrVVjCwtQ6Ltmvnq0BkEzSr6w5InFQxsI32WJ5dYNZaOxrXlvHsRBNu+CAXP65w4OnL/TfuPRkZcdXseFcW1/UzYM4GBz77147BbfXeYAEALHX3mSvL4jqPUZ30eP0PK35Z5cDBFBVfLdaCbPeNMOL2IYZC98W9E3X4eokdS3Zqx9zaNfx/Tm9SR8bcjU5kW4V3YYHTJfDPVi37o1eiDtP/s+Pf7U4Mbee/a9nDqSpy7YX7R5AmzCzh1WvMuOmjXMxY7UCHhgqen2hC8wAudLuYBrbWIS5K65VxdS8DIiwS5m12INQEDG2nw7YjBny71I4NB10lZlFWRLZV4HSWQM/mPOZR9cSRT9XGbUOMmNhDj+R0FW/8YcPni+xoGS/jw1ss5aqr37+VDncNM+CJ8abSX1yE+JpyQBtjjeyox/iuxa/QuraPwbuC+rXZNjz8TR5emGHFyj1ORIVKOJsjsO1o/oqn1AwVJ88J1K0hISMXPvXZOTFcMkmS8NpkM8ItEp7/1YrJ03Lx3TI72iUo5aoxbNBJ3nTpe4aXPTvC446hRnx6mxndm1XeheNdw40wG7QU/0/+sePzf+34arEd3/ynNQx7e67V5/UHT6mwObRyL3d94buSPS1ThVEPhAV+8eslqXszBc9NNOG2wQZMGWjAjf0NeP5KE+Y8EYI1r4Z5G/9eqiRJwuXdDci2Ai/+ZoUiaysHPbo302Hx86F4/koTalhkfLTAjivezMHu4y5kWwVu/zQXx88Ib4kcj2yrwK7jKuq4b7JnrSucERZTDftHlIUsS7i6p7basl60jOcmmvDOjWYIIfDZv9okx6V+njDoJPRopsOWJBfe+NMGVWgT0Z5eOh/fakZ0uIxpf9sw9MVsvPuXFX+st2PTISeS01Vk5YlCqzozcgUOnFLRvqGCTo21wPP5jbOT0lTE15Shr4Z1bcPMUonXZ1f21MPq0Fand2io+DSbvpjia8q4bUj5z81lIUkS6kXLOJqm4nSmio8WaMGIHJvA9e/n4ve1FSubc+KswO5kFT8VseozxyaQZ2dD6+Jc3csAqwN45Ns8pGSoWLJDazB9VU+D335PE+OUQuXBCrp5gDbWa0eU/DtSUU3cZacKZkj8tdGBzDxgUm8DujZR0KyujFlr7X6trZ6crvVX40KnwvSKNiksAXjqxzyf0k3LdjkRZtYC52VVI0TC4LY6bD7swuf/2tE8VsaMh0Nw17Cia/V7+igePS1QK0IKyDmpiXvx0cECZZvW7nfhXI7A8A563DLIALNBuxf1Z5YE+0eUrkdzHd64zow3rjPjh/ssVSYYAWjXd/eNMCIzD/jiXxuOnlax45iKQW30MOgkXN1Lu8f4aaX/y9QdP8P+EVS9MUOCqo0ws4SXrjbjmStMWLbLiS1JLtw0wFDu0g7aSatiwYhg4GnMeOyMQFqGijR348JuTXXYf9KFa97LxcaDLnR1rwDY7r5Iu32oEbPXOfDzSgeu7mVAkzqyt5QEbxyK17mxDnOfDMUb7lVIsgQ8P9FU7omLB0cZ0bO5DoPKkR3hYdBJ6Neqcieh46Jk/PNMKDJyBWRJS7VUZK0x8gNf52HHMa15ouf74qnP3LeFDst2O/HwN7n46FYLdh5z4VCKtlKdadVFkySt8XVVNqqTDq/PBrKtwJC2ukKNpo16CVf3MmBiDz2mL7Hjnbk2XPl2DhrWkrHvpIr7Rhixaq8T6w5o9ZgNOgmbD7ugCuCG/gZ8v8yOP9Y78MBI7cbcpWqNmds14Jgrzi2DDBjZSY/YyPzfzTb1FTz0TS62HVGrxAr/PonaquQlO5zo2VxBxwKr0we01qN3Cx1mrnHgw/k2fPJP4RtXSQI6NVLwzT0WKLKEre5+Th0a6mDQSejeTIelu5zIzBUIt2jj7kiaip7NeblelMs66PHq71Y4VeDVa0wBa3hZ2RpEy1iy04k359iQbQVemWRC/WgZd3+Ri6d+tOKLRXZ0bKigfYICk0GC1S7gVLVjY81imskfdfekOHVOYMdR1SdDMT1Lm+hjyaaijeqkx+q9LszZ6MDoqdmICpOhk4GJPS7edVbHRgpGdNQhppif74UKMUqoV1PCPnf5HCEEflxhh8UAjOuq9WaZ3NeA536x4o/1Dkzu459rjhPpKupESlX2d/lCtYhXcNsQAz5aYMfHC2y49zIjzuVqC8mGtdOVO0hw80Ajdh9XMbGHHtf3M5TY165zYwUmvdYTyt/9Izya1s1vbN0uQXtsvrtc0/D2OkSHy7imtwFfLrZjwVYnLuuQ/zsnhMCafS6YjSi2v1lxdhzTxjkzJEo2ugr0iyjOqE56fLnIjm+X2ZHr7qtyWQdtHDWqraB7UwULtzqRck5F7Rr+G//5Da15zKPqiXc4VO0YdBIGt9Vf8mVLLoQkSagfLRWKxreur8CgAzYecgLQUvi3ucs1tU9Q0CpewcS3c/DA13lwuvKbLFaFiaZACjVJeOEqM8Z11SPHBp9yHGXVqLZSKaUo/CkmXEZMeOHH2zdUsPWIC0dOq94yaDuPa+Pu2YkmfPKPDb+tcaDz41mwuxcPD/FDWQS6dEWGyBjcVod5m50lTgIpsoQpg4zo2lSHR77Nw76TKq7upcedQw2QJWDDQRc2HXKhezOdt1xT1yY6ZFsFPpxvx8o9TvRtqceZLAFVsF9OSTwlswqKrynj+/tCcChFRfPYS/884ekjAWjZaufTK1ogbEI3PQ6eUnHsjIpjp1WkZgpk52nZEBsOuvD3JidGd9Z7G1p7VrX2baHD4h1OrNrnxPD2epw8K+BwVb/+EWVlMUr4YIoFkoxyl9C8lNSLluFStZ4B3ZsqGNpOB0mSMOPhELw1x4Y1+534bY0Dv63xzeqa/p+MH++3FJnpcKxAk+yF2xw+AYnTWdpzUQxIFMmgk/DG9WYMa6/D879akZSqYkSHwoHxQJIkCW/f4P8eTQU1i1WweIcTUz7OQaNaCnYd186fnoVcozvp8dafVnzznw2KBBj0QJhJQqt6CupGVmzRyImzKlpW4Bq5OrljqBH/bnPi43/s+GqJHVEhEoRAufpHeLSup2De/4WW6bVGvYQuTRQs3+1CbFRgjg2Na+eXCgO08q0LtznRtK7sLd06ZZABP62w49Fv87B4uxM3DDDgbLbAB/Ns2HrEBb0CfH23BZ0bl/37seOoCxGWwtcwVH0osoSHRhtxx2d5+GG5AxEW+CwGuaaPAWv252H4K9kY2VGv9U6TtHPpiXQBg17rMVczTEL3pjqf0tcl8SwO8PSLIqpuOKNDRF4GnYS2DRRsPuyCSxVQZAnbjrhg0mt1PXWKhMu7ab0AakVIuKaPHqM66gNSv7Yqqmit56qujXtF0o6j+QGJXcdU783BsxNNOJcjcCZboEczBT2b69AugTes1d3j40zo1dyJPi1K/71qU1/B74+GYNMhF3o215oW9m6hw7t/2bBij9MdkHAhxAgkxsmIsBjw4Xw7fl/rQI/mOm/z0FqXeNmhymDQSUisIqn98TVldGmioIZFQsdGxY87g05Ci3ilUPD5XI7A4Bez8MF8Gy7roJXKMBvgDdb0aZnfOHt4ez0Op2o3qg15o1qsHtUge6SBe/GIImvNRT0TvTXDZLx6jVa78NQ5FduPaFleFqOEHUddeO9vG27/NBfT7wkp1NPEMwli0AH/bnfiodH5z6VnM0OiLAa31aNzYx1mrLZjTBVcOXxjfwMycgXWH3Bh5R4teDqpV34mhMUo4aqeBny+yI7nZ/iW3awVIaFbEx1evNrk7d/kUTAbtqDMXIGsvMCtvq8qDDoJH91qwbdL7Tic6sKRNBX1akoY0Drwx8LeiTotIBGgn1G9aBk1QiT8tMKOBtEy6kXLOJcjcH2//HEXFSrjszssmPa3DXM2OjBnY37D6wnd9Ji/xYG7v8jDzw9ayhSodroEdie70LkxG1pXd/1a6tC5sYINB10Y7C7X5DGkrQ7/u9aEH5bbi1wAUNDdww2497KyVdPwnIsbsGQTVVNV/yqeiMqlUyPtRLwnWUWLOBnbj7rQqp7iTeN97koTbhxgQOPaMpu7kl94AhLbjrgwurMeLlVgT7ILHRtqNwcGHfDBLYFdCUiXnjo1ZFzRo+xlIkKMkk/womWcjKhQCSv2OHHPcIFtR1zo0UwHRZYQX1NC1yYKFm13YtTUHBxJU1EzVMLQdrxsqu6+uzekUM39sqoRIuHG/kZ8MN+G2esc2HbEhTb1FW+ZjbgoGY1ry/h3uwNxC2SkZmg3qsyQqN6auMuYXN1Lj2axRU+w1akho06BMhJ9Wuhgdwp8/I8d936Zi09us/hMrhw7rTVLH9VJj9/XOnAoxeXNwjztLtlUXLknylcjRMKtgwtnS1UFXZro8P192jjacdQFhwuF6sbfN9KIQW11sNoBq0PgTJZ2Ll17QCtpNbitDsPa5wdrlu924v6vcvHBLZZCpehOnNWOd4FafV+VxNeU8dSEi18+eFh7PWasdvhkC/qTIkv4/HYLHvwmFy/+ZkWERRsLw9v7fl7nxjp8e68Ou4+78NNKOww6CTcNMCAuSsawdjrc+Xke7vhUC0pEltILx9OzrhUzc6o9SZLwxHgTHvw6F1f3MhR6bmwXA8Z2MWD3cRf+2epAqEnr8RQXJcPuFDiXI/Dg9Dxv9mtZHD2tItSknUuIqiPeWRORj86Ndfh0oR0bDzphNuiQbc2fMAa0lTlN6/KijfynfrSECEt+v5LDqSqsDqAlm8tRAMmyhJ7NFczd6MTiHU44XFqNZI/Lu+ux7oALaZlaz4kb+hsKrTKm6ulCVlHe0N+A75bZMHWWFbl2rRZ8Qdf0MeC1WVZM+9vmfYwBieqtUyMFX9xpQdcm5Tsn3jfCiNNZAjNWO/DFIjvuGpY/cX70tIrYSAmXddACEgu3OXH7EO39PT0kaobyeEfadX9xGWF6RSpUr//y7kBSqgvDX8nB2gNOn4DEvM0O5NqBx7/Pwx+Ph/iUEzuR7g5IMEMiaNWpIWPuk2Ur8VRRbRoomPVoKJ75OQ/ztzjRPFYutmRti3gFL15l9nmsXys9nrlC4IUZVjz2fR4+vyOkxM/b4e5Zx/4RBGhlzBY+G1bia4rKgPVIjLNj5zEXhBBlulY8dlpF/WiZ2TlUbTEgQUQ+OjRUtNrqh1wIc69MYXkcCiRJktC6vpaZ43AJ7HLfHLCOMAVa70Qd5m504v152uRv5wITfqM76RFilNCxkVJkDXaiiggzS7hpgBHv/qWNuQ7nnV8n9zHgiu567DzuwubDWj3sOn5soEiXHkmS0Dux/LdskiThuYkmzN3owIYDTsAdkBBC4PgZFe0bKujWVEGoCfh3mwO3D9Ge9/SQqMmSTVRBDWJk1I6QsG5//kphIQRW7XXCbADSMgX+7ycrPrrF7J2IO+4OSMSxL121F2aW8M6NZozb5US9CoyHSb0NWLXXiYXbnEjLVBFTQrnNnZ6ABBdBkR+0qa9gS5ILx88I1CulUfWZLBUnzwnOs1C1xjM+EfkINUloHitj40EXtiVpF2ltuGqEAqxNfQU2B3DgpIqdx7Sb0pbxPEVRYPVyT/IdTlVh1PvekMqyhMFt9QxGkN9d29fgTc9vl1B4otmol9CxoQ5TBhpxfb+qWQ6GLg6dIqFZrILdyaq31NjpLIFcO1A/WoZBJ6FfSx22H1Vx6px27j3jzpCIZIYEVZAkSejWVIcDp1ScztTG1eFUFafOCUzopsfoTnos2eHETyvy67CfSNfGXRwzJAjaGOrfSu9tZl1eQ9pq59Zlu5w+j3+8wIY7P8+F3amNtx3HXIgKlVA3ksc7unCt62vHL0/mTUl+XeWAEPDJIiOqbnjGJ6JCOjXW4Uy2wD9bnYgK1RoLEwWSt4/EURd2Hnch1KRNlhAFUky4jMQ4bZy1T1B8aqwTBUqoScLr15rw1Hgj6wZTwLWIk3E2RyA1U5uAO5qmTRDXc59jh7TVJkPmbdYmh89kC9QIkby9TYgqoltT7bpu3QFtYm7VXu3vXs11eHaiCXFREv73hxWHUrTHT5xVIUlA7Rocd3Th+rTQQZKApQUCErk2gc8X2bBkhxNvzbHB7hTYk6yidT02tCb/8Cxs8pQhBgBVFfjkH5vPYw6XwI8r7KhTQ8LgNixaQ9UXZ3uIqJDO7prWZ7IF2jbgRRoFnicgsf2IC7uPu9AiXmHTdLooPKVQujTmDQFdPH1b6nF9f2Y/UOAluhsR7zmuTYYcPaMFJOq7S6H0aalDqAn432wbHpqei2OnVUSzXBNdoG5NtXPq2v3ahPCqvU4ostYsO8ws4Y3rzLA7ged/tUIIgeR0FbUjJC4MIL+IDJXRroGClXuc3myIRdudyLUBIUbgm//s+GKRHXZn/qp2oguVUEuGxQjsKBB82JLkwrt/2XDPF7nIzNXG4j9bnUjLFJjU2wAdg/9UjfHoS0SFFGyy2bYByzVR4NWK0OoNL9zmRI6N/SPo4hnbRY+mdWVc1oEBCSKqejxZYLuTtUDEsdPugIQ7QyLEKOGH+0PQr6UOf2924tQ5gSiWa6ILFF9TRmyUhLUHXHC6BNbud6JtAwVhZm1sdWykw8Qeeqw74MKfGxw4kS7Y0Jr8qn8rHXJswMaD2uTwH+vt0MnAt/eGIMICTPtb6+XE/hHkL4osoWW8gp3HXFBVLfgwf4sWlE3JEHjldysA4Luldhh0wJU9WK6Jqjee9YmokFoRMuq7GzGxfwRdLG3qK8hwrxxpxf4RdJE0ratgzhOhFa5TTEQUzJrVVSBJwJ5kd4aEOyARX6AsYvNYBZ/ebsGP91swpK0OE7pxkoQuXLcmOiSlqvh3u7bYpEcz3/PsQ6NMiAqV8NosG87mCMSyRCz5Ub+W2kKT/3Y5kZqhYtVeF/q01KFVPQWvTDJ7X9eK97rkR23qK8ixAUlpKlRV4J+tDsTX1Po1/bHegff+smJLkgujO+kRyT51VM3xN4CIitS7hQ5mAwMSdPG0KZCN05KrlYiIiC6YxSghIUYukCEhEB0mIcRYePK3YyMd3p9iwbiuhou9mVQFefpIvO9eid6ruW8mYo0QCY+ONeJsjruhdRSnJsh/EuO07Oulu5z4a5MDqgDGdtaCrYPb6nH3cAOGtNWhdgTHHflPa/fcyc5jKrYddeHUOYHh7fV48WoTIizAx//YAQDX9uV5lohHXyIq0qNjTJj7ZCgbbtJF4wl+mQ1Aw1o8PREREflDizgZR0+ryLYKHD2tehtaEwWSp4/EwRQVFiPQNqHwYpNxXfTo0kR7PJYBCfIjSZLQt6WWpfP1EjvCzMCA1vlBsXsvM+H9KZZK3EKqijwlwHYcdWGBu1zTsHZ61I6Q8cwVWmZO58YKWrA8MREDEkRUNLNB4koluqg8F3DNYxUobGhNRETkF4lxCoQANh924WyO8PaPIAqkupH5JWC7NtFBX0TzVkmS8NJVJgxpq0PfFuzlRP7Vv5U2plIztFXqRj3vLyiw6kdLCDMD24+6sGCrA7GRkrdx+siOOrxxnRkvX22q5K0kCg486xMRUVAIt0h4fqIJDWtzooSIiMhfPCsx/9nqAAAGJOii6dZUh6OnHejZvPjVwAm1FK5Up4Do3kwHvQI4XFo2DlGgSZKE1vUUrNnvghDAjf0NkCTJ+9zozhyHRB68GiUioqBxdW+DN8WfiIiILlxinHbL9+82rXwEAxJ0sYzvqkezujKGtuMkHF18IUYJw9rr0DJeRsdGLJFDF0fr+lpWIgAM78D7WqLi8LeDiIiIiIioiooJlxEdJuF0ljZDUi+aZUvo4ujYSIc/nwit7M2gauyN67S6/Z5V6kSB5ilDXKeGhLb1GQgjKg6XxxAREREREVVhniwJgBkSRFR9SJLEYARdVO0TFOgVYFQnPWT2RSQqFjMkiIiIiIiIqrAWcQpW7HEhxAhEhnCChIiIKBBq15Ax7/9CUSuC51qikjAgQUREREREVIUluhtb14+WuVqYiIgogOJrMhORqDT8LSEiIiIiIqrCWrhLNtVjuSYiIiIiqmTMkCAiIiIiIqrCEmJkXN/PgAGtePtHRERERJWLV6RERERERERVmCxLeGqCqbI3g4iIiIiIJZuIiIiIiIiIiIiIiCjwGJAgIiIiIiIiIiIiIqKAY0CCiIiIiIiIiIiIiIgCjgEJIiIiIiIiIiIiIiIKOAYkiIiIiIiIiIiIiIgo4BiQoICz2WyYNm0abDZbZW8KVSMcd1QZOO6osnDsUWXguKPKwHFHlYHjjioLxx5VBo47CjRJCCEqeyOoasvMzERERAQyMjIQHh5e2ZtD1QTHHVUGjjuqLBx7VBk47qgycNxRZeC4o8rCsUeVgeOOAo0ZEkREREREREREREREFHAMSBARERERERERERERUcAxIEFERERERERERERERAHHgAQFnNFoxHPPPQej0VjZm0LVCMcdVQaOO6osHHtUGTjuqDJw3FFl4LijysKxR5WB444CjU2tiYiIiIiIiIiIiIgo4JghQUREREREREREREREAceABBERERERERERERERBRwDEuRDVVV89tln6NGjB8LDw2EymdC8eXM8/vjjOHfuXKHXHzp0CNdffz0aNGgAs9mMFi1aYOrUqXA4HGX6vClTpkCSJDidzmJfs2XLFowfPx6xsbGwWCzo0KEDPvnkE7DaWNUSjGMPAIQQGDx4MOLj4yuyWxTEgnHMzZgxAwMHDkRkZCQMBgMaNmyIu+66C8nJyRXdTQpCwTj2Zs6cib59+yIsLAyhoaHo3Lkzpk2bBpfLVdHdpCATjOOuICEEhg8fDkmS8O+//5Zn1yiIBdu427x5MyRJKvZP9+7dL2R3KUgE27gDgPT0dDz66KNo2rQpTCYTatasiXHjxmHz5s0V3U0KQsE09hISEko83nn+TJ8+/QL3mipbMI07j4ULF2LIkCGoUaMGjEYjmjZtiocffhhnz56t6G5SVSOI3Fwulxg/frwAICwWi+jfv78YOXKkiImJEQBEkyZNxKlTp7yv37Fjh4iMjBQARLdu3cSECRO8rx00aJBwOBwlft57770nAAgAxb528eLFwmg0ClmWRb9+/cTYsWNFWFiYACBuuOEGf+4+VaJgHHseDz30kAAg4uLi/LKvFByCcczdf//9AoDQ6/WiV69eYsyYMSI+Pl4AEDExMWLXrl1+/R5Q5QjGsffGG28IAEKn04kBAwaIUaNGiaioKAFADBkyRNjtdr9+D+jiC8Zxd75p06Z5v2bhwoUXtL8UHIJx3H3xxRcCgOjcubOYPHlyoT/PPfecP78FVAmCcdwlJSWJhIQEAUAkJCSI8ePHi1atWgkAwmQyiQ0bNvj1e0CVI9jG3gMPPFDkcW7y5Mli8ODB3vuO9evX+/17QRdPsI07IYT4+OOPhSRJQpIk0bt3bzF27FhRp04dAUA0btxYpKSk+PV7QJcmBiTIy3OB3rx5c3H48GHv45mZmWL06NECgJg4caL38Y4dOwoA4rPPPvM+lpGRIfr27SsAiLfffrvIz3E6neLxxx/3HsSKO5BZrVZRt25doSiK+Pvvv72PnzhxQiQmJgoA4vfff/fDnlNlC7axJ4QQOTk54oYbbvC+jgGJqiXYxty///7rDTxs2bLF+7jNZhO33367ACC6dOnihz2nyhZsY2/Hjh1ClmURHh4utm7d6n08PT3d+9kfffSRH/acKlOwjbvz7dq1S5jNZgYkqphgHHd33323ACDmzp3rn52koBOM427AgAECgLj77ruF0+n0Pv7qq68KAKJdu3YXttMUFIJx7BXF5XKJ/v37CwDi448/Lv+OUlAJtnGXlpYmzGazMBqNYsmSJd7Hc3JyxPDhwwUAceedd174jtMljwEJ8urVq1exF+hpaWlCkiSh1+tFbm6uWLRokQAgevToUei1Bw4cEJIkifr16wtVVX2eW7ZsmejcubMAIBo1alTigezLL78UAMTVV19d6LnFixcLAKJPnz4XsMcULIJt7M2ePVs0bdrU57UMSFQtwTbmJk+eLACIDz74oNBzNpvNu1r9wIEDF7DXFAyCbew99dRTAkCRq4J/+eUXAUCMHj264jtMQSHYxl1BdrtddOzYUURHR4uWLVsyIFGFBOO469GjhwAgTp486Z+dpKATbONuxYoV3oUl57+PEEK0b99eNG7cWKSlpV3AXlMwCLaxV5xXXnlFABBjxowp/05S0Am2cTdz5kwBQFx22WWFnlu1apUAIFq2bHkBe0xVBXtIkFdkZCQSExOLrJ0aHR2NyMhIOBwOnD59Gn/99RcAYOzYsYVe27hxY7Rt2xZHjx7F9u3bfZ4bPXo0NmzYgMmTJ2P9+vUlbo/nM8aNG1fouX79+iEyMhIrVqwosiYeXVqCaeydO3cO48aNw6FDh3D//fdj7ty5F7h3FIyCacwBgMViQatWrdC7d+9CzxkMBiQkJAAATpw4UdZdpCAVbGPvxRdfxL59+3DvvfcWei47OxsAoNPpyrx/FJyCbdwV9Nxzz2HTpk345JNPEBMTU849o2AWbONOVVVs27YNsbGxqFOnzgXsGQWzYBt3v/32GwDggQcegCRJhZ7fvHkzDhw4gOjo6DLvIwWnYBt7RTl27BhefvllhIaG4uOPPy7311PwCbZxJ8vaNHNycnKhvq9paWkAgKioqLLvIFVZDEiQ15w5c7B7927UrFmz0HMHDx5Eeno6DAYDYmJisHPnTgBA69ati3yvli1bAkChA9nIkSOxevVqfP/996UehEr6DFmWkZiYCCGE93V06QqmsSfLMq655hps27YN7777Lsxmc0V3i4JYMI05APjss8+wY8cOtGvXrtBzWVlZ2L17NwCwuXoVEGxjT1EUNG3atND27NmzBy+99BIA4IYbbijbzlHQCrZx57FixQr873//w7XXXovLL7+8PLtEl4BgG3d79+5FTk4OmjRpgldeeQVt2rSBxWJBXFwcbrvtNiQnJ1dkNynIBNu427hxIwCga9euOHv2LD7++GPcfvvtXPhUBQXb2CvKY489hry8PDz11FOIjY0t99dT8Am2cdenTx+EhIRg27ZtuP3223HkyBHk5ORgwYIFuOeeeyBJEh566KGK7CpVMVzyRmXy1FNPAQBGjRoFk8nkXaVbt27dIl/veTwlJcXn8R9++KHMn1nRz6Cq5WKPvfDw8HKNU6p6KuN4V5KXX34ZeXl56NixIxo2bOiX96TgFAxj74EHHsDatWuxdu1aWCwWTJs2rchVVFR1VNa4y8rKwvXXX4/Y2Fi8//775d1susRVxrjbtGkTAGDZsmVYu3Yt+vXrh/j4eGzYsAGff/45/vzzTyxevNg7IUNVT2WMuwMHDgAADh06hN69e/u817Rp0zBs2DD89ttvCA0NLfuO0CUnGK7xdu/ejV9++QU1a9bEfffdV+H3oUtHZYy7mjVr4tdff8X111+Pzz//HJ9//rn3ubi4OCxYsABDhgwp135Q1cQMCSrVO++8g19//RUWiwWvvPIKACAnJweAVmakKJ5V5Z5yDxVxMT6DgltljT2qvoJtzM2cORNvvvkmZFnGm2++6ff3p+ARLGPvq6++wpo1ayCEgCzL2LdvH3Jzc/32/hRcKnPc3XvvvUhKSsLXX3+NGjVqXNB70aWlssbd5s2bAQBdunTBoUOHsGDBAsybNw9JSUmYNGkSUlJSMGnSpEIlJqhqqKxxl5GRAQC46qqr0KJFC2zYsAGZmZn477//0LJlSyxYsAC33XZbhd+fgl+wXOO9++67EELgvvvuQ0hIiN/el4JTZY679u3b4/LLL4dOp0OPHj0wcuRI1KpVC8nJyXjjjTeQnp5+Qe9PVQMDElSid999Fw899BAkScKXX36JxMREAFqJBwBF1sEsSFXVCn/2xfgMCl6VOfaoegq2MTdjxgxMmjQJqqpi6tSpGDBggF/fn4JHsIw9IQR27dqFnJwcLF++HM2aNcMHH3zADIkqqjLH3cyZM/HNN9/g7rvvxuDBgyv8PnTpqcxxN3XqVOzfvx8LFizwKVUSEhKCL774AnFxcdi2bRuWLVtW4c+g4FSZ485mswEAatWqhfnz56NTp04ICwtDv379sGDBAlgsFvz000/Ys2dPhT+DglewXOOdO3cO3333HcxmM+655x6/vCcFr8ocd4cOHULXrl0xe/ZsrFq1CqtWrcLcuXNx+PBhXHfddVi4cCHGjx9f4fenqoMBCSqSEAKPPfYYHnzwQSiKgq+//hpXX32193lPSmleXl6RX+95/EJSTy/GZ1DwCYaxR9VLMI659957D1dffTUcDgdeeuklPPbYY357bwoewTb2JElCfHw8LBYLevfujYULF6JOnTr4999/8d9///nlM6jyVfa4O3nyJG6//XY0a9YMr7/+eoXegy49lT3uAECv16NJkyaIjIws9JzFYsHAgQMBABs2bKjwZ1BwCYZx51mJfOutt8JoNPo8Fx8fj1GjRgEAlixZUuHPoOATDGOvoLlz5yIvLw+jR49mQ+EqLBjG3dNPP43k5GS8/vrr6NKli/dxi8WCzz77DA0bNsSyZcuwfPnyCn8GVQ0MSFAheXl5uOKKK/DGG2/AbDZj5syZhRpaxsXFAQBOnTpV5HucPHkSQPG16criYnwGBZdgGXtUfQTbmHO5XLj33nvxwAMPQJIkfPzxx3j66acv+H0p+ATb2CtKZGSkd6LEU3udLm3BMO5eeeUVnDlzBhEREbj11ltx7bXXev/s3r0bAPDaa6/h2muv5c1qFREM464s6tSpAwAsU1dFBMu4i4mJAYBi+4AlJCQAAE6fPl3hz6DgEixjr6DZs2cDgM/kNFUtwTLuFi9eDAAYNmxYoedMJpM3+O8po0jVFwMS5CMzMxODBg3C77//jpiYGCxZsqTIUg2tW7cGAOzatavI99m5cycAoE2bNhXelpI+Q1VV7NmzB5IkoVWrVhX+DAoewTT2qHoItjHncDhwxRVX4IMPPkBISAhmzZqFO+6444Lek4JTMI29t956C1dffTUOHz5c5POe1ZwOh6PCn0HBIVjGnacu8fr16/HDDz/4/ElNTQUALFq0CD/88AMOHjxYoc+g4BEs4w4AHnroIUyYMAFHjhwp8vlDhw4B0Fat06UtmMad52uTk5OLfN4zMVirVq0KfwYFj2Aaex6qquKff/6B2WzGiBEjLvj9KPgE07g7d+4cAECn0xX5vOdxu91e4c+gKkIQudntdtG7d28BQDRu3FgcOHCg2NcuXbpUABB9+vQp9NyBAweEJEmiXr16QlXVEj8TgAAgHA5Hoee++eYbAUBcd911hZ5btGiRACB69epVhj2jYBdsY6+gw4cPCwAiLi6ubDtDl4RgHHNXXXWVACBiYmLE+vXry7dDdMkItrE3duxYAUC8/PLLRW5rYmKiACAWLlxYhr2jYBVs4644/fr143irQoJt3PXs2VMAEO+8806h506ePCnCwsKEoiji6NGjpe8cBa1gG3effvqpACB69OhR6Lm8vDxRr149AUDs2rWrDHtHwSzYxp7H9u3bBQDRs2fPsu8MXTKCbdy1b99eABDffPNNoeccDodo3rw5r/VICCEEAxLk9dRTTwkAok6dOuL48eMlvlZVVe+B5r333vM+npGRIfr27Vvo8eKUdCDLysoSsbGxQlEUMXPmTO/jJ0+eFC1atBAAxB9//FGOPaRgFWxjryAGJKqmYBtzn332mQAgLBaL2Lp1a/l3iC4ZwTb25s2b5x17q1ev9j6em5srpkyZIgCIjh07CpfLVY69pGATbOOuOAxIVC3BNu6+/fZbAUBERESITZs2eR/PzMwUw4cPFwDELbfcUo49pGAUbOMuMzNTxMXFCQDi2Wef9U70ORwOcdtttwkAYujQoeXcSwpGwTb2PL7++msBQNxzzz1l3xm6ZATbuPvkk08EAFG7dm2xZcsW7+M2m03ceeedAoBo3bo17y1ISEIIUUzyBFUjZ86cQf369ZGbm4t27dp5U7mK8tZbb6F27drYsmUL+vXrh8zMTHTs2BENGzbE8uXLkZqaipEjR2L27NnFpml5SJIEQCsFUdRr58+fjzFjxsDpdKJXr16Ijo7G4sWLkZmZidtvvx2ffPLJhe04VbpgHXseSUlJaNiwIeLi4nD8+PGK7SQFlWAbc06nEw0aNMCJEyfQpEkTdOvWrdj3ePLJJ1mm7hIWbGPP49FHH8Wbb74JWZbRq1cvREREYMOGDTh16hQaNmyIxYsXe2tc06UnWMddUfr374+lS5di4cKFGDx4cNl3koJOMI47IQRuvPFGfPvtt9DpdOjVqxeioqKwbNkynDlzBr1798b8+fMREhJy4d8AqhTBOO4AYOXKlRgxYgQyMzPRpEkTtG7dGlu2bEFSUhIaNGiApUuXokGDBhe281SpgnXsAcBzzz2HF198ES+99BJ701UxwTjuhBC44YYb8N1330GWZfTu3Rs1atTAxo0bkZycjLp162Lx4sVITEy88G8AXdoqNx5CwWLmzJneKGdpf/bv3+/9ur1794qrrrpKREdHC7PZLFq3bi3eeOMNYbVay/S5KENEf926dWLkyJGiRo0aIjQ0VHTs2FF88cUXjKhWEcE89oRghkRVFGxjbuPGjWXeHq4avrQF29gr6Pfffxf9+/cXYWFhwmg0isTERPHUU0+Js2fPXuhuUyUL5nF3PmZIVB3BPO6mT58uevToIUJCQoTZbBbt2rUTb775prDb7Re831S5gnncHT58WNxyyy0iLi5OGAwGkZCQIO6//36Rmpp6wftNlS+Yx55nVfr7779/wftJwSWYx90PP/wg+vXrJ8LDw4XBYBCNGzcW999/vzh16tQF7zdVDcyQICIiIiIiIiIiIiKigJMrewOIiIiIiIiIiIiIiKjqY0CCiIiIiIiIiIiIiIgCjgEJIiIiIiIiIiIiIiIKOAYkiIiIiIiIiIiIiIgo4BiQICIiIiIiIiIiIiKigGNAgoiIiIiIiIiIiIiIAo4BCSIiIiIiIiIiIiIiCjgGJIiIiIiIiIiIiIiIKOAYkCAiIiIiIiIiIiIiooBjQIKIiIiIiIiIiIiIiAKOAQkiIiIiIiIiIiIiIgo4BiSIiIiIiIiIiIiIiCjgGJAgIiIiIiIiIiIiIqKAY0CCiIiIiIiIiIiIiIgCjgEJIiIiIiIiIiIiIiIKOAYkiIiIiIiIiIiIiIgo4BiQICIiIiIiIiIiIiKigGNAgoiIiIiIiIiIiIiIAo4BCSIiIiIiIiIiIiIiCjgGJIiIiIiIiIiIiIiIKOAYkCAiIiIiIiIiIiIiooBjQIKIiIiIiIiIiIiIiAKOAQkiIiIiIiIiIiIiIgo4BiSIiIiIiIiIiIiIiCjgGJAgIiIiIiIiIiIiIqKAY0CCiIiIiIiIiIiIiIgCjgEJIiIiIiIiIiIiIiIKOAYkiIiIiIiIymH69OmQJAmSJKF///6VvTlERERERJcMBiSIiIiIiIiIiIiIiCjgdJW9AURERERERJeSNm3a4PHHHwcANGrUqJK3hoiIiIjo0iEJIURlbwQREREREREREREREVVtLNlERERERFTNPP/8894eCJ988gleffVVxMTEwGKxoG/fvgAAq9WK119/HW3btoXZbEZkZCT69++PH3/8EcWtaVq8eDEGDhyIsLAwREZGYsKECThw4ABefvll7+dNnz7d+/r+/ft7H9+wYQOuu+46hISEoEaNGt4MBAA4deoU7r33XiQkJMBoNKJOnTq4/PLLsXbt2iK347///sP48eMRGxsLg8EAs9mMZs2a4e6770ZycnKh12dnZ+P5559H+/btERoaCp1Oh+joaAwcOBA//PBDodeX1kMiJycHb7/9Nrp27YrIyEiEhISgTZs2eOaZZ3D27NlCr7/xxhu97zd//nxs374d48aNQ2RkJEJDQzFo0CCsWLGiyH0lIiIiIrqUsGQTEREREVE19sknn2Dr1q3e/9evXx9ZWVkYOnQo1qxZ433carVi6dKlWLp0Kf755x98/fXXkCTJ+/yXX36JW2+91SdYMWvWLCxZsgTDhw8vdTtuueUWn+1o2rQpAGDHjh0YPHgwUlJSvM+lpKTg999/x+zZs/Hpp5/illtu8T73008/YfLkyT7b4XA4sH//fuzfvx9z587FihUrUK9ePQBAXl4e+vTpgy1btvhsz5kzZ7BkyRIsWbIEBw8exLPPPlvqPgDAoUOHMGrUKOzevdvn8R07dmDHjh2YPn06/v77b7Rp06bIr1+6dCneffddWK1W72OLFy/GihUrsGzZMnTr1q1M20FEREREFIyYIUFEREREVI1t3boVdevWxT333IO+ffti0qRJeOCBB7zBiJCQENx44424/vrrYTabAQDffPMNPv/8c+97JCUl4e677/YGAeLi4nDbbbdhzJgxyMjIwM8//1ym7ejQoQPuuecetG7dGpdffjmcTieuuOIKbzCiQYMGuPPOOzFixAgAgKqquOuuu7Bjxw7v+zz88MPe7RgwYADuv/9+3HrrrYiOjgYAHD16FE8//bT39d999503GBETE4Obb74ZDz74IAYNGuR9zQsvvICDBw+Wug8OhwNXXnmlNxgRGhqKSZMm4cYbb/R+/vHjxzFy5EicO3euyPd47bXXYDKZcOutt+LKK6/0Bn3sdjumTZtW6jYQEREREQUzZkgQEREREVVz8+fPR9u2bQEAJ0+exNixYwEABoMBq1at8j535513olevXlBVFW+++SZuu+02AFp2hM1mAwAkJCRg48aNiIqKAqBN+F9//fWlbkNcXBxWrlzpDXoAwIwZM7B3714AWsbEpk2bEBoaCgB444038Nhjj8HhcOC9997D559/DqvVipMnTwIA6tWrh0WLFnkn9B977DHcc889aNGiBTp27Oj9jEOHDnn/PXXqVEyZMsX7///7v//DkSNH0LJlS7hcrlL3YcaMGdi4cSMAIDw8HGvXrkViYqL3+9qzZ08kJSXh2LFjePfdd/H8888Xeo/Q0FBs2LABjRs39n5f3nnnHQDAzp07S90GIiIiIqJgxgwJIiIiIqJqrGXLlt6AA6CVDPJMvvfu3dvnue7du6NFixYAgP379yMpKQkAsHLlSu9r7r77bm8wAgCuu+46NGvWrNTtGDt2rE8wAgAWLVrk8z6eYAQA3HTTTd5/L1y4EABgMpm823fs2DG0aNECjzzyCP744w9ERkZi/vz5eOedd3Ddddd5v7ZgcOKee+7B+PHj8cEHH2Dz5s146aWX8P333+Opp54q0z78+uuv3n/fd9993mAEANStW9cnM2PWrFlFvseYMWO8wQgA6Nevn/ffWVlZpW4DEREREVEwY0CCiIiIiKgaS0hI8Pn/0aNHvf9evHixt9my50/BVfqe0kQnTpzwPta8efNCn+EJEpRnO87flmeffdZnO2JiYrzPHTlyBLm5uQCAr7/+GjVr1gQA7N27F2+99RbGjRuHmJgYdO3aFZ9++imcTqf3aydOnOjN4LBarZg9ezbuvfdedOzYEdHR0ZgyZUqhfhDFOXDggPffBQMdRT1W8LUFeXpbeISFhXn/XZYsDSIiIiKiYMaSTURERERE1Vh4eLjP/wtOeptMJkRERBT7tZ4yTQUbSKuq6pftOH9bwsPDC2VQFJSdnQ2LxYJu3brhwIED+O677zB79mysXLkSNpsNQgisX78e69evx19//YU//vjDG9z45ptvcOutt+KHH37A/PnzvZkfZ8+exVdffYXvv/8eS5YsQc+ePUvcB71eX+LzBb9PBRuCF2QymXz+L8tcQ0ZEREREVQevbomIiIiIqjGDweDz/7i4OO+/+/bti1OnTvn8SU5O9v573LhxAIDY2Fjv1+zbt8/n/YQQZep9cP52nL8tTzzxhM92nDhxwuf/tWrV8r7W00x60aJFyMjIwIoVK/DCCy94P2POnDlYu3atz2e1aNEC06ZNw+HDh3HkyBH8+OOP6N27NwCtofSLL75Y6j40aNDA++/NmzcXer7gY02bNi31/YiIiIiIqhoGJIiIiIiIqrHzV+r369fP+9h///2HLVu2eJ/bunUrQkJC0KZNG1xzzTXIy8sDAPTp08f7mo8++gjp6ene/3/66afFlicqaTsAoH///t5/f/XVV8jMzPT+/4MPPkB4eDi6deuGZ599FgCwbNkytG7dGiEhIejatSuysrJgNBrRq1cvPPPMM2jYsKH3648dOwZA601Ru3ZtREdH47PPPgMA1K9fH5MmTcKdd95Z6PUlGTNmjPff06ZN8wnOnDp1Cq+88or3/xMmTCj1/YiIiIiIqhqWbCIiIiIiIq8GDRpgwoQJmDlzJux2O3r06IHLL78c4eHhmDFjBmw2G3bs2IGWLVt6SyjdcssteOONN2C1WpGUlIR27dph5MiROHr0KObNm1fhbbniiivw9NNP49ixYzhw4ABatGiB0aNHIysrCzNmzIDD4cC6detw4403AgA6d+6MU6dOwW634/Dhw2jTpg0uu+wy6PV6rFy5Env37gUA6HQ6dOvWDQDQpk0bfP/99wCA+++/H/Pnz0fTpk2RkpKCP//807stnmyJklx11VX43//+h3379iEjIwOdOnXCmDFjYDQaMXfuXKSlpXm/x/fff3+Fvy9ERERERJcqBiSIiIiIiMjHJ598gj179mDnzp2wWq344YcffJ5v06YNPvzwQ+//69evjw8++AC33HILAOD48eP49NNPAQANGzZEfHw8li9fDqB8PREsFgt+++03DBkyBJmZmThx4oT3fT0mT56M22+/3fv6P//8E8OGDUN2djaOHDmCTz75xOf1kiTh7bffRv369QEADz/8MDZu3Ihff/0VLpcLc+fOLbQdiYmJePnll0vd3pCQEMyaNQtjxozBwYMHkZ2djR9//NHnNfXr18dff/1VZM8MIiIiIqKqjiWbiIiIiIjIR3R0NNatW4dXXnkFHTp0QGhoKEJDQ9GmTRu8/PLLWLFiBaKjo32+ZsqUKfj777/Ru3dvhISEoGbNmrjxxhuxZs0an9eGhISUa1u6du2KHTt24O6770bjxo1hMpkQHR2NXr164dtvv8W3337rE+To2bMndu3ahYcffhht2rRBjRo1oNfrERcXhyuuuAJLly7Fvffe6329oij46aefMHPmTAwdOhQNGjSAXq9HWFgYOnXqhBdffBHr169HTExMmba3ZcuW2Lp1K95880107doVNWrUgNlsRqtWrfDMM89g69ataNWqVbm+B0REREREVYUkhBCVvRFERERERHTpWrZsGdatW4eaNWsiOjoal112GXQ6LRnbarWiQ4cO2LNnDwBgw4YN6NSpU2VuLhERERERVRKWbCIiIiIioguSlJSERx991Pv/Pn36oEuXLrBarViyZIk3GBEfH4/27dtX0lYSEREREVFlY4YEERERERFdkLy8PPTs2RNbtmwp9jU6nQ6//PILJkyYcPE2jIiIiIiIggoDEkREREREdMGys7Mxbdo0zJkzB/v27UNmZiZ0Oh3q1KmDPn364IEHHkDHjh0rezOJiIiIiKgSMSBBREREREREREREREQBJ1f2BhARERERERERERERUdXHgAQREREREREREREREQUcAxJERERERERERERERBRwDEgQEREREREREREREVHAMSBBREREREREREREREQBx4AEEREREREREREREREFHAMSREREREREREREREQUcAxIEBERERERERERERFRwDEgQUREREREREREREREAceABBERERERERERERERBRwDEkREREREREREREREFHAMSBARERERERERERERUcAxIEFERERERERERERERAHHgAQREREREREREREREQUcAxJERERERERERERERBRwDEgQEREREREREREREVHAMSBBREREREREREREREQBx4AEEREREREREREREREFHAMSREREREREREREREQUcAxIEBERERERERERERFRwDEgQUREREREREREREREAceABBERERERERERERERBRwDEkREREREREREREREFHAMSBARERERERERERERUcAxIEFERERERERERERERAHHgAQREREREREREREREQUcAxJERERERERERERERBRwDEgQEREREREREREREVHAMSBBREREREREREREREQBx4AEEREREREREREREREFHAMSdFFkZGRU9iZQNcRxR5WB444qC8ceVQaOO6oMHHdUGTjuqLJw7FFl4LijQGJAgi4Kl8tV2ZtA1RDHHVUGjjuqLBx7VBk47qgycNxRZeC4o8rCsUeVgeOOAokBCSIiIiIiIiIiIiIiCjgGJIiIiIiIiIiIiIiIKOAYkCAiIiIiIiIiIiIiooBjQIKIiIiIiIiIiIiIiAKOAQkiIiIiIiIiqnJsDoHLXsnGTyvslb0pRERE5MaABBERERERERFVOcnpKg6nqtiS5KrsTSEiIiI3BiSIiIiIiIiIqMo5nSUAAFl5opK3hKoTIQRcKsccEVFxGJAgIiIiIiIioirndKY2KZzJgARdRIt3ONH+kSycSFcre1OIiIISAxJEREREREREVOWcztQmhJkhQRfTlsMuOFzAwRQGJIiIisKABBERERERERFVOWks2USVICWDmTlERCVhQIKIiIiIiIiIqhyWbKLKkJqhZUZk5nLcEREVhQEJIiIiIiIiIqpy0twlm7KtYJNhumg8GRIZDEgQERWJAQkiIiIiIiIiqnJOZ+VPCOdYK3FDqFpJ8WRIMDOHLqI9yS5sP+qq7M0gKhMGJIiIiIiIiIioyvGUbAI4OUwXR7ZVINem/Zslm+hieuKHPDz6XV5lbwZRmegqewOIiIiIiIiIiPzJpQqkZ+dPCLOxNV0Mnv4RAEs20cUjhMDR0yp0XHZOlwgOVSIiIiIiIiKqUtKzBVQB7wRdoDMk7E6BZ3/Jw2Pf5cHm4ER0dZVyjlk5dPFl5gG5NiDLCqjsl0OXAGZIEBEREREREVGVkuYu11QvWsbhVDWgGRJWu8D9X+dh6S4nACDLKvDeTWYYdFLAPpOCU2pmfoYESzbRxXLyrDbuhACyrUC4pZI3iKgUzJAgIiIiIiIioirltHtiuFFtbdojUKvVc2wCd3yWi6W7nBjfVY+xXfRYssOJR7/Lg9PFCenqxpMhoZOBDGZI0EVyIr1AIIzjji4BzJAgIiIiIiKiS8binRK+Xp6N7++zIDKUa+yoaJ4MiUa1ZCwCkB2ASTohBO77Khdr9rtwTW89nr7cBFVo5ZvmbXYiwmLFi1eZ/f65FLxS3D0kEmrJOHVOLeXVRP5x4ixLhdGlhVdvREREREREdMnYcVzCwRQVf292VvamUBA7naVNyjX0Zkj4/zP+3uzEyj0ujOuqxzNXmCDLEnSKhNevM6NdgoLf1zhgtXNysDpJzRCQJaBhLRnZVjBLhi4KT8kmgAEJujQwIEFERERERESXDIdL+/uvjY7K3RAKaqc9GRLugMSF9pD4da2EH5bbIYT2Pjk2gf/NtiLCIuHxcUZIUn6/CL0iYUArHZwqsCvZdUGfS5eW1AyB6HAJkaHaeAhk7xIijxNn2buELi0s2URERERERESXDLs7MWLTYReS01XERXGdHRV2OstdOidGAXBhq4YdLoGPFsqwu6w4fkbFY2ON+OQfG1IzBJ6faEJkSOEx2K6B9rlbk1zo2JBTL9VFSoaKWuESIsxaQCIjVyAytJI3iqq8giWbLkYQbPluJ977y4omdRW0ra+ga1MFTeooAf9cqjp4ViQiIiIiIqJLhr3AgvO/Nzlw62Bj5W0MBa20TIHIEAkRFkCWLmySbt8JFXaXBKMe+HqJHSfOqli83YmW8TIm9tQX+TVtGiiQJC0gQdWDSxVIyxRoVU9BuEULSASiVBjR+U6eVaHIgEvVgmCB9tMKO3YcU7HjmIrZ67RsxX+eCUX9aC4QoLK5qCNl6dKlGDp0KGrVqoWwsDD07NkTv/76a7neIzMzE08//TQSExNhNpsRHx+PO++8E6mpqUW+3maz4X//+x/at2+PkJAQWCwWtG3bFq+88gqsVmuh12/evBmSJBX7p3v37hXadyIiIiIiIrpwDneGRLgZ+GtTftmmxTscuO2TXJzOZCNZ0gIS0eHafXyYWSpzhsTMNXas2e/bn2T7ES2oMPUaM/q0ULBgixMOF/D05SYoslTU2yDUJKFJHZkBiWokPVvApQK1IyRvQCIQk8NZeQJ3fp6LDQfZR4cAu1MLhPmrPF1pnC6BdQecaJ+gYNUrobhrmAEAsOs4j3VUdhctQ+KHH37AddddB51Oh4EDB0JRFCxatAhXXXUVdu7ciRdeeKHU98jKysKAAQOwadMmNG7cGKNGjcL27dvxySefYM6cOVizZg3i4+O9r8/NzcXgwYOxevVqREREoFevXpAkCWvWrMHTTz+NOXPmYNGiRQgJCfF+zaZNmwAAnTt3RvPmzQttQ5MmTfzw3SAiIiIiIqKKsDsBnQwMbafHb2scOHjKhRwb8OD0PNgcwBeL7HhivKmyN5Mq2elM1Vs2KdwMZFtLn6TLtgo887MVTerI+POJ/Do7249qE21dmigY3NaCN/+0ISpUQsdGJU+ptE9QMGO1AynnVNSuwZXDVV1KhjbGakXI3pJNgWgw/OcGB5bscOJwqoo/Hw+BQVd0UIyqh5RzAkIAibEK9p9UkRHggMSu4yqyrUD3ZgqiQmUMaKXHRwvsOJzCxQBUdhclIJGSkoJbb70VISEhWLp0KTp27AgA2LNnD/r374+XXnoJY8eO9T5enOeeew6bNm3C9ddfjy+//BI6nQ6qquKRRx7BO++8g7vvvht//PGH9/VTp07F6tWr0atXL8yaNQsxMTEAgNTUVIwZMwZr167FCy+8gNdff937NZs3bwYAPP/88xg5cqS/vxVERERERER0AewuwKAHRnbUAhJfLrZj2S4nJAD1akr4eaUdtw42oGYYJ4Crq1ybQI4NiA7XxkBZMyS2H3FBFcC+kyrSMlXEuL9+21EXakcI7/+fmlC2gFe7BlpAYusRF4YyIFHlpZ7TJmRrFciQuNAGw9uPulDH4vvYH+vtAICkVBU/rbDjhv4sW1edeRpaN4+TMWcjkJVb9q8VQmD7URXzNjuQni0wtJ0OfVroSgxyrdmnZeZ0b6ZNKTd0Z2YcYkCCyuGinBE//PBD5OXl4Z577vEJOiQmJmLq1KkQQuDdd98t8T0yMzPx2WefwWKx4N1334VOpw18WZbxxhtvoFGjRvjzzz9x8OBB79d8/fXXAIBPP/3UG4wAgFq1auHDDz8EAPz0008+n+PJkOjUqVPFd5iIiIiIiIgCwu6UYNBJ6NpUQUy4hN/XOnA6S+C1a814ZIwJVgcwfYm9sjeTKtGZLG0SODpcm1QLN0tlKmOypUB5pTX7tH/n2AQOnlLRIrb8E8vtEvIbW1PVl+rOkKgdIZerZNPeEy70fjoLO475jpNNh5yY+FYOvl6aP3V3KMWFbUdUXNZBh9hICR/Ot+FsTv5E8NHTKuzOwPcQoOBx0h2QSIiRYdCVPSvnn60ODHoxG1e+nYOvl9jxx3oH7v4iD32eycZdn+fi9q8UDH4xC+Nfz/bJMFuzzwmjHujgPr6FmiTUjpBwMIXHOSq7ixKQ+OuvvwAA48aNK/TcuHHjIEkS5s6dW+J7LF26FDk5Oejbty8iIyN9nlMUBaNHj/b5rOzsbDRu3Bjt2rVDq1atCr1fs2bNAAAnT570PqaqKrZt24bY2FjUqVOn7DtIREREREREF4XDCRh1gCJLGNFBayj84CgjhrfXY0hbHZrWkfHDcrvPJB1VL2lZ2s8+OkybFA4zS8jKA1S15Im6LUkuKO5ZktXuVcC7jmlZEy3jyj/J27i2jFATsPUIJ+qqg5SM/AyJ8pRsWrzdidNZAst2+faE8ATIflot4Yx7TP+5Qeubc1VPAx4abUJmHvDBPBuOn1Fxzxe5GPpSNj5daPPbPlHwO3lWG2N1I2VEWMreL+frJXacyRK4aYABvz4UgiXPh+KRMUbUipCwZKcTyemALAG7k1X8vkYL8tudAhsPu9CxoQKjPj+LolFtGYdT1VKPsUQeAQ9ICCGwa9cuAEDr1q0LPR8ZGYk6derg7NmzSE5OLvZ9du7cWex7AEDLli0BANu3bwcAhIaGYunSpdiyZUuRr1+3bh0AIC4uzvvY3r17kZOTgyZNmuCVV15BmzZtYLFYEBcXh9tuu63E7SMiIiIiIqLAs7sAg7v48L0jjPjiTgtuG6w11ZRlCbcPNSLXDnz7H7Mkqqs090r1mAIlm1QB5JYwJIQQ2JLkQut6ChrWkrF6rxNCCGxz949oGVf81xZHliW0ra9gx1EXHC5O1FV1KUVkSJSlZJMnYLX7vKbAe09oQYg8h4TPFtqhqgJ/rnegbg0JXZsoGNlRh3YJCn5e6cCIV7Px73YnZAlYsYcBsOrEU7IpNkpyB19LH3MuVWBvsgvtExQ8Ps6Etg0U1I2UccsgI+Y8EYqdb4dh7iMu/Pl4KKJCJXyz1A6nS2DzYRdsjvxyTR6NasvIs+f/DhCVJuABibNnz8JqtSIsLMyneXRBdevWBaD1mijOiRMnfF5bkffwUFUVTz/9NADgiiuu8D7uKde0bNkyvPTSS4iNjUW/fv1gt9vx+eefo1OnTt7gChEREREREV18die89a1DTRJ6J+ogSfkrNS/roENCLRnfLbNj/0lOzFVHp7M8AYn8DAmg5MnhpDQVGbkC7RMU9Giu4OQ5gaQ0FduPuCBJQPO6FZtoa5egwOoA9p1gxk5Vl5KhwmwAwsxAmAmQpNJLNgkhsM0dkNiT7Hu82pPsQp0aEhLrCvy4wo4/Nzhw4qzAqM56yLIESZLw1HgjJAANomVMv9uCPi102HXMBaudE8PVxYmz2rirYZEQbpbKVCbsSJqKXDvQIk4p8nlZ1o6ZJoOEa3obkJwusHCb09s/okehgIT2PgfZR4LKKOABiZycHACAxWIp9jVmsxmAVmapou9TlvfweOihh7BmzRrUrl0bTzzxhPdxT0PrLl264NChQ1iwYAHmzZuHpKQkTJo0CSkpKZg0aRKEKPqX22azITMz0+ePzcZUOSIiIiIiIn9xOAGjvvjnFVnCgyONyLEB417PwdRZ1jKtGKWq43Smu2RTgR4SAJBlLX4ceMrjtEtQ0Ku5Ntm2eq8L24+60Li2jJAK9g1mH4nqIzVDoFaEDEmSIMsSwkyll2w6fkYgPVt7zbEzwnussjsFDqWoSIxTcNtAFQ4X8MzPVgDA2C75B8B2CTr890IoZj0Wgu7NdOjYSIHDhUL9KKjqOnlWoG6kNu7K2i9nd7J2jGwZX/q08DV99DDqga8W27B6nwuhpsJf16iWp7E1xx2Vja70l1wYRdFOvgVXrBRHVYuPpJX1fUp6DyEEHn74Ybz33nswmUz49ddffZpdT506FXfccQdq1qzp06ciJCQEX3zxBZYtW4Zt27Zh2bJl6NevX6H3nzp1Kl544QWfxx5//HE89thjJW5zdXD27NnK3gSqhjjuqDJw3FFl4dijysBxR5XB5pAgAUhPTy/2NV3qA59PAd6ep+Cb/+yYs96Gtya70LzohHuqYo6nyQBk6FwZSE8HFCEBUJCckoEYU9Ffs3aP9jUJkVkINQKypGD22jwkp0sY0d5V4eNdgwgA0GHdvlwMb1X6Akq6dJ06q6BJ7fxjU6hRQXqWs8Rj1cqd2thsXEvgYKqE9XvOon0DYP8pwOHSISHKjmZR6WjfIBpbjkhIjBWIMmjj2kMGkJmh/btJNADosGJnFhpFMhBb1QkBnEhX0La+QHp6Okw6GVaHjFOp6d7ShkXZtF873sWGZaO44VnwmHdZWxmzN2r/7t1cRWaG7/EwygQAOuw+mof09JwL2ie6tEVFRZXpdQEPSISGhgIA8vLyin2N5znPayvyPqW9h81mw80334wff/wRFosFs2bNQt++fX1eo9fr0aRJkyK/3mKxYODAgfjuu++wYcOGIgMSTz75JB566CGfx4xGI4zGCi6lqGLKOiiJ/InjjioDxx1VFo49qgwcd3SxOdQMhJh0iIqKKPF1vaOAnq0EZq1z4IUZVjz6kx4/PxiC+JoBLxRAlSzLngu94kSD2EhIkoQ6Ne0ArIA+DFFRRafX7D6ZjVoRAi0StK9p0yDHm9XQpakFkZFqhY53UVFAg5hsbEySkKOGo140x19VZLULZFmzEBedf2yKDMvG2RxR4rHq0BkrADuu6WvGS79ZkZwVioFRBpw8oI3Z9k1CEBWl4okJIbhuWi6u62dGVJSh2PfrFSqgV7Kw+6QRUVHFVyqhquFstgqrIxsNahkQFWVGdEQeAAd0phqICi/+WHP4TA6MehfaN42ETil+4bfnmHfbMBf+2JQDIYC+rcyIivKd54yMFAgxZiH5nB5RUUWX6ycqKOBnwrCwMISFhSEjI6PYYMLJkycBFN8fAshvPn3q1Klyv8eZM2cwePBg/Pjjj6hZsyb+/fdfDB06tFz7AQB16tQBAOTm5hb5vNFoRHh4uM8fBiOIiIiIiIj8x+FEiSs/C5JlCZd3N+CN68w4ky0w5eNcnM1mjeuq7nSmiugwyVthwVuyqZhSJtlWgf0nVbRPULxf07NZfm311vWLrrNeVpP76JGWKTD29Wz8vtZebBlounSlZuY3tPaIMEulNrXeesSFCAswvL12UNtz3NNPQjtOJcZq79c+QYfVr4ZhQrcS6tVBq/nfMl7BliQnVJXjrKo7cVb7GcdGauPE2y+nhLJNQgjsPq6ieaxcYjCioEa1FQxopY3R8/tHAFo1m8Z1ZBxO5fmVyibgAQlJktCqVSsAwO7duws9n56ejlOnTiEyMtIbdChK69atAaDYptI7d+4EALRp08bn8eTkZPTs2RMrVqxA48aNsXr1avTo0aPI93jooYcwYcIEHDlypMjnDx06BACIj48vdjuJiIiIiIgocAo2tS6rYe31+L8JJhxJU3HHZ3nIY8PXKi0tUyAmIn+MhHon6Yp+/Y6jLqgiv98DAPR095HQK0Dz2AubOrm+nxFf3mlBqEnCUz9a8dSP1gt6Pwo+Kee0idhaBcZduEVCthVwFRMYsDsFdh93oW0DBTXDZNSKkLDb3dh67wkXLAagfoGMmlCTVKZy6B0bKcjIBQ5xcrjKO3lW+xnXjXQHXy2lByRSMgTO5ohiG1oX58WrTPjwFjOa1i366xrWUpCWKUoNwhEBFyEgAQCXXXYZAGD27NmFnps9ezaEEBgxYkSJ79GnTx+EhITgv//+Q0ZGhs9zLpcLc+bMgSRJGD58uPfxc+fOYdCgQdi3bx+6dOmC1atXo2nTpsV+xtq1azFr1izMmjWr0HOnTp3CP//8A0VRMHjw4BK3lYiIiIiIiPzPpQq4hFRiU+viXNvXgNsGG7D1iAvvzrX5f+MoKKiqwJksgeiw/OmO0jIkPA2tOxQISLRLUBBi1LIjyhsAK0qvRB3+fDwU3ZoqmLXOgQOn2Py1KknJKJwhUdrk8J5kFXYn0LaBNu5axCnYf1KFwyWwJ1lF01gFslz+sdexofZ+Gw9xjFV1J7wBCW3c5R/riv+a3e4snBbx5QtIRIfLGNSm+JNvo9ruxtapHHdUuosSkLj55pthsVjw9ttvY9WqVd7H9+7di//7v/8DADz66KPex0+ePIk9e/Z4yzABWg+HKVOmICsrC7fffjvsdjsALdXosccew+HDhzFu3Dg0a9bM+zV333039u7di5YtW2LRokU+DayLcscddwAAnn/+eWzevNn7eFZWFm666Sbv3/Xq1buA7wYRERERERFVhN2p/V3RCeL7RxrRoaGCb5fZsemw049bRsEiPVvAqQI1w/LHSFgZAhJ6BWhVL3+CzqCT8PXdIXj1mmK6YFdAjRAJdw3TyjrP2eDw2/tS5UvJKJwhEeHJzCm66je2HtEmbtu5AxKJcTIcLmDNPhfO5ghvuaby6uAOSGxiQKLKO5GuHdPionwDEhklZCnsdpcDa1nOgERpGnsCEinMzKHSBbypNaCVOJo2bRpuvfVW9O3bFwMGDIDRaMSiRYtgtVoxdepUtGvXzvv6J598Et988w1uuOEGTJ8+3fv4Sy+9hMWLF+OXX37BmjVr0KVLF+zYsQN79uxBQkICPvzwQ+9rd+3ahZ9++gkAEBkZiTvvvLPY7fv2228hyzKuvfZa/Pvvv/j222/RtWtX9OrVC1FRUVi2bBnOnDmD3r1749133/X794eIiIiIiIhKZ3PP4Za1h8T5FFnCK5NMGPd6Dv7vRytmPxYCo/7CV79T8Fi1Vws0FQwulJQhkZkrsPmwEy3i5UJjwbNy3Z+6NFZQp4aEORsduH+EsVwr4O1OgVPnhE8ZHwoOJ9K1SdjYqCIyJIqZHN7mzsxpUyBDAgBmrdMW4CaWs6SOR3S4jAYxMjYz6FrlnTynQpLyA2GlZYMBWoaEIgPN6vr3ONKIAQkqh4sSkACAKVOmID4+Hq+99hrWrFkDRVHQsWNHPPzww5gwYUKZ3iM8PBzLly/Hyy+/jN9++w1z5sxBXFwc7rrrLjzzzDPeptMAMH/+fG+jqJUrV2LlypXFvu/06dMhyzIkScI333yDgQMH4tNPP8WGDRugqiqaNWuGJ598Evfddx/0+grkBhMREREREdEFszu1e7yKBiQArTnnvZcZ8dYcGz6YZ8PDY/y3Aj4YrdjjxMw1dqRmCKRlqmgQI+PT2ywVKgVzKZi3xQlFBoa2yx8kIUZAkgqXzhFC4Kkf85CRC1ze3XBRtk+WJYzsqMeXi+3YfNiFTo3LPphf+s2K39c68OtDIT4BF6p8yekCegWICfftIQEAGcVMDm874kKDaBmRIdpEbmKc9vei7U6f/1dEx4ZaabC0TBUx4QxgVVUnz6qICZe8WYPeMVdChsSu4y40qiXDZPDvOaBetAydzIAElc1FPSoNGzYMS5YsQVZWFs6dO4eVK1cWGYyYPn06hBA+2REeNWrUwJtvvomkpCRYrVYcPHgQH374oU8wAtAaVAshyvRHp/O9ALjhhhuwatUqZGdnIzc3F1u2bMHDDz/MYAQREREREVElutCSTR43DTCgdT0ZXy62Y+WeC19FnGcX3gVxweREuor7vszFvM1OHE5VYXMAy3e7vBOeVU1mrsCK3U50a6ogKjR/ukOWJYSZCq8a/mqJHf9ud2JERx2u7HHx7vfHdNY+a87GspdtOnDKhZlrHHCpwDtz2RQ72CSfUREbKUORiyrZVPjYcDZHxZHTqk8j9frRMiwGLRNMkoBmsRUPOnVsdHHLNqVmqDh1jhPRF5MQAkfSVMRHlb1fTkauQHK6KHf/iLLQKxLqx8hspk5lwjAp0SVq+xEX9iSzJiQRERERVR+eDImKNLUuSKdImDrZjFATcOfnuVi8o/SJ4dQMFbd/mottR3yvwU+dU9Hr/7Lw/fLg6wnwyu9W5NqBz++wYNUrYZj5SAgMOuDzRbagDKBcqEU7HHC4gMs6FB4goWbJJ0Ni/QEn3p5jQ+PaMl662gxJungZI83jFDStK2PeZqd3TJfm7Tk2qELrN7Bijwtr91fNoNKlSAiB5HQVcTV9x1BJJZu2uss1FSwLJssSmrvLNNWvKSPEWPEx2ckdkHjuVyumzrJi34nAzR0IIXDTh7n/z955h0dRvW34npntm0JCIPTeOwgISBekCtg7Knbsvf/s+tmw9y6KBRUQUFS6SJfem3QCpJCyfWfm+2N2NwnpIRXOfV1cwPbdOXNm5n3O+zxc9577tJxXqippLp10NzSvU/wg9a2HwoHW5VMOblZb5kCyVux5TXDmIgQJgaCacveXbh6b4qnsjyEQCAQCgUAgEFQYZdUhAdCyrsJXdzhxWiXu+szD72sLFxSe+8nLoi1Bvlnsz3X7vI1B3H6YU8TzK5p5GwPM2xhkRFcT/doargAJMTIXnW1mwz6NFbtOv8VNc9Yadk1DOuW1QYqxS5FVw0FV54GvPVhN8NYE+ykVfkvL+WeZSQ91dBTFv7uDzN8UZHgXE6+Nt2OSYdKs/EWlLK/OzNUBURiuQNJcOm5/drBwmMIsm+ZvMrZ7r1a5V6q3Ddk0tT4FuyYwrOmevcxGDafEVwv9jHnZxYd/+k7pNQti3V6V3Uc19h7TOJAixl1FEbZGalo7e6xEhRwICxQkDoYFifKxfGuWKKNq8PA3HpbvCKJppz4eDiQbov+izVXrGCs4NYQgIRBUQzx+ncOpOkfSxMFeIBAIBAKBQHDm4AuceoZETto2UPjmLgfx0RL3f+Xhx6X+fB83d0OAvzYYBcRFWwIE1ezz8IWhIsm6vSpZ3qpxfu7y6Tz/k5coGzx6Qe6MjAmDrcgSfDLXKE4GVJ1XZ3i54QNXgeG7Fc2xdI3r33OxenfxuwDS3Tr/bAvSu5US8eTPSbRdIjO0nmvzAZWj6TrXDLDQok7lZDGMOsvo4pixuvAim67rvDbThyLDPaOsNEyQufQcM+v3qpGidk7emu3jwckeVuw8/QSnqsrBUBG+Qc3c464gy6agqvPX+iAt6sh5xl84yLq0gdY5ubSPhd8fczLlbgdNa8t89JeP1KzcdjoB9dTt5masyh7Dy3eIzp2KIixIhMOkARRZIsoGmQXM5ZsPhASJMhhf+TGup5luTRV+XxvkuvfcjHzJdcpWXvM3BUjJ0pm6TAgSpxNCkBAIqiH7jxsTeppLJ6BWjYsGgUAgEAgEAoGgvMnukCi712xeR+Hbu5zUj5f43w9e3puTe+V5llfnuZ+8RNvh0j5m0t2w9j+jqOP26azYqSJLoGrksdHxBysnW+L9OT6OnNC5d7SN2rG5L/sbJsiM6Grin20qf28NMuF9dyhLQ+XuL9xV4vpi9poAy3ao3P6ph/3JxStmzd0QIKjlb9cE2R0Suq6zMtQd0rt1GQ6kElI/XqZHC4U/1gW54JUsvl6Yt1gMMGddkLX/qVza20yT2kYR8bbzrNgt8MYsH2qOFchev86MVYaoFh6jgvLnUKqx3QrqkDh5tfqqXSppLp3hXfKO1UEdTPRppTC8S9mMTUmS6NbMxN0jrXj88Pn8bNH1aLrGuc9k8ezU0meS+IM6v60J0DBkVyUEiYoju0Mit7gQ45Dy7cpJy9KYuyFI+4YysY7y6Qprlqgw5R4nvz3mZPwAC3uPaXx0ip054fl6ybYgHn/lH58EZYMQJASCasi+HCflKZliQhYIBILqhq7rvPiLt1ie5QKBQCDIpiwtm3LSMEHmu3uctGsg887vPp760cuWgyqHUzVe+9XL0XSdB8bYuKyPBci2W1m+M4g/CJf1MQqL/2zPLsYlZ2j0ezKLD/7Mv+uivEh360xZ4qdNfZnLz8m/OH/TEKvx94duVu1SuaqfmUv7mFm2Q+XZqd4KE1GMleK5O04A/t4SxCQbhdzbPnYXGNCakznrApgVGNIp/+8cbZcIauDxG8KRWYEujSunOyLMy1fZubSPmQMpGi9O8zHk2Sx+W5N9bvDX+gAPTfYQY4eJw62R22vFyFw70MKuJI1fc3RY/LE+QEaoC2TdXiFIVBQHU4zr8wYnZUhE24xw6vSTVqv/vs7YZsO75hUdasXIfH67k2aJZTs2z+tsomUdmSl/+0nN0lA1nYcmeziWrvPT8gApmaVbxb5gU5AMD1ze10Lb+jLLd6plYtMjKJr/jmmYFagff1J2SQ57upxMWRLAG4DrB1nz3FfWNEtUePQCK50ay/y0PMDRUnZJaJoe6ZTzBuCfbULwOl0QgoRAUA3Zdzx7Mj+eIQ72AoFAUN1Id8PXi/xMXykECYFAICgJvjIKtc6PhBiZr+900qeVwo9LA1z4qovBz2Tx/T8BujdXuKSXmXYNZBJjpYggsXCz8fe1Ay3Ui5P4Z1t2EfiHpQHS3Tqz/q3Yuf6XFX48fuMzKXL+wk2b+grndjRhVuCFK2w8ebGdJy+20ae1wtRlAb5YUDEiyoxVAe783MOPS7N/I7dPZ9VulbNbKTw4xsruoxr3f+XJ1QlwMimZGsu2q/RpbSpw5W9MyD4nNUtnzR6VLk0VbJaKz47ISb14mWcvs7PkuWheudqGwyJx31cenvvJsA+7+wsPsQ6Jr+90Uismd/nmhsFWYh0S7/zmiwTITl0WwGIyLFzW7VVFjkQFUVCHhCxLRNtyWzYVZtdUnsiyxO3Drbj98MV8P5/M9bNip0qb+jIBlVLb4cxYFUCWYPRZZs5uaSI1S2dn0qlZ9AiKx3/HVJrUkjEpeQWJdHfux3r9Ot/+7adevFRm3TdFIUkSE4dZCajw6bzSHVO2H9ZId8PIkHgXtk4UVH+EICEQVENyChLJGeJgLxAIBNWN5NAqtKxirPgUCAQCQTbl1SERJsom8eEtDl66ysbdI61cO9DCpX3M/N9VdmRZQpIkBnUwse+4xp6jKos2B2lSW6ZJbYVz2hi3H0zR8Ad1vv/HKMDsOapFVlCXN6qmM+VvP3FOiZEFWBeFmXStnUXPRnFRL6Prw6xIvHmdgxZ1ZF791cfyneVf+FkcCnSetjK7WLViZ5CACv3bmrh+kIULzzazeGuQzwopaH37t5+gBhf0LPg7h8Nel+0wQsjPblF5dk0nY7NIjOlh4ZcHnfRoofDt3wH+94OXOnES39zlyDdPINoucfNQC4fTdL5f4md3ksrq3SrDOpvp28ZEulvPdd0oKD8OpmjYzJAQnXdeinFIuSybwnZNwyqoKJyTcJfE5MV+3vndR8u6Mt/c5SQxVuK7Jf4S27WlZmks3hKkT2uFxFiZ3qGA7qpg2/TjUj/Dns9i04HTs1PIF9A5mKLTNDFvWTenPV2Y6SsDpGbpXDfQkkfAKE8GtDPRroHMj8v8HC9F7WrlLmMsXXC20YGzcHPejjpB9UQIEgJBNSSnZZPokBAITp2gqgs/SkGFcjzdGG9ZPjHuBAKBoCT4w6HW5biw2GKSuKCnhduGWXn0AhvPXmbPFVY7uINRSHz/Dx9H03UGtDP+f04oj+Cf7UH+2hDkeIZO5ybGB128pWIKdIu3BDmQonPZOWas5sKLTlazRHxUXs/7d2+wYzfDQ5M9pOWTaVBWBFWdpSGLq437NXYnGYXDv0MiRb+2JiRJ4ulLbTSuJfPhn758bT9cPmPlb6MEiaGdCy7yhjsk5oZW2PZsWbl2TflRO1bmi4kOJg6z0Ke1wpS7nZHciPy4qq+FxFiJD//y8+VCQ7C5tI85Mu7W76ucYmymR2fsy1mc/WgmZz+aSZ/HM/n4r1Pzka/KHEzVqR8vI0n5CBJ2KZdl05ywXVM++RHljSxLTBxuxRsAs2KIklE2iSv6WjiarjOvhKvPZ68JEtRgbA9D1DyruQmTDMt2VK4IMOvfAE/96GXfcY17vnDnCRU/Hdh3XEPTcwdah4lxSGg6uEK7nKrpfLHAT6yDiABdUUiSxG3nWfEFcueXFJeVu1QUGbo2VRjSychwWr379BSZzjSEICEQVEP2HTdWYAAkiwwJgeCUmTTLx4gXsgq1AhAIypLw3J3pqeQPIhAIBNWMcIdEeVg2FZezW5pwWGDWv8aHGdTeKIL3amVCkmDp9iDfLPZjMcFr19gxybCoggSJyYv9KDJcfk7pi05Naiv87xIbx9J1Hp1SfnkSG/apZHqgRwujeD5jVQBd11m8NUiDmhJNaxvlCotJ4tELDKuZ12fmLWr/tMxPuhsmDLIWaFEFRkcBGB0SVjN0aVL1BAkAkyJx10gbn090UqdG4SUbm0XijhFWUrN0pi4L0LS2TPfmCp1D2RjrKylH4rslfrYf1mhYU6JtfRm7xTjf/mFpwQXJtCytWi4Q0jSdw6laHrumMLE5OiSCqs5fG4I0T5RpWbdyxt+wziZuPNfCG9fZI5/hkt5mzAp883fJCsa/rvLjsMKQTsYcGGWT6NhYYdWuYKWtYl+4OcAj33ioHSNx23kWDqboPP6d57SzL9tzzBBnm9XOO+7Cc11YiJm7Ici+ZI0r+lpwWivepu7cjiZa1pX5/h8/SSXIkgjnR3RoqBBlkyLjbO7Gyu/AEZw6QpAQCKoZbp/OsfTs1VbCskkgOHW2HFBJOqHnaqcWCMqTY6G5O8srxpxAIBCUhPK2bCoOVrPEOW2MwojTCt2aGeflNZwSHRvJLNgUZO1/KqO6mWmYIHNWc4UVO4N4y7nYuueoytLtKkM7mYosZBfFuJ4WxnQ3s3BzkEkzfXw6z8edn7m55SN3nqLxyl1BbvrQRWoJuynCdk33jrZSL07i19WBkL2VTv9Qd0SYge3N9G9r4tfVAdb8l12MCqg6Xy7wUzNKYlwhdk2Q3SHhD0LXJkqljqGy5IKeZpqEipKX9DYjSRL14yVqxUiVEmzt9et8vchP3RoSU+5x8uUdTn68z0nDmhLP/OhlwabcWQW6rvP1Ih/9/5fFY1Oq30qN45k6/mDeQOswsQ6JTI+xSn3RliCpWXq+YdYVhSxLPDDGxqAO2ftLzWiZkd3MrN6t8ssKP8/86OGcxzN58vuCt8feYyob92sM7WTGniOLpXcrBZePSrFK2nRA5e4vPETZJT6f6OCukVZGdjXx14YgkxdVTC5ORfHfUWO+bZpP+Hk4RyccbP39P37MClzdr2K7I8LIssQ9o6x4/HDde+5iB1zvOGLkR/QMidat6so0rCkxb0PgtBOYzkSEICEQVDP2h+yaujZVkCVh2SQQlAUpodXqaVlifxJUDMmhuVsIEgKBQFAywqHWlkrskAAYFLJtOqeNKVdhu09rU0Q0ubq/Ufzp39aENwCrytFmQtf1SMZC+H1Plf9dYqNxgswn8/y89quPvzYEWbQlyF/rcxeUP/jDx99bVV6eXjJLnr+3Bol1QOfGCmN6mEk6ofPqr8Zr9Gubt2D7yAVWTDK88LMXLdTVOvvfAEdO6IwfaCkyoDo6R9h1z5ZVJz/iVDEpEk9fYgSSX3i2se0lSaJzY4XthzXcFWwPOX1lgORMnesHWSL7Rs1omU9udRDjMEK7p/ztZ93eIIdTNe74zMOLv/gIqMZK7sxKXCB0PENj9e6Srb4uKNA6TExo3K3YqfLQZA9OK1zQs3IKw4URLlY/NsXLd/8EcPl0floeYM/R/Oet39cav9Pos3JPxr1aGfvW8kqwbZq8yI8vAB/dbKd5HQVJknj2cjuNaxm5OF8v8p02HfF7woJEIR0S6R4dl09n9W6V3q1MJMRUXgn43I5mnrzYxt5jGuPfcRerU2LlzrC9njGmJEliSCczR07obD4oFuZWd4QgIRBUM8LBZM1qy9SMloQgIRCUAceFICGoYMKhbh4/IphNIBAISkBV6JAAGNrJTL+2CuMH5C4shnMkujZVaN/QWNXZP5QxUV62Tf6gzhPfefl5RYCuTRXOalY2VjBRNomPbrHz2AVWJt/pYP5TUZgV+GVFtiBxKFVj+U6j8DhjVaDYQdgpmRqbD2ic09qEIkuM6W4UNRduDmJWDFusk2mWqDB+oIXNBzRGveTi/q/cvDvHh9MKVxTDoircIQFwdouqaddUWnq1MvH5RCc1nNnfsXMTBVWDzRW4Uj2o6nw230esQ+Li3rm3SZPaCh/ebAfg2Z+8XP6Gm8HPZDFvY5BR3Uw8NNZKQCVPB0VF8vzPXq5+283kxcVfTX8oxTiPq1+zAMum0Li7/VM3QQ0+uNmRK5OmqtCxscLtwy3ceK6F6Q85+fx2B7oOn8zN+1vous7sNQFqOCV6tcq9L3VpomAzG1k6FUlA1VmwKUC7BjKdm2TPH1E2iXcm2KkdK/HiLz6ufNPN9sPVP4Pgv2MqtWMlomz55ZYYf2d6dFbuDBJQ4Zw2lT/nXdXPwtOX2NiXrHHN2y5+XOovtFsiZ35EmPNCOUEv/eIt967D4rJ6dzCSgSQoPlVvFhQIBIUSFiQa15JJiJZIzhTKsEBwKgRUnROukCDhqhonNYLTn+QcYnKWtxI/iOCMJlAGYlhAhad+8OSyUBEIyhNfBYRaF4dou8Qntzrp3jx34bxbM4WbzrXw+EW2yG0t6sjUi5P4uxwEiZRMjevfc/PzigD92ip8fIsj32Dd0tKktsL4gVZ6tDBRL15mcEcTy3eqkVXhRu4DPHahFasZnvnRiz9Y9Nzyz7ZQcHVIrGmWqEQsaXs0V3AU4HM+cZiVC3qaCao6s9cEOZiic2VfS2QVemFEhwp3NrNRfD3dKSzYWtd1/jumsv2Q8edgSvGvKV0+nUe/9bDlYN7X/WO9Eap+TX9LvtuwSxMTfz0Zxfs32bl3tJVxPc28fLWN18bbGdvDjCzBnHWVczzxB3WWhGzEXvjZy7fFzFMoboeEPwhvXGenZ4uq251z5wgbD4yx0aa+QremJnq0UJi5OhD5jmF2HNHYlaQxvIsJs5J7O1tMEv3amli1S61QcWnVLpUMjyEWn0yregozH4niuoEWNu5XuehVFyuKKZ5WRXRdZ88xLd/8CMgWX9PdOv9sN/bTvm2qxri7vK+FZy+zkXRC538/eBnwVBYXvJLFm7O9rNsbjHSwaJrOqhz5EWG6NjVx3UAL/+5ReXCyB1XT0XWd7//x0/eJTKavLD9rrvxsopIzNO75wsN177kj5yeC4lE1RqRAICg2YUGiUS2ZhBiJPcc0dF0v0wsPgeBMIjVHV4QQJAQVxfFcgoSea0WjQFARrP0vyFVvufn5ASdtG5S+MLftMPywNMCcdUF+ut9JwwSx3klQvgQiodZVc95UZIn7x9hy3SZJEv3bmfj+nwB7j6k0qV02xfAsr874d9zsPqpx3UALD44tPNS5LLigp5k/1gWZvjLAxGEWpq3wUytG4sq+Ftw+eHO2j8/n+7n1PGuu56VlafywNMDYHmbqxsn8vdUokoU7SgDG9TCzfq8a6SjJjyibxEtXGct/s7w6+45rtKpXvHknXBju1vT0yY8ojA4NDYvf/IKtp60M8NiU3CsiHh5n5fpB1jyPPZmZqwJMWxlgy0GVnx9wYgoVpHVd55O5PuwWuKp/wZ5qCTEygzvIDO6Q+/aa0RI9Wygs2RYky6vnu/K7PFm3V8Xlgyv7mlm2Q+W5n7xIwJVF+O6HxZyCMiSa15ExyfD8FTYGd6hkr7kScstQKzd+4Obz+T6evNgeuX32v4bQMLJb/t/nsQttLN+ZxZPfe/n1EYX4qPI/N5i7wfhM4eDjk3FYJR65wMbIbmbGv+Piye89/PpwVMTqLaga1kbdmlXe/JDh1nlwsge7BW4cYqVDqMvuv2MqM1YFaF1PYURXM8fSddw+aJZYuCCR6dFZsi1InRpSgY+tDC7tY2FYFzNLtgVZtDnI4q1BPvzTz4d/+omxG/O8qpErPyInD421cjxDY/aaII9/5yUpLbtT77t/AowrB0s0Xde55h03AO/e4KCGU0LTdB751kNyps7r19qr7HlJVUUIEgJBNWNfskasA+KcMrViZHwBlSwvRNuLfq5AIMhLOD8ChGWToOLI2d0mciQElcGWgxqaDjuPqKckSBxMzV6FN/FTN9/f68RZwMpmgaAsyLZsqtzPUVLCgsTCzUGuKwNBQtd1HpviYfdRjfvPt3LTkKILyWVB3zYmasVITFvpp0cLhQMpOjcMtmBSJCYMtjBzdYAP/vQx+ixzLluaSbN8TF0W4OO5Pu4ZZWXJtiBt68vUjs1+zMW9zZiVvJ70BRFlkyK2WMUh1iFx//lWujc//bsjwCjAtq4ns26vmmsBm8ev89ZsH3FOiWtClmMzVwd4dYaPNvUUercufOeattIo/G4/rDF1WYAr+hqv8cUCP9sOaVw70EKcs3TFz2FdzCzfqbJgU5Dzu1ds8T7cwXTB2RZuPU9i/Ltunv3JiyQR+Y75cTBFw2GFGgV06QzuYGb1y6YiM06qIue0VmjfUOan5QFuO89KQoyMruv8tjZA7ViJ7gXYw9WNk/nfxXYenOzhqR+8vD3BXq4LKDVNZ+7GII1rybSoU/jY69RY4a6RVl6Z4eO9P3zcf76NoKrz0GQPv60N0q2pwrs32itERMlJpkfnxg9dbNhnXCPMWRekTyuFgGZ0fwCYFWiUIJPhNq4d8gu0hmzxdeshjb3HNC7uZa5yC1hjHRKjupkZ1c2Mqums36eyaHOQ1bvVSJddwwSZMT3yzgOybAjTaVlupofmo0v7GELNws1BjqZrJMaW7fbbsE9ldSgH6vr3XHw20cH0lQGWbFO56GzjewhKRtWRyAQCQbHYd1yjcS1j102INg4qYS9ygUBQcpJz7D+iQ0JQEfgCOunu7P8LQUJQGaRmGXPfqVqGhQWJMd3N7Dyi8cg3nkjYrEBQHoRDra3V7Nq/V0sTMXb46K+8ntnLdxjhviXhs/l+/lwfZGRXEzeeW3EBuSbFyHs4mKLz3FRjArngbGNjWEwST1xkwxeAt2ZnB1wfTtWYvjJAyzoyNZyGj3uaS6fvScHVZsXIHSjPwu1NQ6x0bVrN1KxToHMTheMZOvuTs+flbxb7OZquc9swCxOHWZk4zMr7N9lxWOHerzx57HlysjtJZf0+lfM6m0iMlXhzto8TLp3Vu4O8PtNHs0SZu0aWXhwb2smELMGf6ys+R2Lx1iDxURLtGxhC2Vd3OGhcS+aZqV6+/6dgG5hDqRoN4uVCC77VUYwAo7vrlqFWfAF4ebqxrTfsUzmYojOiqxm5kI6s0WeZGN7FxF8bgsxYVbrt+e3ffoY9l8XUZf6Ilc+hVI0HvnYz7Lks9h4zCsQb96scS9cZ2slUrML7+AEW2jeU+Xy+n80HVB751hAjmiXKrPlP5ZLXXeyowJyJLK/OjR+62bBP45ahFmY85GRUN8Meb91/KiO7mXjhCqPz7oGvs+3SirJsmrfR+N3PqSJ2TQWhyBLdmpq4d7SNb+92MvX+KKbeH8U3dzlpWTd/0cViknj7BgfXD7Lw+UQHz15mj4iYCzaWvR3XjNXGbzm2h5mthzSueNPNG7N8NK0t57JoFBQfIUgIBNUIl0/neIZO4wRjUk6ICQsS4sJfICgtyZnCsklQsYTHXKwju51aIKhowh1hmacoiB1MNf5++lIbI7sZhYdvl1ReIKng9KeqhFqXFIdV4vkr7KS5dO7/2kNQNXyvP/zTx3Xvubn5IzfBHLku/qDO5W+4uPEDV0RADLNse5BJM320rCPz3BXlu/I4Py4MCRA7kzQ6NZZpUSe7YNS7tYl+bRVm/huIFM0+m+8joMJD42zMeiSK6wdZqBUjFbsTQlB6BrY3CpF3fuYmLUsjzaXx8VwfDWpKXJ4jCLxpbYVXrrZzwqVz52fuAsNiw6uRr+xn4YExNtLdOs//7OHeLz1YTfD2BPspdcklxMh0b66weGsQl6/izo+OpmtsP6zRt40pUmRPjJX5+g4HjRNknv7Ry1uzvUya6eWmD11c846L4xkaqqZzJE2vkiHVZcWQjiY6NzH26cFPZ/Lk94YQObqIFeGSJPHUpTZqxUg8M9XL6t0lKxIHVGN+3Jes8eT3Xi54xcULP3sZ8UIWs/4Nsi9Z447PPGR5df7aYLz20ALsmk7GpEg8d7lhMXH12y5m/Rvk3I4mZjzs5LnLbRw9oXPFmy62Hyp/USKo6tz6sZv1e1VuOtfCPaOstK6v8Pq1DhY/G8WS56OZdK2Di3oZ9/13TOPdOYbg27QgQSJyjQGSBL1bVW1BorRE2SQeHmejT6ira0A7E2YF5m4s2/NQf1DntzWGYPV/V9l4aKw1YqU+6Vp7gZlHgsI5fWdNgaAQfAGdH5f6c530Vwf2R/IjjAmvVoyxC+csqFZ3NE3ntzWBYoXhCQRlgbBsElQ04a628EWECLUWVAZhAfZUO3QOpkrUjpVwWCVeuMJOnFPim8X+fIP/BIKyIHyOWN0smwDO62zmmv4WVu9Wees3H6/P9PHmbB8OC+xK0iLFXoCvF/lZt1dlyTaVi19zsfmAygmXzku/eLn5IzcOK7x9w6kVf0tL8zoKnUOh0Bfk49V9//k2JAkmzfRyPMOw9WnfUKZvGyOs+uFxNv5+LprW9c4M66TKZGB7Mw+MsbLjiMaE991Mmukj0wP3jLLlEfUGdzRz+3ALWw5qvD7Tl+e1VE1nxuoA9eMlejZXGH2Wia5NFWb9G+R4hs4zl9lziVOlZVgXM74ALNxccaHD4TDrfid17STWkPnqTgeNEiQ++NPPx3P9rNipsmqXyh2fetifrBHUCg60Ph2QZYlv7nLw6jV2GtWS2XFEo3GCTIdGRX/nOKfMuzfYkSS4+SM36/YWf5vO32iMq5uGWLhlqIW9xzUmL/bTtLbM5xMdPDDGyq4kjcemePhrQ5DasRIdGxV//LVroHD9IAsevyHcvXGdHbMicUlvC+/daMflg5+Wl/8Ci8mL/azerXJlXzP3nW/NJTAnxMiRBUwA1w2y0LOFgscPdgvUqZH//G8zG/ZOAB0byWdMVl2UTaJXK4UVO9UCF3xt3K+SllWyjsQl24KccOmM6W5YX00YbOW9G+18dIvjlGxPz3RO31lTICiE39cG+N8PXhZtqbiTnLJgX7IxcTapZUx6tUIdEsmnkWXTkm1B7vvKw5y1YnWloGJIDnUYKTKkuYq3L/26ys+QZzMj/p0CQUkIj7lsQUKMI0HFkxoSYLNOoUNH13UOphp+xgB2i8TYHmb2HdciPrthVGHjJCgjfKFTRHM1rQE8ONZKx0Yyn8z18+k8P12aKMx6NIo4p8Tbv/vw+HWOpmu8/4ePRgkST19q43iGzlVvuRj2fCZfLfLTpr7MFxOdNC2jcOzScNswC71aKvl2ObSpr3D+WWaWbFO590sP/iDcdp61ynmYnynceK6Ve0ZZ2XrIEIfaNZAZ2TV/RW/iMCtnNVOYvNjPip25r5WXbjdsccb1NKx6JEni8Yts2C2GBU5ZZT4Ytjvwx7qKux5cvDWIJEHfNnn3qTo1ZH6418lb19uZ8bCTf1+J5qYhFtbvU7n7cw9QcKD16YJZkTi/u5lpDzr55i4HH91S/M6szk1MfHyLA12Hmz50s+lA8boOfvjHj0k2xta9o2388UQUX0x08MuDTvq0NnHDYAvDu5j4c32Qfcc1hnQ0FWohlR/3jLLyya0O3p5gzyXQ9W9nol68xILNgXJdYHEoVePt33w0rCnx0Fhbkb+pIku8fLWdGDu0rqcU+H0lSYrYNvWt4nZNZc2QTmYCKvnW+pbvDHLJ6y6e/7lkq8F+DVmO5Zzjzu1ojnRmCEqHECQEZyRJJ4yDypG06nVxHG4LOzlDIvk0smw6lGp8l/A2EgjKm3C4cONacrE7JBZvDXIwRS/2CbVAkJPjQpAQVAEigsQpdOiccOtkeiWa1ModSgu5VxUmndAY8kwWr/8q2oEEp44/CBZFr7bFbYtJ4o3rHCRES5zTRuGziQ7qxctMHG7lWLrO14v8TJrpw+2DRy6wcfk5Fr65y0GsQ8JplXj1Gjs/3OukY+PKVWQGtjfz5R1Oou35b4e7RloxK7B6t0rLOjKDO4jCTWVy63lW7hxhxW4xrLMKKmQqssSLV9qxW+CxKZ5c5yjTVhg5CuN6ZHfFdGiosOT5aB67sOw81GvHynRtorBka7BA66iyJKjqLN0epGMjmbgCgozjomSGdTHTup6CWZG4d5SVczua2HHEuI44nTskciJJEt2bm2hSQjG0e3MTH9zkwB80AoEXbylcbNp7TGXpDpUhnUwRV4i6cTK9W5tQQmNXkiReuNJOy1CI9ZBOJRfETIpEv7amPN1CkiQxsL2Jgyk6u5LKZ/Gnrus8O9WLxw9PX2ovds5I3TiZXx6M4q3r7YU+LtpxZgoSgzsYgua8k2ybMj06j35jCIgLNweL7ciR4daZvylIjxbKGbOfVxTi1xSckYQtjqpbGHRYkAivREwIHZyPnUaCRHibpFaAdc7xDI2Hv/EwfWXBIWWC05+UTJ1oO9SJlYqdIRHeF3ceEYKEoOScbNkkMiQElcEJV8kzJFw+PddKwf0nnZcAtKij0KWJwh/rApGx/cLPXo6c0Pl0vp+1/1Wv7lRB1cMf1KulXVNOGtSUmf90FJ/e6ohYLl3Wx0yjBIkP//QxY1WA/m1NDAr5/3duYuKv/0Xx55NRnN+98CDZqkKDmjJX9jMK17ecZ60Wn/l05/bhVla8FE2vloXvQI1rydx/vo1DqTqvzvCy95jK9//4mbvRKMo1TMhdRioP27BzO5lw+2HZjtzHjAPJGkknyvYafv1elUxPXrumwpBliVeusdO6nvFbNK4lSmtF0auViY9udiBLcMvHHj6Z6yuw++DHZUYxOWfOSX44rRKf3Obg6Utt9G5VtiLtoPaGwFEW1mEev07SCY3th1T+O2ZYBv2+NsiiLUHOP8tc4tDpBjVlEmsUPuYSoiVi7FS6eF3R1IqR6dxYYdGW3KLD86Fz0XYNZFw+WLGzeNfxf6wP4A/CmDLqABNkI2ZNwRlJSmhFdHXrLNifrBHrkCIegFE2CYcle4X36cCxdGOblHe48LLtQS54xcWMVQEe/87L8h2iQHKmkpypkxAtUyNKIstLkasldF3PIUicPvueoOKIdEgkhjokTjG0cd9xLTImBYLioOt6pCOsuB06e4+pnP1IJrPXZB8v9ycbz210UiHm4l5mvAGY/W+ABZsC/LXBKGKZZPjfD16REyU4JfxBMFdzQQKMTomcXR4Wk8Q9o2x4/IYd1SMXWPPcb1KqV1H//vOtfDHRwahup8EGO00obhj8lX3NnN1S4YelAYa/4OLpH72oGtwwuPACcVkxpKNR/Ju3MfuY4/HrXDrJxZj/yyrTsOG5offoXwJBAoxi+OcTHbxxnZ2Wdc+som9p6dXKxNT7o2hRR+b1mT4enOzJk+vpD+r8siJA41oyZ7cs+netU0Pm8nMsZd41d3ZLBYcFFmwquk6w9aDKyBezmL0m96r8DLfOkGcz6fpgJgOfymLsKy5GvOCi9+NZ3PeVh1iHMdeXB09dYuPT25yYq9lxoywY2smE2wffLfGz95jK7DUBZqwKMKCdiVeuMTpLFmwq2hJO03R+Xh7AYoJhnYUgUdYIQUJwRhIuBh2vooLEv7uDHErNW1z676iWyxYBICFGqnbCSmGEt0lKOYosH/7pY8IHbjx+nYfGWrGZ4Z4vPRxMEQW9M5HkTJ2a0RJxIaHvRBFi2Am3TobR7SkECUGpSM7UMSvZ7f1ZntK/lj+oc807Lu741F1Gn05wJpDhgWBo+iquILFxvxHcOT/HBVzESvKk1bIjuplxWOH7pX6e+9mL0wqvjbdzy1ArO49ofD5fdCYKSo8/qGM5TWt/w7uYuOhsMw+Ns9Essfp/SYtJondrU7W11zqTkWWJl66006+twpX9zLx1vZ0lz0UxsH3FFOUa15JpWVdm/qZgJINoxqoAaS7jPPyGD9zsPXbqosTvawN8udBP41oyHUoQiBymZrTMiK6iUFkSGiXIfH+vk3M7mpj1b5DHv/Oi5ciZmrk6wAmXzmV9zJU6d1hMEue0MbFub+EhyAeSNW760M2eoxqTZnpzCSxTl/k5mKLTv62J8QMs3DHcys1DLFzWx8yIriZeudpOzejyKcu2rKvQ6QzrjggzpJMZSYKXpvkY/oKL+7/yUMMp8fwVNponyjROMOaWwvJBdF3nuZ+8rNurMraHmRiHOI6VNWKpguCMJKWKWjZ5/Dov/Ozlp+UBw1P2NmfkvtQsjZQsnYHtTxIkomX2nkYrY0tj2bTnqEqDmnKxVvwcTNF4c7aPVnVl3rnBQeNaMk1qyUz81MOdn7mZco8TezH9GwXVn4Cqc8KlkxAtER/yjE1z6dSOLfg5OVei70xS0TRd2BAISsTxdI2EaAmb2VgFeyoZEr+tCXAsXed4ho634rIfBdWcnBfWxR1/B0Ki/b+7VXTd8O8Pz4f52XeM7GqO5Eg8fpGNxFiZm4da+G1tgPf/8DG8S8k9qAUCMEKtq7tlU0HIsuGJLhBUBerFy3xyq7PoB5YTQzqa+OBPP+v+U+nWTOGbxX6i7fD4hTYem+JlwgduptztpE4h1jXLtgfZekhlWBdzHv/3RZsDPPi1h1oxEp/e6ohkEwjKH6dV4s3r7dz1uYcZqwI4rfDwOBvv/u7js/l+nFa4oGflCz0D25v4a0OQxVuDjO2RtzsoOUPjhg9cpGTp9G6lsGyHytyNQYZ3MRNQdSYv9lMrRuLdG+3F7k4SnDqNa8l8f4+DrQc1DqRoHDmhcUkvSySPZHBHE18s8LP5oEaHhvmfi06a5eO7fwL0bqXwxEVll5EjyEZ0SAjOSMIWR1WpQ2J3ksqlk1z8tDyAJMGm/VouxTYcmNWqXu7dtlbI9z6gVp3vciqEt0lxw4X3HFUZ/ZKLb/8u3mrLRVuMlss7R1gjXp+DO5q5Y7iVrYc0Xp0hAjfPJFJD4mTODomi7MLCBbg6NSTcPjicdnrse4KKIzlTJyHGsOpw2qQSefjnRNeN8FPj37D3eFl+SsHpTGqOeS6zmB064bnvaLoemff2J2vEO3WibHkvssPh1h0aylzZ1/i3xSTx7GU2/EH46C/RJSEoHX719LBsEggEhXNuKKR47sYgy3ao7ErSuOhsC+N6WnjuchuHU3VueN9d4Or15AyNOz9388oMH+c+k8XVb7t493cfH/7p4+3fvNz1hYcou2G7dLKwLih/zIrEm9fZObulwpQlAQY9ncUn8/y0bSDz3T3OAgPGK5IB7YyA5Pxsm3wBnZs/crM/Weepi228Nt6OxQRfLDDOb+asDZJ0Qufq/hYhRlQCnZuYuLyvhQfH2ph0rYPerbNPHAZ3MP49f2P+q7k++svHJ3P9dG6i8O6NDqxmsf3Kg8rfwwWCCsYX0CMX36lZeqQFtDIJqjrXvmu0+d032solvc2ku3WOpmd/trA1zMn+lAnRErpeMSHQ5U1Q1SPdK6kuvdAWujArd6poOuw4XLwukcVbgpgVch2QACYOs9C5scJPywPlahclqFqEA+5rxcjERYUEiSL2pXBRbnBHYwztShLB1oLio2k6yRk6CaEVOtE2cJVSkFi1W2XLQY2GNY2xu+eYOFkWFI+c85zLW7zj7YHk7GPjmj3GvLcvWaNBfP6P79LExKRr7bw9Ifeq0+7NTTSpLbN6t8huEpQOf+D0tWwSCATZtG8gU6eGxLyNAb5e5EeS4Or+xir1i3pZeGScld1HNW780J1vt9/rM31keY3rvBFdTWzcr/LuHB9vzvbx/h9+zAp8cquDFnXEhFJZWM0S793ooHNjhUyPzr2jrfxwr5NW9arGNkmIkenUSOHvrcE8+Vd/rAuw5aDGTedauLyvhZrRMmN7mFm/V2XNf0G+WODDboHL+lRM7oqg+HRtqlDDKTH/JKFJ1QzHkjdmGY4aH9/iwGkV11flRYUKEosWLeK8886jdu3aREdH06dPH3788ccSvUZGRgZPPPEEbdq0wW6306BBA2677TaOHTtW4HOmTZtG3759iYuLo0aNGgwdOpR58+YV+Pg9e/Ywfvx4GjdujN1up23btrz00ksEAsIL4XQgXIAE0PRs+6bKZFeSRnKmzvWDLNw81Eq7+sYBeFuOsK6dR4x/t6p7UodEqKhVlbo9SktKlk5YH/IFwF2MxZNr9xq/y+G0okUEr19n+c4g3ZopeVZzyrLE9YMt+IPw3RKxr58phMWnXB0SRQgSYYu0czsYq7ZEjoSgJKS7dYIa1IoxxluUTSLTU7r5+6uFxiT5eKiNeLcQJATFJDzPxTklgppxzC2K/ckaibHGGPt3T5B0t2F516BmweN3ZDcz9eLzXm50a6pwIEWvctaZguqBP3j6WjYJBIJsJEni3I4m9ifrLNwcZHAHEw1qZh9TrhtkZeIwC5sPaNz2iRuvP/t4tG5vkGkrA/RprXDnCCtvXOfgn+ejmfagM/Jn7v+i6ViK3AhB2RJlk/jmbgeLn43ilqFWTFUshHlQBxMuH6zenXsR2rSVAcwKTDg3W3C4dqDx78eneNlyUOOCnmZqOKvW9xGASZEY2N7EtkNaJEfU5dO5/VMPkxf76d5c4es7ncSK3IhypcIEiW+//ZZBgwaxcOFCunXrRv/+/VmzZg2XXXYZTz31VLFeIzMzk0GDBvHCCy8QDAYZPXo0UVFRfPjhh3Tr1o2DBw/mec6LL77IhRdeyLp16+jXrx/du3dnwYIFDB06lM8//zzP4zdv3kz37t2ZPHkydevWZeTIkaSkpPDYY48xYsQIgkGxmqu6kxy6+I0K2cBVhUL+hn3Gwa1rU+OEqHV9Y9fcdij7Qn3HYY04p0TN6NyTYriolXwaXNSHt0V4IWVqMcSidWFBIp8Q8JNZtVvFF4D+7fK/ih3S0UT9eIkpS/z4ApU/LgTlT3jMldSyqXasFAkJE4KEoCQcC425hNBcHmWXyCyFU9z+ZI35m4IMbG+iX1sTVjPsKXhthkCQi3BXZaOQRUVRtmEun05ypk6PFgp1a0j8u1tlf6hjokFcyY+X4fOdcKeFQFAS/EEds0mcpwkEZwJDOmbnCFwzIO9K8ztHWLm6v4VVu1QmvO9mxc4gmqbz/E9eTDI8cZEtEowcZZNo20CJ/BGF4qqDWZGqhEVTfpzX2agdfLM4e7XkoVSN5TtVBnUwEefM/twt6ij0b2viv2MakgTj8xmzgqpB2Lbp8e883PShixEvZLFwc5CxPcx8PtEh5ocKoEL2+KNHj3LTTTfhdDpZvnw5c+bMYfbs2axbt47ExESee+451qxZU+TrPPXUU6xZs4bx48ezbds2pk6dypYtW7j33ns5dOgQt99+e67Hr1+/nscff5x69eqxadMmfv31V+bOncvChQtxOBzccccdHD58ONdzxo8fT1paGh9//DHLly/n559/ZteuXfTv35958+bxzjvvlOlvI6h4wh0RbUJdCFVhdd7G/cYFeXiFRtiWacdh43Zd19l5RKVlXTlyQhUmXNRKrgLCyqlyPN3YFk1qG1NTagF+oGHSsrSIfc6RtKLtt8L5EQMKECRMisQ1/S2kZunM+ld0SZwJhOeDWtEyNaKKFiR0XWffcY3GtWSi7RJ1a0iR7iWAlbuC3PGVzJFidOyA0Raa8/mC05/wXF071N0WZZOKbZmTk8mL/Og6XDfQgiJLNE+UhWWToNiEBYmGCcaYKSrYOmzX1ChBplszhZ1JGhtDiykKsmwqjG4hQWLtf2L+E5Qcn+iQEAjOGLq3UIiPkmhbX+bsFnm7GSRJ4rELDFFi3V6Va991M+IFF5sOaFwzwEKzRNEBITg1miUqDO1kYv6mIFsOGuctM1YF0PX8g7evH2SIEIPam2hSW4y/qkrfNiZiHbBip8rKXSpRNon7z7fyf1fZROZHBVEhgsR7772Hx+PhjjvuoFu3bpHb27Rpw0svvYSu67z55puFvkZGRgYff/wxDoeDN998E5PJOAuVZZlXX32VZs2a8euvv7J79+7Ic15//XUA/ve//9GkSZPI7X379uX+++/H4/Hw4YcfRm6fP38+a9asoXfv3tx0002R22NiYvj888+RJIk333yzxEUDQdXieKgA2TYkSJxcyA9WQjj0hn0qdWpI1I7NLlA1SpDYdji72O7yka+XYsJpZNkU/g6t64UFicK/U7g7wmGFoFb4b6DrOos2B6gXbxTuCuLi3hacVvhygV/s62cAKfmFWhcihKW5dLK80CQUiN6yrsLuo1pk3nhvjo81e2Ue/saTSyCbvSbA0Gcz+Xy+LxJAfzBFY/w7bs7/PxczVolw1zOF5JBNWEKouy3aJhFQi2eZE8bl0/llhZ9WdWXObpktZB/PlEh3i3lLUDRh4TXcIZFVRLB1uBuicS2Zs5oZ5+DTQ/NWg/iSj7mmtWViHUKQEJQcXdcNyyZR4xEIzgjMisQP9zr5+FZHnoV5YWRZ4omLbPz+eBSX9jFzOM3oZr59uLWCP63gdCU8lt6f40PXdaat8JMQLdGvbV51vFcrhTevs/PUpbaK/piCEuCwSsx5PIrFz0ax9pVofnssipuGWAucZwRlT4UIErNnzwZg3Lhxee4bN24ckiQxa9asQl9j0aJFuFwu+vfvT1xcXK77FEXh/PPPz/VeAL/99luB73vBBRcA5Hrf8HPHjh2b5/HNmzenU6dO7N+/n40bNxb6WQVVm3ABsm2DvIX8LQdVOj+QyapdFWfN5fbp7Dyi5fGvbF1PYe8xDa9fZ0doBXXLunl32XBR6/hpEMScLUgYv0VRgkS4kBFu5S3MtmnvcY0DKToD2poKPchE2SQu7m1hZ5LG0u2iUHK6czxHhoTFJBFlK7xDIpwf0TgiSMj4g0axbs9RlRU7VWxmnZW7VL5YYBTrlmwL8vBkDwdSdF6Z4WPcyy4+/NPH2Jez+HePilmBz+YJAexMITzPZWdIGLcXtUI9JzNXB3D54Mp+lsh8Fs4XEh03guKQlqVjNWfnUBU1/sKCRMMEmbOaGcfoDfuM2+qXokNCliW6NDGx5aCay/NbICiKoAa6DmbRISEQnDE0TJAjx6vCaFxL5tnL7Cx6NorpDznzZAYKBKWlTX2FIR1NzN0Y5Nu/AxxI0RnTw5xv3oUkSQzvaiYxtmpaUAmyiYuSqR0rI8tirqgMyn0P0XWdLVu2ANChQ4c898fFxVGnTh3S0tI4dOhQga+zefPmAl8DoF27dgARsSApKYmUlBQSEhJITEzM8/i2bdsiSRJbtmxBVdVSvYegehLOWmibj2XTql1BVK1iV+xtPqCi6UT86MO0qa+g6YY/fdijPj9BomaUhCSdHpZNx0KWTeEOiZMLwyt2BvEHc4aVqTgsMLC9cVV6KDX7Pn9Q58mfZN7+zYvHr7M4ZNfUrwC7ppxc09+CLMHXi8Sq9dOdlEydGDtYzcZJSJxT4kQhgkTYIqxxaFVxi0gRWOPHpcYS9xcu0WiWKPPWbB8/LfNz1+duHFb46QEnd46wcjBV483ZPhxWiU9vc3BlXws7jmgs2yEKyWcCYUEiIYdlExTt4R9G13W+W+LHaYXzu2e3iYc76ESmiaA4pLk04p1SscdfpEMiQaZFXZlou3F7nFMiupQLALs1UwiosOmAmPsExSfcTSYsmwQCQUHER8nEV9E8AkH1ZWKoS+LFX4zwt/zsmgQCQfEp91k6LS0Nr9dLdHQ0Tqcz38fUrVsXMLImCiKc9RB+bFGvUdTjrVYrcXFx+Hw+0tPTS/UegurJ8QwdSYLmdWRkKXeHxO6jxgX3oWIEJJcVG8L5EXkEiVCw9WE1UmBqVTdvf7pJkYh3SqeFIHE8Q8ckQ/M6eTskNuwzPEFf+9UHGNZaG/erdGik0LCm8VsdzuHbv/2QxrzNMu//4Wf0S1n8uDSAWYFeLYu+gm1QU6ZfWxNLtgVPi7BwQcGkZOrUjM4+FMZFSaQV0pmzL0+HhDFWNx1QmbYyQLNEmV4tdF4bb1Trnvjei6rBhzc76NBQ4fbhVmY/GsXD46z8+rCTvm1MXDPAEMC+XCAEsDOBsAie3SER9vAv3vPX7lXZflhjbA8zTmv2ap6WokNCUAJSs3TioqSIsFBkh8RxDYcV4qMkFFmiaxPjWBqeC0tDV5EjISgFgdDCFGHZJBAIBIKKpF0DhXM7mtB06NhIjlwHCgSC0lHugoTL5QLA4XAU+Bi73bgaysrKKvXrnPwapXnfkr7Hyfh8PjIyMnL98fl8Bb7/mYrLp5NSifZCKZk68U7DnqVmtJRLkNhTCYLExn0qkgQdGua1bALYdkhlxxGVenFSgW2ntWIljp0GhfPjGRo1o43tAtn2WpBdZPtuiZ+DKRo7jmh4/EZBo1688fic2y1sczWiq4k0l87uoxo9Wyo4rMVrxxvTw4yqwW9rRLj16czxDD0y3gDinDKproIDhsOCRMNQh0TzRBlJMsZlulvnsj5mJMk4YX1onA2HFV4fb6dbs2whrEFNmesHWYkLrdxqUFNmaGcTi7cG2ZUkCnOnO8kZOrEOImFp0faQIOEpnqj8/RJjTrr8HEuu2xNjJaKsOjtEh4SgGKSFBImIIFbE+NufotE4QY5YhHUL2TadiiDRsZGCSYY15SxI+IM6vkD1X7QhMPCFXFVFh4RAIBAIKpo7R1ixmQ1HBYFAcGqU+6mcohgXLMUJBtG0gi+ii/s64dcozfuW9D1O5qWXXuKZZ57JddvDDz/MQw89VORnON1JS0uL/PuxH2XW7ZP45R4VWyV0uR09oRDnhNTUVOIcCkdPaKSmpgKw64gCSBxIDkZuK2/W/afQJAH87jRS3dm32wGnVWHDXj+7k6B7M73AzxRnl9mTJJGSkkp1zuA5ekKhZhR4s9KwmBSOpflITTWSNncelAGZgAqvTs+gQwMdUGiR4AG/G6tJYd/R7Mdv+M94/PV9vdw6CH5cLjOoXZDU1OKJhN3qG7//z8u9jO7kKp8vnA8/LJdYuFXm3WtVFNFpXK4EVUh3m4i1BSL7lsMs4wvIHD6ahj2f88zdRxRqx4AnK41wBmz9OIWDqRIWRWdAS1dkvhvVAYa1BZOSSVHTyYXd4I91Jj7+I5NHzhcF5dOZpDSF+NAxCICgBCgkJWeSWqvwoukJN/y+VqFzI0iwpecZV41r6mw/RLU/FgjKF18A3H4TTnMA1ecDTBxPc5Oamv+xzh+EI6kKrdpmn4e0TQQwUTfam+scr6S0rKOwZnegRGN291GjGN2wZvEe/+B3ModSJb6ZqCIsgqs/x1IBTKhBb4WdqwsEYU5lvhMITgUx9qoGte3w58NgUrKKvL47HRDjTlAa4uOLFzBX7oJEVFQUAB6Pp8DHhO8LP7Y0r3Pya5TmfUv6Hifz6KOPct999+W6zWq1YrVaC/wMZxLx8fH4gzordmfi8cPKfVGM65ld8fv2bz+LtwR5/yY7SjleMaa5M2hcWyE+Ppa68W727ggSFxfHCZfOCbfR/ZKULhEXF1csQetUSM7QSErP4sKzzcTH2/Pc36a+izX/qeg6dGhkJT4+f6PmBrU8LNsVQLbWiKy6rm5omk5qViYdGpmoWTOWmlGZZPok4uON/e24yw0E6d5c4c+NcChNBjT6doglLkqmfs0sjmcRefyBNBc2c5AOzeKQZYknG5f8Mw3v4uHnFQFSfLEV0pKpajpTlmVxPEPHJ9WgQXz13JbVhaMnNCCLujVtkX0rMc4L+MFSg/iTfn9d1zmUlkmHRsb8EaZNfTcHU4OM6GahSX07qanFPwiHGRAPnZu4mLMBHr4wJpeNlOD0Is2dQfsG2WOoTkIA8KCbnMTHF77a6pe1PgKqj2sG2omPz6vot6p7gs2HJYKmGiJIT1AgR9KMua9OvI36iRYgi6BkL/AcY89RFR0XLetln4cMiIcPbw7QvbkJv1sv8ZwXpmdLL18t8pMejKVZYtHH2YCqc9drWcQ4JOY87izyPG37YZV/dhhCy6HMGDo3EcvqqzupfhVwEeO0ER9fo7I/juAMpLTznUBwqoixJ6gMxLgTlBflfrUaHR1NdHQ06enpBRb6jxw5AhSc3QBQv359wAirLs5rFPV4n89HWloaFoslsoOV9D1Oxmq1EhMTk+uPECNys36viidkk/7T8mwrnAy3zqSZXhZtCbJ+X/m17rt9Om4f1AqFiSZES/iDkOHJzo9QZGP1YE67oPJiYyg/olOj/C/CW9eXCTvH5BdoHaZ2rHFBfjS9+loSpLl0glq2r3pclERqjnDhgyk6tWIkHrvQhq7DpgMajWvJEQGmXpzE4VQtYrWz84hG01ogn4K4NTYUVPXrqoqxbVqxU41YiIWtgQTlx/HQPp6Q07Ipyvj3yYHqYMwJLl9ei5IOof33ir6n1vI1YZAFfxAe/NqTK7xdcHqw/bDK41M8ZHqgdg6xIDtDouhtPm1FgPgoifM6519UbVbbeI0dh8X8ISiYcD5TfE7LpkLGXzjQOmxVF2Zge3OBVpLFpWuzkuVILN+hkubS2XdcY9P+osf5N4uzs3n+XB8s3YcUVCmyQ63FcVIgEAgEAoGgulLugoQkSbRv3x6ArVu35rk/NTWVpKQk4uLiIoJAfnTo0AGALVu25Hv/5s2bAejYsSMANWvWpE6dOhw9epSUlJQ8j9+yZQu6rtO+fXtkWS7VewhKzrIdxsVgizoyq3er7DlqXIBOWeLHFXLSWbCx5BeMuq7j8hV9YRIWGcKe8bVChfzkDC0iSIRDFisiR2JjAYHWYcI5EkChK/TDK2GPpVffIlS4EB8Wi+JPChc+kKLRsKZMuwYKo88yinFdmmT/JvXiZbwBo9CSlqVxPEOPFOdKS/dmCvXiJGb+G0DVyv/Cd+bqbOHjQHL13ZYVyafzfHy5oHRZPSmh3JVcgoQzJEjkE2x9cqB1mOsGWpj2oJMup7jy9rzOJi7rY2bpDpUnv/cWmGMhqF6oms69X7oZ+7KLn1cE6NpU4dqB2Z0QkQyJIkKt/UGd/45pdG+uRPInTqZZYliQFVkkgoIJC641nBJRoaaI4ggSjU4hL6IgShps/ef67OPkrH8LXyyQ5tKYuTpAp8YyibESf20IiHn1NCAs2JtFlqhAIBAIBAJBtaVC+vlHjBgBwPTp0/PcN336dHRdZ+TIkYW+Rr9+/XA6nSxcuJD09PRc96mqysyZM5EkieHDh+d53xkzZuR5vWnTpgHket/CPufu3bvZuHEjDRs2FILEKbBsh0qUDR6/0LgC/ml5AI9f56uFfurFSSRESyzYXHJB4suFfno/lsnBlMKLuMczcxcgw8Xv4xl6JNC6X1ujqHgotfwvWjfsU7GYoFW9/HfFNqHbFRmaJRa8uyaeBh0SEUEi9F1qRst4/EZXS5ZXJzVLj6zOvGeUjeaJMqO6ZReA68UZ9x1OzQ51bX6KgoQsS4zpYSbphM7KneVb4PP6df5cH4h0iOwTgkSRHE7VmDTTx2u/+kolxiWfJFCCIYQBnMinQ2JvSJBoclJRzmaRaNvg1CsjkiTx5MU2BrY3MWNVgLd/K53QIqha7Dqi8ftaw27ux/ucfHePk/YNs8dLcTskDqVqaDo0Sij4WNCslvH3ThFsLSiEtBwdEiZFwm4pSpAw7mtcyNgrLYmxMnVrSJEFGoURUHX+2hCkVV2ZRgkSv60tfLHAz8sDeANwTX8rQzub2Z+ss110D1V7wqHWVuG+JRAIBAKBQFBtqRBBYsKECTgcDiZNmsTSpUsjt2/fvp3HH38cgAcffDBy+5EjR9i2bVvEIgnA4XBwww03kJmZyS233ILfb7Rg67rOQw89xH///ce4ceNo1apV5DkTJ05ElmUef/xxtm/fHrl96dKlTJo0CZvNxp133hm5vV+/fnTp0oW///6bt99+O3J7RkYGEyZMQNd1HnjggXLPFThdyfLqbNincnZLE71aKTROkJm+MsD3//hJc+lMGGxlUAcTu5K0EtnVePw6H//lxx+E+ZsKXy2XkpHboiX8tyFIGOJAzxYV0yGhaTob96u0a6BgVvIfUy3rKkiSsSLbai543CXWMHbloyeqryARLijXDls2hVaqp2bpEaGpQU0p9LfM7Mei6N8u2yKnfsjv/1CaFinGNat96p9rTHfjPaaXs23Tgs1BXD64fpAFRc5ekVod2H5IZcizmUWuVi1rfljqR9MhqBn/LinJEcum7ENhdodE3t9/77HQKuFyKMqFMSkSk66107GRzAd/+lm0uWJ/08pk8mI/V7/tIiWz+oz94rDtsFFovaqfhU75dMOFBYlMT+Hz9/7j+dvm5CTWYdjebdinElSr7/FAUL6khua3sAAbZZPILDhyjQPJGhZT9uKHsqZDI4VdSRoef+FjdtUulRMuneFdzYzqZuZ4hs6q3dlCRkqmxuHQuVtQ1fl2sZ9aMRLDupgY2smoXufssBBUTwIhQcIsBAmBQCAQCASCakuFCBINGjTg7bffxu12079/f4YOHcro0aPp0qULSUlJvPTSS3Tu3Dny+EcffZS2bdvy6KOP5nqd5557jg4dOvDDDz/QqlUrLrnkEtq1a8ekSZNo0qQJ7733Xq7Hd+/enccff5ykpCS6dOnC6NGjGTp0KP3798ftdvPZZ5+RmJgYebwkSXzxxRfExMRw9913c9ZZZ3HxxRfTsmVLFi9ezKhRo5g4cWL5/linMSt3BVE16NXKhCRJXNzbTGqWzqSZPuKjJC7qZWZQB+PqYkERwkJOfl4eiNgP/LOt8BV2kQJkqDMivBr/WMiyqWltOVJsLG9BYsVOlXQ39GpV8Mpqh1Xi+kEWrulfeNBpdodE9S3kZXdIZFs2gbGSM2IXUUghrn688fjDqRo7QnYlzRNPvSDXLFGhSxOFOesCpLnK7vd1+fRc2+vX1QFkCc7vbqZ+vBQpPlYkB1O0EttZHE3XuOVjNwdTdKb8XXJRoLT4gzpTlwVoUFOifrzED/8ESpy7kBKZD4qXIbFwS5AaTommtcv30OmwSrx1vQOAaSvPjOKZL6Dz7u8+Vu9WmfiJp8jC5Kmi63rEMrC4HEvXSmXdtu2QsS+3LqATzhmyzHEV0SGxP6V4q9SHdzGz+6jGM1OF7Zcgf8LzW1iAjbJJhY6/fcmGZeKpZDIVRvuGCqoG2w4Vvk/OWWfMh8M6mxh1lrFYYHZICD+cqjHuFReDn8ni2nddvDLDx5ETOlf0tWAxSXRvrhAfJfGXyJGo9gjLJoFAIBAIBILqT4UIEgA33HADv//+O/369WP58uUsWbKEbt268fPPP/PII48U6zViYmL4+++/uf/++wGYOXMmfr+fiRMnsmzZsnzDpp999lm+/fZbOnXqxIIFC1i3bh2DBg1i/vz5XHnllXke36VLF1atWsVll13G/v37+e2336hduzavvvoqP//8MyaTWI5TWpbvMC40+4QK8ON6mjHJEFBh/AALdotE71YmrGaKbdsUUHU+n28IGp0bK6zcFSy0KHn8JM/42iFh4kCyxuFUneaJMvFREjZz+QsS4RX3Y3sUHoT70FgbV/QtXJCo4ZSwmOBYNbZsOhbaNmHLorAgkZqlcSDUIdGwZsFTVr1Qh8ThNJ0dhzVqOCXinWXz2a7qZ8EXgF9WlF1x+L4vPQx6KovnfvKw77jG31uC9GmtUCvGEMUOpGhoFZBbEeanZX6GPJvFe3Nyiwpr/wsy+OlMnvjOk8cSzeXTue1jN0kndBrWlFjzn8rRExUjpPyxLkhqls4V51i4sq+F5EydP9YVv9C06YDK8lCmTc18MiRST8qQ2HNUZecRjaGdTJgK6GgqS+rFy5zVTGHRliDuYuTjFJeDKRreci72l4a/NgRJd+u0rCOzfp/KQ5M95Zrb8uf6ICNfdEXGQFFsPagy8Kksrn7bzX/HSiZkbD+kYjPnzR4JYw5Z5mQWJUgUo0MC4KFxVvq2UZi6LMC7c4TtlyAvOUOtAaLtBVs2qZrOoRStyHF3KnRoZJwXbjpQ8PEjqOrM3RCkZR2Z5nUUWtRRaF1P5s/1AdKyNG792M3xDJ1+bRXW7FH5epEfswKX9jbOsRRZ4tyOJnYmaSUWIwVVi3CotbBsEggEAoFAIKi+VOip3LBhwxg2bFiRj/vyyy/58ssv872vRo0avPbaa7z22mvFft8rr7wyX/GhIFq1asX3339f7McLisey7UFqx0qRLIRaMTLDu5r4Z5vKlaGCu90i0ae1icVbjOJUrKPwwt9vawIcTtO5Z5QVSYL1+1TW7FHp1Sr/oX3yiuiwMLFql3Fx2ixRQZIk6sfL5ZohkeU18gK6NFFoWrtsvOdrx0rVu0MiXUeSoGbUSYKES48EPBdWEKkVI2GSjYLrziMq7RsadldlwbAuJl6aJvHdEj/XDbSgnOIq0T1HVRZtCWK3wLd/B/jhnwBBzeiOAKMTZMk2leMZOok1yr/4neXVeWOWUbh8/w8fvVopdG9u4niGxt1feDieofPT8gDTVwYY28NMs0QZHViyNciWgxp3jrDSPFHmni89/LE+wPgBVsBYhT53Q5BOjZWIrVhZ8e3ffqxmuLCXGQmJd3738c1if+Q3zEnSCY1lO4L4AuAP6CzaGox0U43tYc4VEBzjkJCkvB0Sc0Jix7AuhQuIZcnwrmb+3aOyeEuQ4V1P/X3TXBqjX8rivM5mXrnGXgafsOyYuswoHn51p4Pnf/by25ogr87w8cgFtnJ5v9Uhm5eVu4IFHi9ysnRHEE03gnfHveLinlFWxg8oei7QdZ1thzVa1ZMLfWyUTSoy1Hp/soZZgTpFzAlmReKtCQ7Gv+PivTl+3D6jq8JiNmwAOzYSy4rPdNKydGTJmO8AoqwSe7z5nz8cSdMJqOWTHxGmfUPjtTcVkiOxerdqiNB9s+fC0WeZeX2mj0snuTiQonPvaCu3DLWS5tL4fU2QmtFSpCMW4LzOZqYuC/Dn+iC3nif2g+pKpENCCBICgUAgEAgE1ZYK65AQnNkkZ8LOJI3eIbumMC9eaeePJ6IiF8UAgzuYUDVYvKXwlauapvPpXD9OK1zZ18I5bYwrkyXbCn5ecqaOIkON0PvZLBLRdtgdCrRuXsfYJerFSxxKLbl9TXH5Y10Aj9/oEikrasfI1TpD4niGTs1QwCZAfHT2SvUDKRo2c7aAlB+KLFEnTmLtfyouH7SsW3bTm8UkcWkfMwdTdP7eeup2Dz/8Yyzv++BmB69eYycuSqKGU+LcjtmCBFRcsPVn83ykZOncMNiC1QwPfu0hJVPj3i89HEvXeeVqO1/e7qBzE4WfVwR49VcjSHr5TpVxPc1MHGahfzsTNjO5uhSWbFO583MPk2aV7SrtzQdU1u1VGdXNTJxTpoZT4vzuZtbvU9m4L3dB63CqxkWvunj0Wy9P/+jlxWk+lm1XGdXNxIyHnLx8de7CvCJLxDqkvILE2gCxDomzW1ZcEeu8ziYkCX5fVzadOat3qXgDMHtNoNw7wErCvuMaK3aqDOlkIj5K5qUr7XRpovDlQn+erpyyYstBY5xsLmRFdk7W71WRJPjoZjuJsRIvT/fxyvSix/XxDJ3ULJ3W9QofN1E2iayiMiSSNRrULFzYCOO0Snx0s4NGCRJfLPDz9FQvj03xcsnrLl78xVslu2QEedl6UOXz+b4yPxdJcxkLPsJjKcou4fKRb1feip3GnN6kHK3q4pwyDWpKbD6Qe/7+YamfV2Z4+W6Jn28WG917wzpnnzeN7Gb8+0CKzkVnm7l5iCXyelf2s+QRkM9uqRBtFzkS1R1/6DTDIgQJgUAgEAgEgmqLOJUTVAir/zMuenuflJdgMUl5LigGtAvlSGwO5FntvHp3kG8W+1E1wy5mZ5LGDYMtxDgk2tkMu6V/tgV5YEz+nyM5Uyc+Ssrlg1wrRibTEwpBDnVv1I+X8QVUUjL1XP7yZcX0VQEsJhhZBquewyTWkFjzn44/qOda8V1dOJ6hUTtHYGZ8lLEtUjN1DibrNKwpFxkoXy9OZmWKUdBoVUQBsKRc1sfCx3/5+fZvPwPbl367uX06v6z006KOzNktjI6coZ1MeAJ6JNy2Ua1sK7GeLcrk4xdI0gmNLxb4aVlX5r7zrTRLlHn8Oy9j/s9FSpbO1f0tkf3w7JZG8KjbpyNLEhYztKprbBeHFQa0N/Hn+iBH0zUSoiVe+9VY8r10exBd14vcfi6fzhfz/bRvKDOoQ8G/cTir4sp+2VZmV/ezMHVZgA/+9PHm9XYsJgm3T+f2T92kZOk8eoGVFnUUrGZj/64bV3BxLc4pcSKHZdOeoyo7jmhc3MtcYAB9eZAYG7Jt2mzYNjmsp/beK0OdYKoGXy/y82g5dR+UlJ+WGdvzkt7G9rSaJW4aYuH2Tz38tSHA9YOsZfp+qqazNSRIhIWJotiwT6VlHZkB7c30aGHi5o/cfLXIT5emCiMKmce3Hw7nRxQlSEByRsFFZ1XTOZii0ad18U/bEmJkpj8UxdaDKv4gePw6n87z8/UiP0u3B3n1GjttG4hV4lWZj+f6+H1tkMa15IhgDcYK8YMpGs0SS7f9UrP0SBciGIKYroPbb4zFMG6fzlu/+YhzSozqVr7dYR0aKvyxPojLp+O0Shw9ofH0j15yajHNEuVciw3qx8ucf5YZj1/nqUttRR5jLCaJXi1NzNsYrLbnSgLwhQUJRQirAoFAIBAIBNUV0SEhqBCyBYmiiym1Y2U6NZZZtDnI3hxe3ZsOqNz8oZs564LM2xhk+Q6V2rES1w40iliyLNGntcLWQxrJGfmvek3O0CIZBWHC/5clImG19ePLL9j6YIrGql0qQzqacnWGnCq1Q2HQ1TFHQtd1jqXr1MphrRAuliRn6hxKLZ5/dXi7Qdl2SADUjZM5t6OJv7eq7DuFwOnf1gTI9MAVfS2R4onNIhHnzP68YWuMU3mf4vLmbB/egJFVosgSF55tZmQ3EylZOt2aKjw0NrsYLEkSLesqdG5iomNjhdb1lFwFoOFdzOi64c8/698A2w9rRNmMVeI7jhT+XVbuCjL25SzenePjrs89rN+bfyfKgWSNX1cbdmcdGmYX41rXVxjY3sT8TUEuetXFur1BHp3iYeshw1Lq2oFWzmljontzU6FiBBhjL2eHxB+VYNcUZlgXM94ALCqiY6w4rNwVpFaMRMu6Mj8t85NZxIr8iiCg6kxbaYST98rRfXJOaxN2C+USQLvvuIY7FJVyPEMv0uru6AmNpBM6nZsYn89hlZh0rZ2EaInHv/MU6ke//bBxX5v6hY+5aJtUaIbE0ROGbU6jEtrmOKwSZzU30bu1icEdzUy+y8G9o63sPaZx1dsu4aVfxdkZmjff+d2Xq3vhkW88jH7JFRlfJSU1SyfuJEEC8uZIfDrPx7F0nTtHWMv0fCU/OjRS0HUiYuGsNQF0HZ6+xMantzl4+lIbb1xnzyM6vDrezrs3OootLjSpJaPplFv3laBojmecWo6HsGwSCAQCgUAgqP4IQUJQIXj9xkrq4vrITxhsxeOHy95w8+/uIPuTNW750I2qw7d3OdjyZgxb3oxh8bPRkUI8QN+QbdM/2/MWsXRdJyVTzxVgC9k2QA1rypEL2mxBouwLdtNXGlYBZWnXBJAY6i44Vg1zJNLdRrh5TrHIaQWzAlsOqAS1wgOtw9SLz35+q7plv/L3qtCK/LB1REnRdZ0pS/w4rIWHmTeoKSNJhkVLWfPfMZUJ77u47A0XF72WxYxVAfq0Vujbxvi9JEni2cvsPHaBlXdvtJdoBemAkG3TrNUB3vrNR6wDnrrEsERams8+CXDCpfPsTx7Gv+MmOUPn5iEWFBnu/NyT71ieNMtLQIV7R+ddNf/2BDt3jrCy97jG5W+4+WNdkBFdTUwcVngo/MnEOQ1B4kRIlJizzrBr6tWq4leTh22b5pyibVOaS2P7YY0eLRSuG2jB5YMfl5VuHJclCzcFSc7UubiXJVfnms0i0b+dibV7VY4XIDCXlrBNU/fmSuj/hRfG1odswDo3zt7+tWNlXr/WjtcPd3/uKTB4fNsh47lFdUg4bQVb5kD2XNAw4dSKwooscctQK5/c6sDrh3u/9Aj7piqKP6iz95ix3bcd0vhzgzGH/rk+wG9rjUyTyYtKvg+rmk66WyfOmVOQMP7OKVImndD4fL6f5okyl/YpfzE2LDCHcyRmrg4QYzdygvq2MXH5OZYi96PiEO5ALI/jq6B4PPOjl/HvuEv9fH/ocCgsmwQCgUAgEAiqL0KQEFQIz1+i8cuDzmI/fngXM+/fZCcQ1LnuPTfj33GR5tJ5fbyds5oXfAUStrMIB9bmxOUDbwASonMP+/Cq/GZ1ctoAGBfqZd0hoWk6M1b5qRUjlch6oziExZ6j1bBDIlxwzClISJJEfJTErqNFB1qHqRda+V4vToqs+CxLzm6p0La+zLd/+/mnkKySgli/T2XLQY2x3c2Ffj6rWaJOrFQuBZNP5vpZul3lSJpGmkunZR2ZRy/IbXURZZMYP9Aasc0qLg6rUURev0/lcKrOzUOtnNvRhFnJK0j4gzqfz/dx3nOZTPk7wFnNFGY8HMV959t4/go7x9J17vrcE1kJCbBub5Df1wYZ1MHE2S3z7j8Wk8Ttw61Me8hJzxYKPVsovHhl3hW1RdGlqYKqwbDns3j7Ny/bD2sM6WiqULumMCfbNpWW1SG7pp4tTJzf3UytGInJi/wE1MqbLzI9Om//5kOR4YKz8xY8h3YyOm7mbSzbLomwTdNlfQyhqihBYkNIkOjUOHcx9OyWJu4ZZWVnksb177vz7bTYfkijfrxEtL3wsRNtz7bMyY/wXFBWwcK9W5u4c4SV7Yc1XpxWRJq2oFLYd1wjqMElvc04LPDu7z5SszSe+dFLrANa15P5dXWA1KySHSfS3Tq6Th7LJsjdITFpptE99/A4ayTbqTxpF7IP23RAZecRlW2HNIZ1MZe5rVK4y2h/BXQgCvJnzzGN5Ew91/G9JISfZxGOcwKBQCAQCATVFiFICCqMkl7QDmxv5tu7ncRFSSSd0PnfxTaGdCp8lV7tWJnW9WT+2RbMs9I0bON0ciZErVBnQfPE3L7EUPaCxD/bVQ6k6IztYS7zC/xwh8TRE9XvIvt4yDs9p2UTGAWTsH90w5pF/17h7dayHLojwBBJ3rjOTpQN7vvKXSJLJV3X+WSuUW28om/RK/Yb1ZLZn1z6YPUDyRovTfPmKjBluHV+WxOgcxOFxc9GM/+paH59JKpMf6/hIVujujUkru5nwWGV6NpUYdUuNVJE8Ph1LnzVxSszfETZJV4bb2fynQ4ah1aujj7LzA2DLazbq3LnZx6SThi/wyvTjeL1A+cXninQoo7C13c6+fpOJ3ZLyfezGwZbePUaO1YzvP+Hsc2Gl2HeS0kZHrJtemWGt9SixMqIIKFgMUlc1c9C0gmdOWvLttj/2q9envjOg3rS/PvdEj/P/+yNdJ0EVJ27v3CzM0njnlFWEmPzno4MaGeIWX9tyNsdElB1vlvi55kfPSUuam05qOKwwtBOxusXFWy9bq+K0wrN6+T9jDeea+Gmcy2s36ty8WuGVVgYX0BnzzGNNvWL3r8KsswJk90hUXanbTcPtdCntcKPSwPMXC1Cfqsau5KMbd6zhYmr+lvYlaRx5VtGJs7jF9q4ZagVfxB+XFqybZcWysfJadkUFsyyQtrUjsMqv64O0LeNQv92FTP3xTgkGteS2XRAi4zH0WeV/XuHjzN7TxL8dyWpfL3Ix9r/gqUulAuKRtd1jqQZv/0JV2kFCeNv0SEhEAgEAoFAUH0RgoSgStO2gcK0B518c5eDy4tRxAXo19bwv7/rcw87j2SvfE3ONC58Ek6ybKob6ixokaPYVDNawmoue0Hiq4VGQfXKYn6XkhAu6FXHDonwSsWclksA8Tm2VXEKcc0SZcwKdG1afsvmmtRWeP1aB5kemPiJu8AC4slMXuRn3sYg53U2FStwu1GCTJaXXFkGJeHHZX6+Wujno798kdt+XR3AG4BLe5dfgWlQBxODO5h4+lIbVrOx/fq0NuENwJr/jP1x8iI/u5I0rh1o4ffHohh9ljmXXQ/AfedbGX2WiUVbgox8IYtHvvWy5j+VS3ubaV6nfJdFSpLE+d3N/P54FLeeZ2H0WaZKsWsKM6a7mY6NZL7/J8Dol7KYvyn/AmRBdj+QnR8Rzsm57BwzFhP8tLzsbJs27FP5dJ6fn5YHeGNW9ribvSbAM1O9fLPYz+iXsvhzfYBnp3pZul3l0j5mbjw3//kw2i7Ru5WJFTtU0t3Gd9N1nQWbAox92cUzU718908g0sFQHHRdZ8tBlbb1FWwWI0+jsA6JoKqz+YBKx0YKipxX3JJlifvH2Hj9WjuZHp1r3nazILR9diVpqJqxkr0o8rPMycn+ZA1JMuzcygpFlnjlaju1YiQe/sbD/V+52VHKTIKTOeHSmbrMX+iYFBRO+PylRR2ZCYMtOK2w95jGgHZGl9PQzibq1JD49m9/iQroqSFBIt5ZcIdEeK6+un/Zn6sURoeGMnuPaUxbGaBODYkezct+3q0dY5zfndwh8eoMHy/+4uOKN930eCSThybnFVYFp84Jt44ndNgJz+slJRxqLTIkBAKBQCAQCKovQpAQVHlqRst0L8Sm6WRuONfC0E4m5m4MMuZlF3d+5ubZnzyR1ekJJ63CH9LJxFOX2BjZLbtIK0kS9ePlMs2Q2J2ksmSbytBOJurFl/2uVzvcIVENMyS2hHzW2zbIXXyIDwU9S1LuwOqCqB0rM+eJKCYMLt8iSr+2Ju4fY2X3UY1zn8lkwP+MP49P8eT7+FW7grwyw0eT2jIvXGEv1ntEbCVKadsUtqX5aqGfQ6lGh8EPS/1E28k11ssau0Xi/ZscDGif/R7nhOzJlm4Lku7W+XSej/rxEvefb42IFiejyBKvjXfw4c12akZLzFgVwGGF24cX3h1RljitEveMsvHaeEel2DWFiXFIfH+vkycuspHu1pn4iSeX0ASGzcmAp7KY8ndegeGEywgV79EiO4Q8zinTr62JVbtUUjLLZs54c7YXSTLygj6d5+e3NYZY8NgUD7ViJJ64yEZANcTiqcsC9Gur8L+LbYVaag3tbCKowaLNAZIzNG7/1MNtn3g4kqYxvIsxrsJjPUy6W2fWv4F8i+EHUnQyPdn2MO0bKhzP0AvM3tmVpOHx57VrOplR3cx8d48Tp03isSleUrO0SH5ESTskgqrO+f+XxVM/ZM8n+5M16sZJZW5fkxAj8+ltDnq3Vpi9xjhm3veVG1/g1I597//h48nvvSwphbWdwGDnEQ1ZMoT2OKfM7cOt1I+XePYyY58xK0an0/EMnT/WFf93To10SGQfU8PjLyyI7Q51Z5RXt2FBdGhkvN/xDJ1R3fIK1WWBLEs0SpDzHFu3HlJpXEvmrpFWmicadlgrd4rQ97LmSFr23FJaQUJYNgkEAoFAIBBUf4QgITjtiHPKvHODg6n3OenTWuGvDUGm/B1g0ZYgJtkoluXEYpK4oq8lT6GnfrwUKeSWBZNDQcjjB5RPsdxqlqjhlDhWDTskth5UiY+SqH2SnVbY4zoxViqwcH0y9ePlMi/a5ceEQRYmDrPQpLZCnTgZRYGfVwRYviN3Yehousa9X3qwmODdG+xFesmHORWfa13X2XxAI9ZhWBu8OcvH2r0qO49ojOluLpWN0anQrqFMrMPIkfh0no8MD9w10lqs7TSwvZmZj0TxyDgrb1xrzyMonikossTV/S389ngUrevJvDnbx99bjbGWmqVx12dujmfovDHbm8cGY/XuILpuWL/kZHgXM5oOf2049aLx8p1Blm5XGdPdzKe3OagVI/HYFA8TPzGCS9+/0cHV/S3MejSKkan0AfgAAEWWSURBVF1N9Gml8MZ1jiKt6wZ3MCFL8Nl8P2NedjF/U5BR3UzMeSKKpy4x2gq2HMy9j3y10McDX3uYkY8N0ZZQN0S7hsY4at+w8GDrdXuN27s0Kbry1baBIbCkuXSe+8nIHoHiCRI5LXMWbwmy84jGLysCpGUZx6D9yRqNyrA7Iiet6yl8dpuTqfc5GdDOxG9rgtz/lYdgKfNFVE3n97XGb790uyjolpZdSRoNE+TIsW/CYCvznoqO5EUBXNLbgs1sjPninKscTNGYFOpeapqYU5Aw/g53SOxOUnFYDOu9iiQcbA1wfvfyE84bJxgLTsIZOimZGsfSdbo3U5g4zMrr1xoLB6avElZmZU3YrglOoUNChFoLBAKBQCAQVHvOzMqO4IygY2OjyLL8xWiWPBfFkueiWP5SdLFX/NWPl/EFICXz1Av8J1w601cG6NBQLlc7ocRYqdplSARVY/V22wZynpXSYY/rsvRNLyskSeKukTZ+uNfJD/c6+foOJ2YFXp/pjRSG/EGduz/3kJyp8+KVdlqUwGooLEjsK0WHxJE0nRMuY4Vp/7YmZv4b4P9CwbXhIN+KRJElerUysfmgxuRFflrWlUvkDW6zSFw3yJqr6+JMJTHWEFyjbfDA1272HlO570sPh9N0hnYykemBz+bl7p7ImR+Rk4HtjQyFP9edWtFN13XenOXDJMMdw63UjpV5e4IdVTOs8v7vKjsdQx0GtWJkJl3n4PPbncUKnq8ZLdOtmcL2wxqqpvPGdXZev9ZBYqxMXJRM3RoSW0/qkPh3j/H/D/7w5Smqbw49tn2ODgnIzpEIqjpLtgXx+o3nFRRoXRAjupoY2snE72uDka6eBvFFf8+cHRJTlxvbI6DCzNUBUrN03D4jV6Y86dhY4YOb7Iw+y+gwfPpHb6kE+ZU71Ugu0PKdVb9DYtn2YC57x6qAP2iIUC3zyS3JSQ2nxLieZjYd0Fi9u/DvsP2wyhVvutifrPG/i225iv/Zgpix3XYd1WiWKJdLh0JhtG2gIEvQso5cLKuz0tKoloyqweFQF+y2QyHxMDQvNK2t0KmxzF/rA6XO7RHkT04r1NJmSARCHRLCskkgEAgEAoGg+lL1qnwCQRlTwymRECOTECMXqwAWpiyDracu8+MNwLUDrYXak5wqtWMljqbrkSLSkTSNPUerVqHlZPYc1fAFoF0+q4jDHRINy2llcFnSoKbM5edY2Lhf44/1RhHu/6Z5WbdXZcJgS4lDkcMizIHkbHGjuHZcYQubdg0UHhxrRZZgwz6Nrk2VYuVXlAd9WpvQdfAG4N7R1nz9+AXFo1GCzKvX2MnwwIWvuVi+U+WKc8y8eb2dVnVlJi/25xorK3cFSYjOzo8IE22X6NvWxIpdKmlZpZ/nFm0Jsm6vyiW9zZFx27WpiU9udfDW9XZGnGIg+L2jrFxxjplfH47K81ptGyjsTtIiFkNBVWfjPhVZgv3JOjNOWuG85aCK1WzY4ICR72BWDMsrTdN5/DsvN37g5uLXXWw7pLJhn0r9eKnYnTmSJPHUJTZiHRLpbp3W9ZRiFXXDx6ZdSSqLNgfp1VIh2g4/LQ9ERMlGFSDMyrLEi1fa6ddWyZMFUlxmrzF+83YNZLYd0kg9hbFV3hxN15jwgZsLX3XxzWJ/mXVEnir/HTXyR1rULXqbXzvQgiTBp/MKzoPZfkjlmrddnHDpTLrWzpX9cgvT2YIYZLh1jqXr5Z7Vkx9RNonXxtt58Up7uZ4rRQT/48axMmyv1rZ+9u89tocFtx/mbhBdEmVJTsumE6W2bAJZAlPVPzUUCAQCgUAgEBSAOJUTCAogLEj8tSFYausKMApk3/7tp1aMxLAu5bucKzFWxh80LvJ0XefmD91c/oar2MHLlcHWAvIjwAgXh+ohSADcep4FhxXenO3jp2V+piwJcHZLhftGlzz3IMomkRAtsS9ZY80ew9v93KezWL276BXHmyO2NAot6ypcHAqxvqxP5XUYhHMkujRRGNReLGs8VQa0N3PHcCtunxHi/uiFNhRZ4t7RVrwBozvA7dN5c7aXbYc0eubIj8jJsM4mVA3mbix8XC3bHuS6d1289qs31+2apvPmbB9WM9w6LPc479XKxLAupz7mzmpu4qlL7bmsasK0bSAT1GDHEaPoveOIhtsPV/Q1E+uAD/70RWxZdF1nywGNNvWUiFWUxZQdbP3KDB8zVgVoU98I1r3kdRe7kjQ6F7M7IkxCjMwTFxkeOO0aFG/uClvm/PBPAE03woRHdzOz44jG72uMbVNR86DFJPHW9Q46NZb5eK6fXUnFF7X9QZ0/1gdoW1/mir5G0XtFFfbhX7DRsDOzmOD5n73c9bmHNFflCyg7QxkOxemqa1pb4bzOJhZtCbL9UN7fWtd1XvjFS5YXPrzZka9AmDNDYndoEUPzxMo57o7sZo50VJUXjSMdiMbcED4PaZ1DsB/Z1YRJJl/rN0HpyWXZVMoOCV9QxyoaJgUCgUAgEAiqNdWjyicQVAJnt1SoW0Pi03l+xr7sYtHmQKlWTy7boZJ0QueS3uZyzzYIB1sfO6GzZo/KziSNDA9MW1l1L6i3hvzf2+ZTuOvWVKFXS4UhnapHAbtmtMyEQVb2HtN44nsvdWpITLrWXqRPfkE0SpDZvF/lqrfdHE7VMClw/1eeXCuOs7x6niLUloMaZgVahOw+Hh5n49Vr7OXqyV0UDWrKfHSznTevL9+Vr2cSt51n4f0b7Xx4syMytwxsb6JrU4WflgUY+WIWH/7pp1VdmTtG5C+KDe5gxqzAnAJsmzbsU7n2XRfXv+9m+U6VT+f5c4VIz1kXZNshjav6WUiMrfhTirD1Uti2ae1/xt9925i4fpCVgyk6M0Lz35E0nTSXnkckCAdbf7nQz1nNFL6/x8l39zqpFxKlu5TCZm/0WSY+uMnOxGHFEyPDljnJmToJ0RID2pu4qLdR0P/+H2Ple+NytmzKicMq8fBYQyX54Z/iHz/+3hok0wOjzjLTu5Uxby/bUXVtm+ZuDGBWiGSb/LUhSP8ns7j9Uze/rQmccrh3aQmLQEVZNoW58VxjnOXXJbFkm8rKXSoX9DRzTpv8j6UOC0iScTzZHRFDTt9LhPC+FM5o2nZIo2FNKVfGU1yUzID2JpZtV4vdnSgomiNp2WJC6UOtRX6EQCAQCAQCQXXn9L3aEAhOkZrRMrMfi+KO4VYOp2nc8rGHT+YWbIlQEGH7ijEVUAwOryA+lqHz4zLjfR0WmLzIh6pVzS6JrYdUHNbsFYs5iYuS+fIOZ7FzP6oC1w2yUDNKwqzA2xMc1Iwu/TTbvI6x+rtrE4XpDzl5+lIbR9N1HprsQdN0Vu8OMublLMa+4mJjyOveCLRWaVUvO9zbaZU4v7u50m2SBrQ3UyefVe6C0iHLEoM7mol1ZG9XSZK4/3wrQQ3cPp3HL7Lxy4NOmiXmvw/FOCT6tDaxfIeaa2W4P6jz+kwvl73hYlWomPn+TXYkychJAaP76+3ffTitcNO5FZ9NAtmdVWFBYt1/2SHUV/e3EOuQeHeOj2enenhwsgcwOodyEhY1WteT+eAmBzaLRMdGCtMedDLpWjuXn1Py7yZJEoM6mIu9/+e0E7zgbDNmRaJ9A8NHPxDSfxpUcKdYt2YKLevITF/lx+Mv3vFj9r/GcWdkVzMNaso0rCmxbHvVFCSyvDordqr0bKlQN07m9WvtTLrWTo/mCgs2BbnvKw/3feWplM+264iGIucOni6Mjo0UerdS+G1tgIMp2fuxpulMmunFYqJAURKMucRpBZdXZ1dIkKgMy6aKok4NCYsJ9idreP06e45q+YbPj+luRtNh1r9Vd1FHdeNwWnY2yqkJEmJhg0AgEAgEAkF1RlSGBIJCcFgl7hhhZc7jUbSsK/PmbF+ukM6AqjNnbYBMT/4XVV6/zl/rjTDrJrXL/+I+MdQhsfOIypx1Abo2VbhmgIX9yTqLNle9opCu62w9qNKmmD7r1YEom8Tkuxz8cJ+z2EG4BXHXSCsf3mznm7scNEtUGNfTwkVnm1myTeX6992Mf8fN8XRj7P2ywhDLjmXoJGfqtMvHAktwZtC9uYmp9zn544korulvKbJDZ1gXw7bp8/l+lu8MsnR7kMvfcPHJXD/tG8jMeNjJS1fZGdzBzJjuZv7ZprJ0uxHavPeYxnWDLMRFVc7pRJ0aEnFOiS2hTqu1e4M0rW0EXkfZJG4aYiHphM6UJQE27FNpWUem70mrxEd2M3PbeRY+vc1BTA5xx2GVGNmt/DvbILcgcXEvQwCRJImLehlCdkK0VKIMpLJAkiQuO8dCpgd+X1t0Qdbl05m/KchZzZRId0mvViYOpOi5iuRVhb+3BgmoMKSj8RtLkrG9P7/dyaJno+jf1sS8jcFcHUEVxc4kjca15BKNvZuGWI39eEF27sdva4NsPaRxdX8LdeMK30ejbRKZXsOyyWKCBjVPj2NyfsiyRMOaMvuSNXYe0dD0/G0jB3UwEWMn0mUlODX8QZ3jGToNE2Si7aUPtfYFdNEhIRAIBAKBQFDNEYKEQFAMEmvIvD3Bjt0C93/p4Wi6RtIJjfHvuLnnSw//N82b7/MWbQni8hn2FRXyOUOWKV8v8uMLwCW9zVzZz4JZgS8Xlry7o7w5lKqT4cnfrqk60yxRKRNBoFaMzMD25lxizRMX22hZV2bFTpU29Y1icau6MrND9iJbQvkR7RsKQeJMpmNjpdgiwbkdzVhM8MlcP9e962bC+262HdK47TwL392bu0PpzhFWzAq89quX9+b4iHVIXD+o5BkpZYUkSbRtILP9sErSCY2DKTpdc1gsTRhkYcZDThY/G8X616KZ+WhUnsJsjEPi7lE2ahUzuLo8cFiMHInerZRc1kzndzcstZrUrpzPNqa7GZu5eLZNc9YG8AZgVLfs413Ytml5FbRtCocVD+6Qt7JZK0bmnlD2z6dzSx7sfSr4AjoHkrUSWyb1bqXQroHMz8sDPPuThz/WBXhrtpdoO9w8pOh9NMomkeWF3UkazRLlSu+oK28a1ZI5mKyxKXTMbFM/7+9tMUmM6GpkuWzLJ59DUDKOnjAEiLpxMjUcUqk7JAKiQ0IgEAgEAoGg2iPWlwgExaRpbYUXrrBzz5cebvvYzeE0nRMunRpOiZn/BnhgjDVPAXDWvwEkybCvqAgSaxgXaEkndKLtMKKrGbvFuKD+dXWArQfVfFcBVhZhm5W2+VglCPLHbpH49FYHS3cEGRVavX3B2WZenu5j/qZgxP+7fRXazoKqTaxDYso9TnYeUfH4wOPX6dnClG+wbIOaMlf2s/BVSOB8cKy1wlfun0y7BgpLt6tMW2EUmLs0yf7csizRuhrML7Is8f29TmpG5f4t45wyn010UMNROb9xjENiVDczP68IsOWgWqDQeiRN45UZXmo4JUZ0yz617NXKePyyHUEu7l05tl75EVB1Fm8N0rGRnG9YOhjjql9bhTnrgtx9XIsIRaqmo+uUOhuoKPYcNVbsl1SQkCSJxy+0cf/XHqb8HWDK38b+cO9oKzWcRX/WKLvEziMqWV5yiXqnK40SDEvE+ZuM36mg85CxPc38sDQQCrw//X+X8uRwKNC6bpxMrEMiNav0odYOqxAkBAKBQCAQCKozp9eyZIGgnBne1cz4ARa2HNRQNZ13b7Dz5MU2/EEimQ1hMj06i7YE6dFcKbDgUdbEOY3sAoDzzzLECIDxA41C0FeLKrdLYvshlevedbHnqCFEbA2tOKxKIkl1ILGGzAU9LZEVgmO6mzHJMC1UNDTJ0KqemN4FxadDQ4ULelq4sp+FG8615itGhLl1qIUoG9SKkbiqb+UXmcNF8h+WGvNbdS2mtqiTf1dLzxYmWtWrvO8UztD44Z/8jx9BVef+rzyku+GlK23EObO/Q3yUkYOxYqeKrldejpGq6SzbHoxYR63apZLpMbqDCuOmIVY0HT6bb3RJrNsbZPDTWUx4311uuUzhDIfSZCed1dzEgqejmPO4k2cuszFxmIVrBxRvH42yQVao2bPFaZwfEaZJSGBatl0l1iFRp0b+Be6uTRQaJUjM+jdAUK2aWVzVhSMhQaJenESsU+KECLUWCAQCgUAgOGMRp3MCQQl5YIyV5nVkzmltokFNmYCqUztW4rslfiYMtmAOrZqcuzGAPwijK8iuCYwVkrVjJQ6l6lzaJ7sI0aGhwlnNFH5bE+DxC21E2ytnZdnHc30s36ly9xcefrzPyZaDGiYZWtYVxfNToWa0TP92JhZuDhJthxZ1ZaxmsXpQUD7ERcl8e7cTswI2S+WPs7DlW7gzrHkxg4AFxaNDI5l2DWRmrg4wtoeZbs1ynzq+O8fHmv9Urh1oYVCHvMe7Xq1MfLXQT7eHMpEAk2IEJretL9OyrhIpLNosEud1NkWOoWXF8h1BXp7uZeshDbMCV/e3RFZmn9ux8NPgHs0VOjdRmLYiQIN4mXd+9xFQ4Wi6ytSlAS4vB0EuLNSXtEMijCRJNKmtlDi3KjpHp1PzUr53daJRgvEdgxq0rS8jSfmPO0mSGNPdwrtzfCzbodKv7alfOh1O1Xhvjo/7x1iJr6T8ncrgSFq2ZVOsQ8LtM3IlSmq/JEKtBQKBQCAQCKo/Z85ZsEBQRlhMEpf1sdCgprH7mBWJK/sawalzN2T7ZM/6N4BZgfM6V5wgAcaKzxFdTXmsBcb1NOMPwl8bKiecMc2l8ef6IA4r7Dyi8cLPXrYeVGlRt2TBnYL8uaCnGU2HdDci0FpQ7rSup9AssWqMs8YJMo6QRX7nxkquzBXBqSNJEvefb0MHxr/j5quFPnRdJ+mExod/+vjoLz/tG8rcf37+OQWX9TEzpKOJHs1NdG9uonV9hT1HVaYsCfDMVC+Pf2f8uf8rD5/MLbsuvkyPzh2furnuPTe7koxg55Z1Zb5Y4GfGqgCNEqQii/6SJHHLEAsBFSbN8pFYQ+Lbuxwkxkq8PstLckbZhnV/v8TPVwv91IqRcmWJVATOHIJEacWQ6kTO37coK6YxPYzzuBmrymZ8Tl7s5+cVAT76q+ple5Un2ZZNUsRGrDQ5EiLUWiAQCAQCgaD6I07nBIIy4NI+Zt7/w8fkRX76tTXx6Twfy7arDGhvKpZ3c1ny2IW2fG8/r7OZZ6d6mfVvgAvPLvmqTl3XC1xBWBxmrgoQUOG5y23M+jfAT8sNYaRPazENlQXhsXbCpYtAa8EZhSxLtK2v8O8ela5NxXxSHpzTxsSP9zm583MPL03z8d0/AfYd19B1w7pr0rWOAoXlZokK797oyHWbruscSdPZc0xDDdngvDTdx6dzfVzcy0zt2FMriKe5NG78wM3mAxrDuph4cIyNBjVlNE3n19UBPpnr57JzzMU6pg1sb2JAOxNmBZ67wrCkevwiG3d97uH/pnt5bbyjyNfIjw//9PH72gB9WpsY0N7Ego1Bvlrkp1GCxIc3F/x7lhfhLBizAg0TTn9Bok4Nw+IyoGZ3WRVEowSZrk0V5m4IkuXVTyk3R9d1/lxvnP9MXern1vMsuWzOTmeOpGnYLVDDIUVycdLdOrViSvY6okNCIBAIBAKBoPojrtwFgjIgPkpmVDcz01YGOPeZLNLdOi3ryNw7Kv8Vo5VBrEOib1sTizYHSc7QSIgp/gXwp/N8fDHfz6+POKkZXfILZ13XmbosQIwdhncx07+diXGvuDiWrhdZCBAUD4tJYkx3M18v8tOpkRAkBGcWbRsYgkTOQGtB2dKyrsJP9zv53/ceFm4JMrSTiTHdjfm8pMVBSZKoFy9RLz57/lc1mPiph7d/8/H8FfZSf87jGRoT3nez84jG7cMt3DHcGhEeZFliXE8L43oWX5SXZYmPbsktOgztZGJgexOz/g1yThs/zUPdQomxUrEyo064dD7804cvCNsP+/ligbFSvntzhXcm2PPNEilvwlaOTWrJZW6bVRUxKRL1a8rsPabRphgZLWN7mFn7n8qf60u3qCPM5oMah1J1mtQ23vvbxQHuGJH3XDEtS+PVX31MHGaNdORWdw6n6dSNM+yxYsOChKtkHRKaphNQwVqxzccCgUAgEAgEgjJGCBICQRkxfoCFGasCWExGF8AFPc2YqthF/eizzCzYFGTOuiBX9y/eBXVYTEjJ0pmyxM+dI/LvwCiMdXtVdiZpXNXPjM0iYbNIvHm9nZen+RjUXlxVlhX3jLLSp7VSaCCxQHA6Mn6AhRg7nN1SjP3yJMomMek6xyl3zOXHoA4mzm6p8POKAFf3txRpoxNG13UOpeqs/U9l/T6VeRsCHDmh8+BYKzcMLp9FAZIk8cRFNpbvyOLRb7257mtV18j0ufBsc4G2Zj8v9+MNwMtX22hRR2HRliCKDNcPslTayu+o0E91JuRHhGldTyY5Q6NpMXJnRnQ188LPXn5ddWqCxB/rjO6IZy+z8fgUD5MXG/ljDmvu7f7N335+WREgyiYV2PlanTC6ojS6NTP2ibAgUdJg64ARsYKlip1fCwQCgUAgEAhKhhAkBIIyom0Dhd8ec1I7Vs5zYVlVGNTehN1i5FsUV5DYlaSx77jh+zvl7wA3nmvFXsIg25+WGRfgl/bOfs9uTU38cJ+YgsoSh1VioBB4BGcgjRJk7hpZ/Yt21YWyFiPCr/nwOBsXvebi5elePp/oKPB9srw6Py71s3q3yvq9KilZ2UXN+CiJZy+zcWmfsg+czkmDmjLf3O1kxQ4jO0rHyEdasjXIp/P8TF8ZYP7TUXkEhqCq8+3fRk7EiK5mLCapStjsRYU6JM4kQeJ/F9s44SpeqHKsQ2JQBxN/rg/y9SIf1/S3lHg/0HWdP9cFqRUj0b2Zwg2DrTw91cvUZX6uHZgtngVVnZ9DtpZ/rAvwyDhrtc/GOeHW8fihXpwxvsJ2pidK2CHhD0W1iQ4JgUAgEAgEguqNqAYKBGVIk9qVX1QoDIdVYnAHE7PXBDmYohXLBuCvUFD3kI4m5m4MMmNlgMv7Fr/Qk+XV+W1tgE6NZVoXc8WrQCAQCM482jVQGNfDsD9csCnI4I65q46apjNjdYBJM30cz9AxydC6vszwria6NDHRuYlCw5pSuQgm+dGhoUKHk8QETdN5d46P9//wM39TkOFdcn+HeRuDHE7TuXOEtUr54DcJhTyfSTksNaNlakYX//EPjbWx44ibF3/xsfOIxpMX20q0DXcc1tiXrHFlPzOyLHHB2WbenePjiwV+ruib3R3z99YgSSd0Yh0SR9N11u1T6VbNt8uRNEN4qBsSJGJzZEiUBF/AeLwItRYIBAKBQCCo3pw5y6AEAgFg2DYB/LYmUKzHz90QINYBL15pJ9YBXyz0o2q5LyB1Xeejv3wMez6LmasD6Lpxf7pb59FvPXj8cEnv8l2tKhAIBILqzz2jrTit8PwvXty+7GNNcobGFW+5efRbL0EVnrnMxqqXo/n5gSievNjO+d3NNEqQK0yMKAhZlriirwVFNqyZTmbyYj9mBS7rU7WWeHdrZmLhM1H0bSMqvQXRoKbMj/c66dtGYeqyADd84MYfLH5B/Y9QmPWwzsa2t5olrh1oIemEzufzs8fKj8sCyBK8cIXR9TVnbbAMv0XlcCTN6LStG2fsn6UVJMIdEuYqJOYJBAKBQCAQCEqOECQEgjOMc9qYiHUYtk1h4aAgDqZobDmoMbC9mRiHUWTZd1xjwabsi2N/UOeRb728McvHvuMaD072cPunHuZuCHDBK1n8tSHIeZ1NjO1RtYovAoFAIKh6JMbK3D3KyuFUnQ/+8AHGqujbP/Owfq/K1f0tzHkiisv6WEpsH1hR1IqRGdDOxJJtaqQQC7DloMrq3SqjzjKTEFP1TsHrFCOQ+0wnxiHx4c0OLu1jZtUulV9WFG9xB8Cf64PER0l0b57dVXNNfwst68i887uPdXuDJJ3QWLQ5SL+2Js7taKJhTYk/1gXQtJIV7qsah0/qkCi9ZZPxeKvQzQQCgUAgEAiqNRVy5bFo0SLOO+88ateuTXR0NH369OHHH38s8etkZGTwxBNP0KZNG+x2Ow0aNOC2227j2LFj+T7e5/Px8ssv06VLF5xOJw6Hg06dOvHCCy/g9XrzPH7t2rVIklTgn169epX4MwsEVQ2LSWJkVzM7jmhs3K8V+ti5G4wL7aGdjCu/q/pZMCvw/h8+ZqzyM2OVnwnvu5mxKsDA9ibm/i+KUd1MzN8U5I7PPKS5dJ673MZb19urlDWFQCAQCKouV/Wz0L6hzBcL/Ow4rPLk917W71W5YbCFJy6yRVZXV2Uu7mVG12H6SuM4qus6788xBJZripnhJKiamBSJR8bZiHNKfPyXr1hdEnuOquxK0hjSyYSSIw/CZpF4/Vo7JgXu/8rDFwv8aLrRQSNJEsO7mA3bpr1qeX6lcicszIUzJGLspbRsCq2HEeeUAoFAIBAIBNWbcl9f8u2333LNNddgMpkYPHgwiqIwb948LrvsMjZv3swzzzxTrNfJzMxk0KBBrFmzhubNmzN69Gg2btzIhx9+yMyZM1m+fDkNGjSIPN7tdjNkyBCWLVtGbGws55xzDpIksXz5cp544glmzpzJvHnzcDqdkeesWbMGgO7du9O6des8n6FFixan+GsIBFWDy86x8N0/Ab5b4qdTY3uBj5u7MYjNbHRVgLHqc1xPM1OXBXj4m2xR79oBFh4aZ0WRJV6/1sHIbgH+WBfk1vMsNEsUuRECgUAgKD6KLPH0pXYuneRi/LtuTrh0BnUwcd/51qKfXEXo385ErRiJn1f4uWWohTdn+5i70egYrAoh1oJTw2GVuH6QhUmzfPy6KsDFhdhSevw6T35vnDON7Jq3W7RVPYVHx9l4eqqXrxb6qR0r0b+dcd41vKuZT+b5mbMuSLdm1bctICxI1KlhCAkmRSLaXnrLJhFqLRAIBAKBQFC9Kdcz26NHj3LTTTfhdDpZtGgR3bp1A2Dbtm0MHDiQ5557jrFjx0ZuL4ynnnqKNWvWMH78eD777DNMJhOapvHAAw/wxhtvcPvttzNjxozI41966SWWLVvGOeecw7Rp06hVqxYAx44dY8yYMaxYsYJnnnmGV155JfKctWvXAvD0008zatSosvwpBIIqRZv6Cl2aKPy2NsDD42yR1nkwVnFKkkRKpsa/e1SGdDTlssV45AIbQzqaUEPXkDWjJDo3yT2VnNvRzLkdxdWiQCAQCEpHx0YKV/Y18+3fAVrVlXn1GnuuleVVHZMiMa6HUUx+dIqXGasCdG6s8PLVBS8CEFQvruxn4bP5Pj76y8e4nmZMSt7xGVB17v3Sw797VK4fZOHslvmLUZedY+af7UH+2hDk4l7Zr9WugUzDmhJz1gV4ZJwVuRrtAznZe0wjMVbK1dkQ65CKJUj8sS7ArH8DTLrOjl+EWgsEAoFAIBCcFpSrZdN7772Hx+PhjjvuyCU6tGnThpdeegld13nzzTeLfJ2MjAw+/vhjHA4Hb775JiaTcRYqyzKvvvoqzZo149dff2X37t2R53zxxRcAfPTRRxExAqB27dq89957AHz33Xe53ifcIXHWWWeV7gsLBNWIy88x4wvAjFVGkKKu6zz5vYfOD2Qy7pUs7vzMg65n2zWFcVolBrQ3M7iD8edkMUIgEAgEgrLgvvNtPDDGyse3OoiyVb9C7IW9DGF+xqoAjWvJfHizvcrmXghKTpRN4tqBVg6k6Mz617DmUjWdI2kah1KNP09+72Xh5iBje5h5cIy1wNB1SZJ48Uo79462ct1Aa67bh3cxc6wa2zb5Ajo7j2i0O6kzKNYhcaIIQULXdd6c7eOvDUHW7FEjHRLCskkgEAgEAoGgelOugsTs2bMBGDduXJ77xo0bhyRJzJo1q8jXWbRoES6Xi/79+xMXF5frPkVROP/883O9X1ZWFs2bN6dz5860b98+z+u1atUKgCNHjkRu0zSNDRs2UK9ePerUqVO8LygQVGOGdzET65D4/h8j3Prz+X6mLgtQt4ZMhltnzX8q0XYY0F50OggEAoGg4nFaJW4811ptw5ab1lYYELJu+vRWB3FR1fN7CArm6n4Womzw+kwfl7/hovvDmQx6OotznzH+TF9pZGw9f4WtyO6GaLvELUOtxJyUkXJeZ+M8bMHmYLl9j/Jk+2GNoAYd8hMkigi1Xr9P5b9jht3Tkm3BSF6H6JAQCAQCgUAgqN6U2+mcruts2bIFgA4dOuS5Py4ujjp16nDkyBEOHTpE/fr1C3ytzZs3F/g6AO3atQNg48aNAERFRbFo0aICX2/lypUAud5z+/btuFwuzjrrLF544QW+//57du/eTVxcHKNGjeKpp54q9DMKBNUNm0Xigp5mvlzo57VffXy+wE+LOjLf3+skyiaR5dVRNapFeKhAIBAIBFWRd24wLJrEiu7TkxiHxA2Drbz1mw9/UKVrE4XmdRRModp7fJTE1f0tmPOxcyou7RrKxDoklm4Pcv/5ZfTBK5BN+43Ojg4NcwtyNZwSbh/4g3qB+8cvK4zOE6sZlmwN0rGR8cOK/UkgEAgEAoGgelNugkRaWhper5fo6OhcwdE5qVu3LkeOHOHo0aOFFvsPHz4ceXxBrwNGZkVRaJrGE088AcDFF18cuT1s17R48WJWrFjBgAEDaNCgAatXr+aTTz7h119/Zf78+RHxQyA4Hbj8HEOQ+Gy+nxpOiQ9uyrbFqI72GAKBQCAQVCVE4fT055ahFi7pbaZmtFSgJdOpoMgSvVsp/LE+SJpLI85ZvTptNh8wBImTw9xrhBa8ZLh1EmLy/m4ev85vawK0rS/TvI7MrH+DkXBsEWotEAgEAoFAUL0pkSBx1VVX8e+//xb5uJ49e/LCCy8A4HA4Cnyc3W6sGsvKyir09VwuV6GvVdzXAbjvvvtYvnw5iYmJPPLII5Hbw4HWPXr0YPr06dSrVy/y3jfddBPfffcdV1xxBevWrSvwYsPn8+Hz+XLdZrVasVqt+T5eIKhsmtRW6N/WxNLtQd6eYKdhQvW6yBUIBAKBQCCoTGRZyregXpb0aW1izrogK3aoDO9avc7VNh1QqVNDIiEm9+cOd+Cmu3USYvI+b+6GIFleuOBsMzF2iVn/Bpm/ybCtEpZNAoFAIBAIBNWbEp3O7du3j+3btxf5uDp16qAoxiqY4qwU0jSt0PuL+1qFvY6u69x///289dZb2Gw2fvzxx1xh1y+99BK33norNWvWzJVT4XQ6+fTTT1m8eDEbNmxg8eLFDBgwIN/3eOmll3jmmWdy3fbwww/z0EMPFfq5zwTS0tIq+yMICuB/4+CEC+rFZZCaWtmfpmwR405QGYhxJ6gsxNgTVAZi3JU/7RIBTMzf4KJn48Kvm6oS3gDsOqJwTmud1JNOMs1IgML+pHTiLHmf++M/MiZZom9zF6oGYGL1riAg4fNkiXEnqBTEuBNUFmLsCSoDMe4EpSE+Pr5YjyuRILFkyZJiPzYjIwMAj8dT4GPC90VFRRX6WuH7C3qtol7H5/MxYcIEpkyZgsPhYNq0afTv3z/XY8xmMy1atMj3+Q6Hg8GDBzN58mRWr15doCDx6KOPct999+W6TXRIZFPcQSmoWOKBBpX9IcoRMe4ElYEYd4LKQow9QWUgxl35Eh8PjRIyWbNPIT6+RmV/nGKz9r8gqu6mW3Mb8fG5r4fq1fIDXnQlmvj43B5Mh1M1Vv+XxZCOJpo1iAWgbf0sth4yxJiEuGji4nQx7gSVghh3gspCjD1BZSDGnaC8KLeG1+joaKKjo0lPT8fj8URslXJy5MgRoOBsiDDhfImkpKR87y/sdVJSUhg3bhxLliyhZs2azJw5k969e5fou4DR9QHgdrsLfIwQHwQCgUAgEAgEAkFZ07uViR+WBtifrNGomlhsbj5gCAgn50dAtmXTCbd+0nNU3vndh67DhWdnCxV925jYesgPiGwWgUAgEAgEgupOuZ3NSpJE+/btAdi6dWue+1NTU0lKSiIuLq7QQGuADh06ALBly5Z879+8eTMAHTt2zHX7oUOH6NOnD0uWLKF58+YsW7asQDHivvvu48ILL2Tfvn353r9nzx4AGjQ4ndeSCwQCgUAgEAgEgqpGn9bGOrKl24KV/EmKz6ZQoHWHhnkvOWOd2RkSAOv3Brnk9Swues3Fws1Bzmmj0K9t9tq5c3L8W2RICAQCgUAgEFRvynV5zYgRIwCYPn16nvumT5+OruuMHDmyyNfp168fTqeThQsXkp6enus+VVWZOXMmkiQxfPjwyO0nTpzg3HPPZceOHfTo0YNly5bRsmXLAt9jxYoVTJs2jWnTpuW5LykpiT///BNFURgyZEiRn1cgEAgEAoFAIBAIyoperUxIEizdUX0Eic37VerFS8RF5b3krBHukHDpaJrOo1O8bD+scUlvMz894OSz25yYlOxOiG5NFRyhrAmLOc/LCQQCgUAgEAiqEeUqSEyYMAGHw8GkSZNYunRp5Pbt27fz+OOPA/Dggw/mes6RI0fYtm1bxIYJjAyHG264gczMTG655Rb8fqNdV9f/v717j6qqzPsA/t2H6zkICoqpoELeUDGN1AZB8To54b3mVdO0Xm+Z19FJX31rbFKz0krJxvsoNr6NJWZlowyCivdEITXUJNAK76JigHDg/N4/iDMal7gd98Px+1mL5XLvfZ5n/876rrPg/PZ+tmDWrFlIS0vDoEGD0LJlS+trJk2ahLNnz6JNmzaIjY297wHWJXnppZcAAK+//joSExOt2+/cuYMXX3zR+m/jxo0r+W4QERERERFVXG2ThsDGBhw5l48Ci/z2C3TwP5ty8N8fZiEnT5CdK/j+igWBJSzXBAB17rlDYt/pfKResWBUmDPmDzOW+BpnRw1Ptiy8NcKFSzYRERER1Wg2veHV19cXERERGDduHLp164YePXrAxcUFsbGxuHv3LhYtWoT27dvf95o5c+YgMjISo0ePxoYNG6zb58+fj7i4OGzevBmHDx9Gp06dcOrUKZw5cwZ+fn748MMPrccmJyfj448/BgB4enpi4sSJpZ7jxo0bYTAYMHLkSOzatQsbN25E586dERISAi8vL8THx+PGjRsIDQ3F0qVLq/X9ISIiIiIiKo8urRxx8oc8JP9oQbumJX/Rr5erty34/KgZIsCfN+bghe7OsAhKbUh4GP/TkNiwJw+OBmBkN+cy53j5KRc0e8SAhp4abt6s9hKIiIiI6AGx+QqcY8aMga+vL9566y0cPnwYDg4OCAoKwsyZMzFkyJByj+Ph4YF9+/ZhwYIF2LJlC7788kv4+Pjg5ZdfxmuvvWZ96DQA7Ny5EyKFVw4dOHAABw4cKHXcDRs2wGAwQNM0REZGomfPnli1ahUSEhJgsVjQsmVLzJkzB1OnToWTE+8PJiIiIiKiBy8kwBGrYvIQ/Y1ZuYbEzsTCZkTzBgbEnszHmfRfnh/RpOTzdHTQUMsVSEwrwOVbgvAgRzSoU/bN++2aOKBdKeMRERERUc2hSdE390Q2lJGRAS8vL71Pgx4yzB3pgbkjvTB7pAfm7sGxWARPL8rCzZ8t2P26O0wu6ixd9F/v/YyUyxbs/as7pqzLxuFzhQ2Jw2+6W5dn+rVef72D9IzCP0U/nelWoWYDc0d6YO5IL8we6YG5I1uy6TMkiIiIiIiIqOoMBg0vhDnjdjaw7Wuz3qdj9cN1C05csKDPY05wN2r4YIwJrX0MaONrKLUZAfznORIdm/HOByIiIqKHCRsSRERERERENcDAzk6obdIQuScPFhs/3HpnkhkJ3+f/5nFfHStsjoQHFa4G7G7U8MlMN/zzT25lvq62qbAh8UL3sp8dQURERET2hQ0JIiIiIiKiGsDorGF4qBMuXLdgz7e/3SyorOOp+Zi+PgcvfpiNA2dKn0dE8NVxMzzdNAS3+s/jCZ0cNDg7lr2kVO92TujVzhE9Am3+WEMiIiIiUggbEkRERERERDXEc6HOcHIA1u/Jq5bxks7n4/Iti/X/5gLB65/chZMDYHQGJq/LRmJayU2J7y5akHLZgr4dHOHkULFnWjzX1RkfjjXBwaDOszCIiIiIyPbYkCAiIiIiIqoh6tc2IPwJJxxNKcCpHwuK7T97sQCZ2cWXczr1YwFu/2r7Z1/nYdj72Rj0ThZOXCgca+PePHx3yYJxvZ2x+iUTNAATVmXjTHrxubb/slxTvyecqqEyIiIiInoYsCFBRERERERUg7z4y3MXIr7KvW/7kXP5GPh2FvrM/xmRe3KRly84m16AMSuy8OySLPRb9DPikwubCDuTzPjf/7uL+rU13M0TjF6ehS2H8vDhjlw0qadhfG8XdPBzxPKxJuTkAc8tzcKnh/IgIrBYBOt35yJybx4aeWl43J8PpSYiIiKi8uGCnURERERERDVIKx8HDOjohC8SzIhPNqNbGyeYCwQLou7C2REwOQOLPsvF2tg8XL9TeFdE73aOOHwuH+NX5aBvBzNiT+ajrruGj6a4IeNnC15anY1X/3kXAPDas0a4OhcupdSllSPWTjRh9j9y8No/72L3qXxk5woOnytAU28D3htthIHLLhERERFROfEOCSIiIiIiohpmRn8XuDoBb2/LhblA8PG+PJy7ZMHYXs7Y8b+1MKOfCwAguIUDov7shuVjTfhidi10bu6AnUn5MLloWDfRhKbeBjzu74hN09zQ1NuAPwY7oWvr+69be7KFIz6fXQv9nnBE3Kl8HD5XgOEhTvjsFTe0bcy7I4iIiIio/DQRKb7AKFE1y8jIgJeXl96nQQ8Z5o70wNyRXpg90gNzp68PdtzFhzvz8PJTzti4Nw8eRg1fza0Fo3PpdyxYLIKvjuejbWMDHn3k/mZC0Z+Gmlb66/d8a4ars4bftdDvZnvmjvTA3JFemD3SA3NHtsQlm4iIiIiIiGqgMT1dsOWQGX+LzgMAvPmca5nNCAAwGDT071jyQ6jLakQU6d6WD7AmIiIiosrjkk1EREREREQ1kMlFw8z+hUszdWnlgD6P8XozIiIiIlIbf2MlIiIiIiKqofp3dIKmaQhu6VCuOxyIiIiIiPTEhgQREREREVENpWmlL8FERERERKQaLtlEREREREREREREREQ2x4YEERERERERERERERHZHBsSRERERERERERERERkc2xIEBERERERERERERGRzbEhQTaXm5uLiIgI5Obm6n0q9BBh7kgPzB3phdkjPTB3pAfmjvTA3JFemD3SA3NHtqaJiOh9EmTfMjMzUbt2bdy+fRseHh56nw49JJg70gNzR3ph9kgPzB3pgbkjPTB3pBdmj/TA3JGt8Q4JIiIiIiIiIiIiIiKyOTYkiIiIiIiIiIiIiIjI5tiQICIiIiIiIiIiIiIim2NDgmzOxcUF8+bNg4uLi96nQg8R5o70wNyRXpg90gNzR3pg7kgPzB3phdkjPTB3ZGt8qDUREREREREREREREdkc75AgIiIiIiIiIiIiIiKbY0OCiIiIiIiIiIiIiIhsjg0JIiIiIiIiIiIiIiKyOTYk6D4WiwWrV69GcHAwPDw84OrqilatWmH27Nm4detWseNTU1MxatQoNG3aFEajEa1bt8aiRYtgNpvLNd+YMWOgaRry8/NLPSYpKQmDBw9Go0aNYDKZ8Pjjj2PlypXg40/si4rZAwARQe/eveHr61uZskhhKmbu008/Rc+ePeHp6QlnZ2f4+/vj5ZdfRnp6emXLJAWpmL2oqCh069YN7u7uqFWrFjp27IiIiAgUFBRUtkxSjIq5u5eIoG/fvtA0Dbt27apIaaQw1XKXmJgITdNK/fnd735XlXJJEarlDgAyMjLwyiuvoEWLFnB1dUXdunUxaNAgJCYmVrZMUpBK2fPz8yvz867oZ8OGDVWsmvSmUu6KxMTEoE+fPqhTpw5cXFzQokULzJw5Ezdv3qxsmWRvhOgXBQUFMnjwYAEgJpNJunfvLuHh4eLt7S0ApHnz5nL58mXr8adOnRJPT08BIE8++aQMGTLEemyvXr3EbDaXOd+yZcsEgAAo9di4uDhxcXERg8EgYWFhMnDgQHF3dxcAMnr06Oosn3SkYvaKzJgxQwCIj49PtdRKalAxc9OmTRMA4uTkJCEhITJgwADx9fUVAOLt7S3JycnV+h6QPlTM3uLFiwWAODo6So8ePaRfv37i5eUlAKRPnz6Sl5dXre8BPXgq5u7XIiIirK+JiYmpUr2kBhVzt3btWgEgHTt2lBEjRhT7mTdvXnW+BaQDFXN3/vx58fPzEwDi5+cngwcPlrZt2woAcXV1lYSEhGp9D0gfqmVv+vTpJX7OjRgxQnr37m39u+Po0aPV/l7Qg6Na7kREVqxYIZqmiaZpEhoaKgMHDpQGDRoIAGnWrJlcuXKlWt8DqpnYkCCrol/QW7VqJWlpadbtmZmZ0r9/fwEgf/zjH63bg4KCBICsXr3auu327dvSrVs3ASDvvfdeifPk5+fL7NmzrR9ipX2Q3b17Vxo2bCgODg7yr3/9y7r94sWLEhAQIABk69at1VA56U217ImIZGVlyejRo63HsSFhX1TL3K5du6yNh6SkJOv23NxcmTBhggCQTp06VUPlpDfVsnfq1CkxGAzi4eEh33zzjXV7RkaGde6//e1v1VA56Um13P1acnKyGI1GNiTsjIq5mzRpkgCQ7du3V0+RpBwVc9ejRw8BIJMmTZL8/Hzr9jfffFMASPv27atWNClBxeyVpKCgQLp37y4AZMWKFRUvlJSiWu6uXbsmRqNRXFxcZPfu3dbtWVlZ0rdvXwEgEydOrHrhVOOxIUFWISEhpf6Cfu3aNdE0TZycnCQ7O1tiY2MFgAQHBxc7NiUlRTRNkyZNmojFYrlvX3x8vHTs2FEAyKOPPlrmB9m6desEgAwbNqzYvri4OAEgXbt2rULFpArVsrdt2zZp0aLFfceyIWFfVMvciBEjBIAsX7682L7c3Fzr1eopKSlVqJpUoFr25s6dKwBKvCp48+bNAkD69+9f+YJJCarl7l55eXkSFBQk9erVkzZt2rAhYUdUzF1wcLAAkEuXLlVPkaQc1XK3f/9+64Ulvx5HRKRDhw7SrFkzuXbtWhWqJhWolr3SLFy4UADIgAEDKl4kKUe13EVFRQkA+cMf/lBs38GDBwWAtGnTpgoVk73gMyTIytPTEwEBASWunVqvXj14enrCbDbj+vXr+OqrrwAAAwcOLHZss2bN8Nhjj+GHH37AyZMn79vXv39/JCQkYMSIETh69GiZ51M0x6BBg4rtCwsLg6enJ/bv31/imnhUs6iUvVu3bmHQoEFITU3FtGnTsH379ipWRypSKXMAYDKZ0LZtW4SGhhbb5+zsDD8/PwDAxYsXy1siKUq17L3xxhv47rvvMGXKlGL7fv75ZwCAo6NjuesjNamWu3vNmzcPx48fx8qVK+Ht7V3BykhlquXOYrHgxIkTaNSoERo0aFCFykhlquVuy5YtAIDp06dD07Ri+xMTE5GSkoJ69eqVu0ZSk2rZK8mPP/6IBQsWoFatWlixYkWFX0/qUS13BkPh18zp6enFnvt67do1AICXl1f5CyS7xYYEWX355Zc4ffo06tatW2zf999/j4yMDDg7O8Pb2xvffvstACAwMLDEsdq0aQMAxT7IwsPDcejQIfzjH//4zQ+hsuYwGAwICAiAiFiPo5pLpewZDAY899xzOHHiBJYuXQqj0VjZskhhKmUOAFavXo1Tp06hffv2xfbduXMHp0+fBgA+XN0OqJY9BwcHtGjRotj5nDlzBvPnzwcAjB49unzFkbJUy12R/fv34+2338bIkSPxzDPPVKQkqgFUy93Zs2eRlZWF5s2bY+HChWjXrh1MJhN8fHwwfvx4pKenV6ZMUoxquTt27BgAoHPnzrh58yZWrFiBCRMm8MInO6Ra9koya9Ys5OTkYO7cuWjUqFGFX0/qUS13Xbt2hZubG06cOIEJEybgwoULyMrKQnR0NCZPngxN0zBjxozKlEp2hpe8UbnMnTsXANCvXz+4urpar9Jt2LBhiccXbb9y5cp92zdt2lTuOSs7B9mXB509Dw+PCuWU7I8en3dlWbBgAXJychAUFAR/f/9qGZPUpEL2pk+fjiNHjuDIkSMwmUyIiIgo8Soqsh965e7OnTsYNWoUGjVqhA8++KCip001nB65O378OAAgPj4eR44cQVhYGHx9fZGQkIA1a9bgiy++QFxcnPULGbI/euQuJSUFAJCamorQ0ND7xoqIiMBTTz2FLVu2oFatWuUvhGocFX7HO336NDZv3oy6deti6tSplR6Hag49cle3bl188sknGDVqFNasWYM1a9ZY9/n4+CA6Ohp9+vSpUB1kn3iHBP2m999/H5988glMJhMWLlwIAMjKygJQuMxISYquKi9a7qEyHsQcpDa9skcPL9UyFxUVhSVLlsBgMGDJkiXVPj6pQ5Xs/f3vf8fhw4chIjAYDPjuu++QnZ1dbeOTWvTM3ZQpU3D+/HmsX78ederUqdJYVLPolbvExEQAQKdOnZCamoro6Gjs2LED58+fx/Dhw3HlyhUMHz682BITZB/0yt3t27cBAEOHDkXr1q2RkJCAzMxM7NmzB23atEF0dDTGjx9f6fFJfar8jrd06VKICKZOnQo3N7dqG5fUpGfuOnTogGeeeQaOjo4IDg5GeHg46tevj/T0dCxevBgZGRlVGp/sAxsSVKalS5dixowZ0DQN69atQ0BAAIDCJR4AlLgO5r0sFkul534Qc5C69MwePZxUy9ynn36K4cOHw2KxYNGiRejRo0e1jk/qUCV7IoLk5GRkZWVh3759aNmyJZYvX847JOyUnrmLiopCZGQkJk2ahN69e1d6HKp59MzdokWLcO7cOURHR9+3VImbmxvWrl0LHx8fnDhxAvHx8ZWeg9SkZ+5yc3MBAPXr18fOnTvxxBNPwN3dHWFhYYiOjobJZMLHH3+MM2fOVHoOUpcqv+PdunULH330EYxGIyZPnlwtY5K69MxdamoqOnfujG3btuHgwYM4ePAgtm/fjrS0NDz//POIiYnB4MGDKz0+2Q82JKhEIoJZs2bhT3/6ExwcHLB+/XoMGzbMur/oltKcnJwSX1+0vSq3nj6IOUg9KmSPHi4qZm7ZsmUYNmwYzGYz5s+fj1mzZlXb2KQO1bKnaRp8fX1hMpkQGhqKmJgYNGjQALt27cKePXuqZQ7Sn965u3TpEiZMmICWLVvinXfeqdQYVPPonTsAcHJyQvPmzeHp6Vlsn8lkQs+ePQEACQkJlZ6D1KJC7oquRB43bhxcXFzu2+fr64t+/foBAHbv3l3pOUg9KmTvXtu3b0dOTg769+/PBwrbMRVy9+qrryI9PR3vvPMOOnXqZN1uMpmwevVq+Pv7Iz4+Hvv27av0HGQf2JCgYnJycvDss89i8eLFMBqNiIqKKvZASx8fHwDA5cuXSxzj0qVLAEpfm648HsQcpBZVskcPD9UyV1BQgClTpmD69OnQNA0rVqzAq6++WuVxST2qZa8knp6e1i9KitZep5pNhdwtXLgQN27cQO3atTFu3DiMHDnS+nP69GkAwFtvvYWRI0fyj1U7oULuyqNBgwYAwGXq7IQqufP29gaAUp8D5ufnBwC4fv16pecgtaiSvXtt27YNAO77cprsiyq5i4uLAwA89dRTxfa5urpam/9FyyjSw4sNCbpPZmYmevXqha1bt8Lb2xu7d+8ucamGwMBAAEBycnKJ43z77bcAgHbt2lX6XMqaw2Kx4MyZM9A0DW3btq30HKQOlbJHDwfVMmc2m/Hss89i+fLlcHNzw2effYaXXnqpSmOSmlTK3rvvvothw4YhLS2txP1FV3OazeZKz0FqUCV3ResSHz16FJs2bbrv5+rVqwCA2NhYbNq0Cd9//32l5iB1qJI7AJgxYwaGDBmCCxculLg/NTUVQOFV61SzqZS7otemp6eXuL/oi8H69etXeg5Sh0rZK2KxWPDvf/8bRqMRTz/9dJXHI/WolLtbt24BABwdHUvcX7Q9Ly+v0nOQnRCiX+Tl5UloaKgAkGbNmklKSkqpx+7du1cASNeuXYvtS0lJEU3TpHHjxmKxWMqcE4AAELPZXGxfZGSkAJDnn3++2L7Y2FgBICEhIeWojFSnWvbulZaWJgDEx8enfMVQjaBi5oYOHSoAxNvbW44ePVqxgqjGUC17AwcOFACyYMGCEs81ICBAAEhMTEw5qiNVqZa70oSFhTFvdkS13HXp0kUAyPvvv19s36VLl8Td3V0cHBzkhx9++O3iSFmq5W7VqlUCQIKDg4vty8nJkcaNGwsASU5OLkd1pDLVslfk5MmTAkC6dOlS/mKoxlAtdx06dBAAEhkZWWyf2WyWVq1a8Xc9EhERNiTIau7cuQJAGjRoID/99FOZx1osFusHzbJly6zbb9++Ld26dSu2vTRlfZDduXNHGjVqJA4ODhIVFWXdfunSJWndurUAkM8//7wCFZKqVMvevdiQsE+qZW716tUCQEwmk3zzzTcVL4hqDNWyt2PHDmv2Dh06ZN2enZ0tY8aMEQASFBQkBQUFFaiSVKNa7krDhoR9US13GzduFABSu3ZtOX78uHV7Zmam9O3bVwDI2LFjK1AhqUi13GVmZoqPj48AkL/85S/WL/rMZrOMHz9eAMjvf//7ClZJKlIte0XWr18vAGTy5MnlL4ZqDNVyt3LlSgEgjzzyiCQlJVm35+bmysSJEwWABAYG8m8LEk1EpJSbJ+ghcuPGDTRp0gTZ2dlo37699Vaukrz77rt45JFHkJSUhLCwMGRmZiIoKAj+/v7Yt28frl69ivDwcGzbtq3U27SKaJoGoHApiJKO3blzJwYMGID8/HyEhISgXr16iIuLQ2ZmJiZMmICVK1dWrXDSnarZK3L+/Hn4+/vDx8cHP/30U+WKJKWolrn8/Hw0bdoUFy9eRPPmzfHkk0+WOsacOXO4TF0Nplr2irzyyitYsmQJDAYDQkJCULt2bSQkJODy5cvw9/dHXFycdY1rqnlUzV1Junfvjr179yImJga9e/cuf5GkHBVzJyJ44YUXsHHjRjg6OiIkJAReXl6Ij4/HjRs3EBoaip07d8LNza3qbwDpQsXcAcCBAwfw9NNPIzMzE82bN0dgYCCSkpJw/vx5NG3aFHv37kXTpk2rVjzpStXsAcC8efPwxhtvYP78+Xw2nZ1RMXcigtGjR+Ojjz6CwWBAaGgo6tSpg2PHjiE9PR0NGzZEXFwcAgICqv4GUM2mbz+EVBEVFWXtcv7Wz7lz56yvO3v2rAwdOlTq1asnRqNRAgMDZfHixXL37t1yzYtydPS//vprCQ8Plzp16kitWrUkKChI1q5dy46qnVA5eyK8Q8IeqZa5Y8eOlft8eNVwzaZa9u61detW6d69u7i7u4uLi4sEBATI3Llz5ebNm1Utm3Smcu5+jXdI2A+Vc7dhwwYJDg4WNzc3MRqN0r59e1myZInk5eVVuW7Sl8q5S0tLk7Fjx4qPj484OzuLn5+fTJs2Ta5evVrlukl/Kmev6Kr0Dz74oMp1klpUzt2mTZskLCxMPDw8xNnZWZo1aybTpk2Ty5cvV7lusg+8Q4KIiIiIiIiIiIiIiGzOoPcJEBERERERERERERGR/WNDgoiIiIiIiIiIiIiIbI4NCSIiIiIiIiIiIiIisjk2JIiIiIiIiIiIiIiIyObYkCAiIiIiIiIiIiIiIptjQ4KIiIiIiIiIiIiIiGyODQkiIiIiIiIiIiIiIrI5NiSIiIiIiIiIiIiIiMjm2JAgIiIiIiIiIiIiIiKbY0OCiIiIiIiIiIiIiIhsjg0JIiIiIiIiIiIiIiKyOTYkiIiIiIiIiIiIiIjI5tiQICIiIiIiIiIiIiIim/t/q0/mYdgVVREAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dlt = DLT(\n",
-    "    response_col=response_col,\n",
-    "    date_col=date_col,\n",
-    "    estimator='stan-map',\n",
-    "    seed=8888,\n",
-    "    seasonality=52,\n",
-    "    regressor_col=['trend.unemploy', 'trend.filling'],\n",
-    "    regressor_beta_prior=[0.1, 0.3],\n",
-    "    regressor_sigma_prior=[0.5, 2.0],\n",
-    "    # for prediction uncertainty\n",
-    "    n_bootstrap_draws=1000,\n",
-    ")\n",
-    "\n",
-    "dlt.fit(df)\n",
-    "predicted_df = dlt.predict(df, decompose=True)\n",
-    "_ = plot_predicted_components(predicted_df, date_col)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There are much more configurations on regression such as the regressors sign and penalty type.  They will be discussed in subsequent sections."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### High Dimensional and  Fourier Series Regression\n",
-    "\n",
-    "In case of high dimensional regression, users can consider fixing the smoothness with a relatively small levels smoothing values e.g. setting `level_sm_input=0.01`. This is particularly useful in modeling higher frequency time-series such as daily and hourly data using regression on Fourier series. Check out the `examples/` folder for more details."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "orbit39-cmdstan",
-   "language": "python",
-   "name": "orbit39-cmdstan"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/dlt_daily_forecast.ipynb b/examples/dlt_daily_forecast.ipynb
index 1d88e4fe..122a433d 100644
--- a/examples/dlt_daily_forecast.ipynb
+++ b/examples/dlt_daily_forecast.ipynb
@@ -326,14 +326,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "20:27:33 - cmdstanpy - INFO - Requested 8 parallel_chains but only 4 required, will run all chains in parallel.\n",
-      "20:27:33 - cmdstanpy - INFO - CmdStan start processing\n"
+      "2024-01-21 15:15:10 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d2109c0ca4e240d888184d225729b319",
+       "model_id": "1152778b968344989ac80b7311dec392",
        "version_major": 2,
        "version_minor": 0
       },
@@ -347,7 +346,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "32d6ad32f9284fa1ab7fb8981ebacbd7",
+       "model_id": "7e8045a4479f4144971bf52e32d79a63",
        "version_major": 2,
        "version_minor": 0
       },
@@ -361,7 +360,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c0b9e9dd1c8840b99c9b0845fef6bf59",
+       "model_id": "8e945e61f54744b9b21c8a6ade0abfef",
        "version_major": 2,
        "version_minor": 0
       },
@@ -375,7 +374,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "771d911e612a43fbaa18970bdd58869b",
+       "model_id": "d07ac33947dd452c93743da9a24321f9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -390,29 +389,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                                                                                                                                                                                                                                                                                                                                "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "20:27:59 - cmdstanpy - INFO - CmdStan done processing.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "CPU times: user 715 ms, sys: 70.1 ms, total: 785 ms\n",
-      "Wall time: 26 s\n"
+      "                                                                                                                                                                                                                                                                                                                                \n",
+      "CPU times: user 477 ms, sys: 51.7 ms, total: 529 ms\n",
+      "Wall time: 22 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x2a41f59d0>"
+       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x2a5ebf9d0>"
       ]
      },
      "execution_count": 6,
@@ -442,7 +427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-04-27T18:04:05.483850Z",
@@ -452,7 +437,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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p07FixQp06dIFixYtwqRJk2z3t2rVCl988QXy8/OxevVqLF26FDk5OZgxYwZmz57tchxQb1566SVkZWVh6dKl2LJlC2JjY3Httddi6dKluOKKK2zb3bt3L/Lz87F27VosXboU2dnZeOihh/Dcc88hLS0t8BeSiIiIiIiIFIsFMJFVrwdiNN6XIwuVcDczCknKYDDg3LlzaNu2bUBhVSTU19cjISEh0rsRVe6//368++67qK6uDmqYAbm3jRLf76FSXl6OjIyMSO8G+YBtJV9sG+VgW8kX20Y52FbKwbaSF7aHcrCtAvPjKRPSElXIbBW60SmjqW04hicREREREREREXl09OhRHD16NNK70WKxS7t/2KWdiIiIiIiIiIg82rp1KwAgNzc3wnvSMtXrI70HysIKTyIiIiIiIiIiIpkymQUaDKzw9AcDT6IwWrp0KYQQQY3fSUREREREREQtB6s7/cfAk4iIiIiIiIiISKZqOX6n3/wKPFUqlU//7dy50+Fxv/zyC8aPH4+OHTsiISEBF198MebMmQODweByOzqdDvPmzUOvXr2QlJSEdu3a4Z577kFRUZHbfdu1axduuukmXHDBBUhJScG1116LDz/80O3yZ86cwbRp05Cbm4uEhAR06dIFTz31FKqrq/15SYiIiIiIiIiIiEKGExb5z69Ji8aNG+f2vqKiInz55ZdIS0tDly5dbLd///336N+/PyoqKnD11Vfjqquuwu7duzFr1ixs27YNW7ZsQUxM024YjUbcfvvt2LRpE7KzszF8+HD88ssvWLZsGdatW4fPP/8cl112mcO2ly1bhnvvvRcxMTG4/vrrodFosG3bNtx11134/vvv8cILLzgsf/r0afTr1w8nTpxAr169MGLECBQWFuKll17C5s2b8fnnnyM1NdWfl4aIiIiIiIiIKGplZGREehdarDpdpPdAefwKPD/44AOXt9fV1aFPnz5QqVRYvnw5LrzwQtt948ePR0VFBQoKCjBx4kQAQFVVFW655RZs27YNCxYswPTp023Lv/HGG9i0aROGDBmCtWvXIjExEQDw6quvYvr06bjvvvtw4MABqFQqAJZKzYkTJyIpKQm7du3ClVdeCQA4cuQIBg0ahBdffBG33Xab7XYAmDp1Kk6cOIFZs2bhb3/7GwBAr9fj3nvvxYcffojZs2fjtdde8+elISIiIiIiIiKKWmPGjIn0LrRY9XpWePpLkjE8H3vsMRw5cgSPPPIIfv/739tu3759Ow4cOIB+/frZwk4ASE1Nxdtvvw2VSoVXX30VQlgaTgiBefPmAQBef/11W9hp3caAAQPwzTffYMeOHbbbFy5ciPr6ekybNs0h1OzRowfmzJkDIQReffVV2+1Hjx7FmjVr0KFDB4fKz7i4OBQUFCA1NRVLlixBTU2NFC8NERERERERERFRwNil3X9BB56FhYV488030aFDB/z97393uG/jxo0AgNtuu83pcV27dsVll12GX3/9FYcOHQIAfPfdd/j111/Ro0cPdO/e3ekxo0aNAgBs2LDBaRsjR450Wn7kyJFQqVQOy2/evBlCCIwYMcKhKz0ApKWlYfDgwaivr8f27dt9efpERERERERERFFv37592LdvX6R3o8XRGwWM5kjvhfIEHXg++uijEELgn//8J5KSkhzu+/777wEAl156qcvH9uzZEwBsgae/ywshcPjwYbePSU9PR2ZmJioqKlBcXBzQNoiIiIiIiIiIWrrCwkIUFhZGejdaHM7QHpigAs8tW7Zg7969uPjii3H33Xc73X/q1CkAQFZWlsvHW28/c+ZMQMtXVFSgoaEBKSkpTmGrVNtwRafToaqqyuE/nY4jyBIRERERERERkXQ4YVFg/Jq0qLn58+cDAJ5++mnbJEL2amtrAcBhLE57CQkJAGAbL1Pq5aXYhitz5sxxmvn9qaeewsyZM90+xmg0QqfTob6+Hkaj0e1yclJfXx/pXZBEhw4d0LNnT2zduhUA8NBDD+GDDz7A2bNnkZyc7Ne6fvnlF3Tp0sX278TERIwcORLLly+XdJ+9kXvbGAwG6HQ6VFRUOA0d0dJUVFREehfIR2wr+WLbKAfbSr7YNsrBtlIOtpW8sD1Cz5qnlJeXB7UetpV/Tp8xoarS0qe9IkmDOLMk0/G4JIe2ycjIkGQ9AScRP/74Iz799FO0b98eY8eOdbmMRqMBAJdhqD2z2RzS5YPZhivPPPMMZsyY4XCbVquFVqt1+xiDwQCj0YiEhATExsZ63V+5sAbASqdWq23PZcqUKRg6dCjS09N9DuMMBgOGDh2KCy+8EEuXLrXd/v7776Njx44ReZ3k3DYxMTHQarVIT09X1Ps9VKT6wqbQY1vJF9tGOdhW8sW2UQ62lXKwreSF7RFa1p61UrzObCvfnaw2IdVkyajS0zXIaBW6wBOInrYJOPD86KOPIITA2LFj3YZG1uo5d9Vo1tuty0m9vBTbcMVbuEny1q9fP/Tr18+vx+h0OuzYsQP33Xefw+333HOPlLtGRERERERERGTDGdoDE3AsvGbNGgBwOXanVU5ODgCgpKTE5f2nT58G0DRupr/Lp6SkICUlBZWVlW4DzGC3QURERERERETU0nXt2hVdu3aN9G60KGazQL2egWcgAgo8z549i/3796Nz587o3bu32+WsM6FbZ1Jvzjpjeq9evQJaXqVS4ZJLLgEA/PDDD07Ll5eXo6SkBOnp6bag099tUPAGDRqEa665Bp9++ikuv/xyJCQkoEePHnjjjTdsy+Tn50OlUmHLli3o0KEDEhMT8eyzzwIA9Ho9XnjhBVx00UXQarVo3749/vSnP+H8+fMO2zGbzZg7dy66deuGhIQEXHXVVdi+fbvT/tx///1QqVQO47SWlpZi6tSptm1fcskleP311yGEwPHjx5GSkgIAePfdd6FSqWzd2lUqFe644w6H9W/duhWDBg1CUlISkpOTMXDgQNv4oc2f74kTJzB69GikpaUhJSUFt912G44ePRrwa01EREREREQUCsOGDcOwYcMivRstisEEMO4MTECB5759+wDAa7fg4cOHA2iqBrVXVFSEQ4cOoUOHDrZwsXv37ujSpQu+++47FBUVOT1m9erVAIDf//73Pm1jzZo1EEI4LD9s2DCoVCps2LABJpPJYfnKykrs2LEDiYmJGDhwoMfnRv45duwYbr31VvTp0wdz585Fu3btMHXqVKfJn8aNG4eJEyciPz8fQ4cOhdlsxq233oq//e1vuOGGG/Cvf/0Ld9xxBwoKCjBgwADboMkAMHXqVMycORM9evTAvHnzcMUVV2DEiBGoqqryuG+VlZXo27cvlixZglGjRmH+/Pno0aMHHnnkETz//PNo27Yt3nzzTQBA//798f7772PAgAEu1/Xmm29i2LBhOHfuHJ5//nnMnj0bZ86cwfDhw/HOO+84LT9w4ECYTCb885//xMSJE7Fx40bcdttt/r68RERERERERBRlTO6nlyEvAhrD8+uvvwYAXHnllR6X69+/P6644grs3r0b//rXv/CnP/0JAFBVVYUHHngAQgg88cQTDhMITZs2DTNmzMCECROwfv16W2Xda6+9ht27d6N3794YMmSIbfkHHngA//znP/HKK69g2LBhuPbaawFYJlWyVgg++eSTtuU7duyIW265BevWrcPMmTPx8ssvQ6VSQa/XY9KkSaiursb06dORlpYWyEsTlNEv16C0Sj7ZvRACKpVlRvk2qSqsfMK/Gc3tnT17Fi+++CL+/Oc/AwAmT56MQYMGYc6cOXj44Ydty02ePBnPPfec7d/vvfcetm7dig8//BB/+MMfbLffcsstGDJkCObPn48///nPOHz4MBYvXoz777/fIVjs1asXHnvsMY/79tJLL+HYsWPYtGmTLUCfNGkShg8fjnnz5mHmzJm466678OCDD6JLly5ux+2sqKjA9OnT0bNnTxQWFiIxMRGAJYjt06cP/vSnP2HkyJFIT0+3PebGG2/EkiVLbP+uq6vD4sWLcfDgQY/V00REREREREThtGXLFgBglWcYmeUTESlOQIHnsWPHAADt2rXzuJxKpcI777yDgQMH4tFHH8W7776Lzp07Y/fu3Th79ixGjBiBKVOmODzmkUcewYYNG7B9+3bk5uaif//+OHbsGA4cOID09HR88MEHDsu3b98e//rXvzBx4kQMGDAAgwcPhlarxbZt29DQ0IA5c+bg8ssvd3jM66+/jv379+OVV17Bpk2bcOmll2Lfvn349ddf0adPH/zlL38J5GUJWmmVwJlKub2bpdmfhIQEh5ntY2Ji8Nhjj+EPf/gDNm/ebLt90KBBDo/7+OOPkZSUhEGDBqG0tNR2++WXX4727dtj3bp1+POf/4xNmzZBCOEQngLAww8/jFmzZnnct3Xr1qFbt262sNNqyZIlqK2tRUJCgseJsaw+/fRT1NTUYMaMGbawE7BMgPXEE0/goYcewieffIK77rrLdp/93wBsIae7MWaJiIiIiIiIIsFVT1wKLVZ4Bi6gwPPcuXMAgFatWnld9oorrkBhYSGee+45bNu2DT/88AO6du2KJ598Eo888ojTDO8xMTHYuHEj5s6diw8++AAbNmzABRdcgHvuuQf5+fkuB8idMGEC2rdvj3/84x/48ssvodFocOWVV+Lxxx/H7bff7rR8hw4dsG/fPjz//PPYuHEj1q9fj44dO2LWrFl46qmnPM7QHkptUlXeFwojS4WnZZ+C3bfOnTs7hIAA0K1bNwDAL7/8YtvOBRdc4LDM0aNHUVtb63S7lV6vB9AUwufm5jrcHxcX53VQ5WPHjmHw4MFOt7dv397j41ytBwB69OjhdF/Pnj0dlrFq/ry0Wi0AOA23QEREREREREQti5klngELKPDctGmTX8t369YN//73v31ePj4+HrNnz8bs2bN9fszQoUMxdOhQn5fPzs526EosB8F0GQ+F+vp6JCQkSLKu2NhYp9usoV5MTIztb41G47RM+/btXY5/ab9ea2DqqhJTCM9fEEaj0WFYhUBZt+Nqe9bnZw00rdTqgIbRJSIiIiIiIqIox7wzcExbKCyOHz/uVLX4888/AwAuuugit4/r1KkTysrKMGDAAAwZMsThv6qqKlvVqLWK88cff3R4vNFoxPHjxz3uW8eOHV3OjP7f//4X9957r9M63encuTMA4MiRI073WW/r0KGDT+siIiIiIiIiopaNXdoDx8CTwqKyshJLly61/dtoNGL+/PlITk52GjvT3siRI1FfX4+XX37Z4fZPPvkEo0ePtlXpjhw5EjExMXjppZdgNjd9I7z55puoqanxuG8333wzfvjhB+zcudPh9ldffRUffvghsrKybJWnnrqa33jjjUhMTMT8+fNRV1dnu722thavvPIKEhIScNNNN3ncFyIiIiIiIiIigBWewQioSzuRvzQaDaZNm4b//e9/uOiii7BixQp8+eWX+L//+z+kpaW5fdyECROwbNkyPPvsszh06BAGDhyI48ePY+HChcjKyrLN+t65c2c8++yzeOGFFzBkyBCMHj0aP/zwA9566y2vY80+88wzWLlyJYYPH45p06YhNzcXW7ZswZo1azB37lykpqYCAFJSUrB9+3YUFBSgf//+uPjiix3Wk5GRgblz52Lq1KnIy8vD/fffDyEEli5dip9++gmLFi2yrYuIiIiIiIhISfLy8iK9Cy0OKzwDxwpPCotWrVph7dq1+OSTT/Dkk09Cp9Ph448/xuTJkz0+Li4uDp988gmeffZZFBYW4tFHH8UHH3yAUaNGYc+ePbjwwgtty+bn56OgoABnzpzB448/jh07duA///kPOnbs6HEbbdq0wd69ezF27Fi89957mD59Oo4fP4733nsPTzzxhG25V199FUIIPPLII1i9erXLdU2ZMgVr165Feno68vPz8be//Q1ZWVnYvHkzHnroIT9eMSIiIiIiIiL56Nu3L/r27Rvp3WhRzAw8A6YS3mZ0IUkYDAacO3cObdu2dTmBjxxJNWnRoEGD8N1336G0tFSCvSJA2gmlQkGJ7/dQKS8vR0ZGRqR3g3zAtpIvto1ysK3ki22jHGwr5WBbyQvbQznYVr47ftaEX842pZ4922uQ2Sp0tYvR1Das8CQiIiIiIiIiIo9WrFiBFStWRHo3WhSO4Rk4juFJREREREREREQelZeXR3oXWhyO4Rk4VngSERERERERERH5qaYhtCWYrPAMHCs8KeR27twZ6V0gIiIiIiIiIpKU3iigMwDaWFVI1s9JiwLHCk8iIiIiIiIiIiI/mcxAdX3oyjBNrPAMGANPIiIiIiIiIiIiP5nNoe3WzgrPwLFLOxEREREREREReTR06NBI74LsmMxAjS6U62eJZ6AYeBIRERERERERkUe5ubmR3gXZMQugJoRd2lnhGTh2aSciIiIiIiIiIvKTWQD1egFjiAbb5BiegWPgSUREREREREREHi1cuBALFy6M9G7IitksIADUNIRo/Qw8A8bAk4iIiIiIiIiIyE/WQDJUExexS3vgGHgSERERERERERH5ydQYSNaGKPA0MfAMGANPCouSkhLU1taGdBtFRUUhXT8RERERERERkZW1wrM6ZBWe7NMeKAaeFHKrV69G9+7dce7cuZBt49lnn8VNN90UsvUTEREREREREdkz21V4CiF9OMlJiwLHwJNCbs+ePaiqqgrpNrZu3QqTyRTSbRARERERERERWVkDSZMA6vTSrpvVncGJifQOEBERERERERGRvI0ZMybSuyA7wm6MzZp6gSStSrJ1s7ozOKzwpJC6//77MW/ePABA586d0alTJ9t9p0+fxoMPPojMzExotVr07NkT8+fPh7nZNGTr1q3DNddcg9TUVKSkpOB3v/sd1q5da7u/U6dO2L9/P06cOAGVSoX777/f4/506tQJS5cuRdu2bZGamopFixYBACorKzFjxgxceOGF0Gq16NKlC2bPng2dTuewji+++AKDBw9G69atkZiYiD59+uDtt992WKZTp064++67sXz5cnTr1g0JCQno3bs3PvzwQ6d9+vHHH3HXXXehTZs2iI+PR69evfCvf/3LoRx+586dUKlU2LRpEx5//HF06dIF8fHx6Nu3Lz755BOH9RUXF+Ouu+5CTk4OtFotLrroIsyaNQv19fUOy/3000+488470bp1ayQkJODKK6/E+++/7/a1IyIiIiIiopYrIyMDGRkZkd4NWTHZnbdLPVM7Z2gPDis8o5GxBji5CmgoAeIzgfa3AzHJEdmVSZMm4dy5c9i0aRPmz59vCzxLSkrQt29f1NfXY8qUKcjKysL27dsxY8YMHDp0yBYgfv755xg9ejSuueYa/OMf/4AQAkuWLMGoUaPw6aef4oYbbsCrr76KGTNmoLq6GvPnz0fXrl097tOZM2fw5JNP4qmnnkJtbS0GDRqE2tpaDBw4EEePHsXkyZORm5uLr7/+Gn//+9/x1VdfYfPmzdBoNPj5558xbNgwdOzYEc899xzi4+OxYsUKTJgwAQDwwAMP2LbzxRdfYNWqVZgyZQo6d+6Md955B3fddRfq6upsoew333yDAQMGICYmBlOmTEG7du2wbt06PProozh48CDeeecdh32fMmUKWrVqhccffxwmkwlz587FzTffjGPHjiEnJwdGoxHDhw/HqVOn8OijjyIrKwt79+7FnDlzcPLkSbz33nsAgO+//x7XXXcd0tLS8PjjjyMlJQXr1q3D+PHjcezYMTz33HNSND8RERERERFFifLycgBg6GnHPpSUOvDkDO1BEhQWer1eFBcXC71eH9oNGaqF+Gq8EDtvFGLnTZb/fzXecruf6urqJNmlxx9/XAAQx44ds932xz/+USQnJ4uioiKHZf/85z8LAGL37t1CCCGmTJkiAIhz587ZliktLRUXXXSRePnll2239enTR3Ts2NHrvtx3330CgCgoKHC4/YUXXhBqtVp88cUXDre/+eabAoB4//33hRBCvPTSSwKAKCwstC2j0+nElVdeKWbMmGG7rWPHjg6PE0KI2tpa0aVLF9G2bVvb++C6664TcXFx4scff7QtZzabxbhx4wQA8cknnwghhNixY4cAIHr27Cl0Op2tbVasWCEAiFdeeUUIIcS+ffsEADF37lyH5/HHP/5RDBgwQJhMJiGEEIMHDxY5OTmivLzcYbl7771XaDQah7YKRNje7wpQVlYW6V0gH7Gt5IttoxxsK/li2ygH20o52FbywvYIvddff128/vrrQa8nmtqq8KhBbDukF9sO6cXuHwySrru63mxbt/W/0xUmSbfRXDS1Dbu0R5uTq4D60wDMAEyW/9efttwuE2azGatWrUK/fv2QmpqK0tJS23+jRo0CYOnGDgDt27cHAEybNg379++HEAKtW7fGTz/9hMcffzzgfRg0aJDDvz/++GNcdNFFuOiiixz2Z/jw4YiNjXXan6effhq7d++GyWRCXFwc9u/fb+u6b9WpUyeMGzfO9u/ExEQ8/PDDOHfuHL744gucPXsWe/bswZ133olu3brZllOpVHj++ecBAKtWObbbqFGjEBcXZ/t37969AVgqZgEgOzsbarUab7zxBlauXIna2loAwNtvv41du3ZBrVajrKwMO3fuxNChQ2EymRye7+jRo2EymbBp06aAX1siIiIiIiKilsC+ClNvFNAbpavyZIVncNilPdo0lABoPkiuqvF2eSgtLUVlZSU+/fRTtG3b1uUyJ06cAAA88sgj+Oyzz/Cf//wH//nPf5CZmYlhw4bh3nvvxfXXXx/wPlxwwQUO/z569Cjq6+u97s8f/vAHrF+/HitWrMC2bduQnp6Om266CXfffTduu+02qFRNr33Pnj0d/g3AFmz+8ssviI+PBwD06NHDaXtdu3ZFXFwcjh075nG/tVotANhmqM/JycG8efMwc+ZM3HHHHdBqtejfvz9GjRqF+++/H4mJiSgqKoIQAm+//bbT2KPNny8RERERERERuSaa5Zt1OiBOoqSNs7QHh4FntInPBND8QyEab5cHazg3YsQIPPbYYy6XsQZ7ycnJ2LZtG77++musXbsWW7duxXvvvYelS5fi2WefxV//+teA9kGj0Tjt01VXXYU5c+a4XD4lJQUAEBMTg+XLl2P27NlYvXo1tm7dilWrVuE///kPxo0bhw8++MD2mNjYWKf1WJ97TEyMbVIi0fwbsvE2IYQt0LRSq70XZT/22GMYM2YM1qxZgy1btmD79u347LPPsGDBAhQWFtr2YcKECbj77rtdruPCCy/0uh0iIiIiIiKilqx5FaZZCDgXoQWGeWdwGHhGm/a3A2e3NXZrVwEQQEKW5XaZaNu2LZKSklBfX48hQ4Y43FdVVYWtW7eic+fOAIBjx47h1KlTuO6663DVVVfhxRdfxG+//YYbbrgB8+bNw1/+8hefQkBvOnXqhIqKCqf9MZlM+Oijj5CZaQmMT506hSNHjuD666/HxRdfjFmzZqGsrAwjR47EsmXLMHfuXGRlZQEAioqKnLbz888/AwAuuugi23M8cuSI03JFRUUwGAzo0KGDX8+jsrIS3377LS677DJMmjQJkyZNgl6vx8yZM/Haa69h/fr1tu78Qgin51tcXIx9+/YhKSnJr+0SERERERERtTTNQ0kpu6GzS3twOIZntIlJBq5cCHS8B2h3g+X/Vy6M2CztQFM1pX1144gRI7Bjxw7s2bPHYdl//OMfuPPOO/HVV18BAGbNmoUhQ4bg9OnTtmU6dOiAnJwcaDQaW5dxjUZjW38gRo4ciaKiIixfvtzh9rfeegtjxoyxjeG5YMEC3HDDDfjmm29sy7Ru3Rq5ubkOzxUAvvvuO2zbts3275qaGrzxxhu48MIL0bdvX7Rr1w5XX301PvroI/z0008O27VWrt52221+PY8vv/wSAwcOxNKlS223xcXF2cb61Gg0yMrKQt++fbFixQqnUPbJJ5/E7bffzi7tRERERERERF4073ZuljCkZIVncFjhGY1ikoFO4yO9Fzbt2rUDYAkzr7/+eowZMwb//Oc/sWPHDtx44414+OGH0b17d+zZswfvvfceBg0ahD/84Q8AgBkzZmDVqlUYMGAAHnroIbRq1Qrbt2/Hzp078dRTT9kCz3bt2uHrr7/G3Llz0adPH7/H93zmmWewevVqjB8/Hrt27UKfPn1w6NAhLF68GJdccgmmTp0KAJg0aRIWL16M4cOHY8qUKcjKysL+/fvx3nvv4a677nIYY1Or1eK2227Do48+irZt2+Ktt95CcXExVq9ebatKXbBgAQYNGoRrrrkGU6dORbt27bB+/Xp88sknGDt2LG688Ua/nscNN9yAvLw8PP300zh27Bh69eqF3377DQsWLECXLl1w8803AwBef/11DBo0CHl5eZg6dSrat2+PTZs2Yd26dRg3bhyuvfZav7ZLRERERERE0c16XkxNTKzwlK8IzhDfouj1elFcXCz0en2kd8VndXV1kqyntLRUDBkyRGi1WtG6dWvR0NAghBDi+PHj4r777hPt2rUTWq1W5ObmimeffVZUV1c7PH7Xrl1iyJAhom3btkKr1YpLL71UvPrqq8JkMtmW2bdvn+jZs6eIjY0Vw4cPd7sv9913nwDgtA3rfk6bNk106NBBxMXFiU6dOompU6eKM2fOOCz37bffipEjR4qsrCwRFxcncnNzxfPPPy/q6+tty3Ts2FH06dNHvP/++6Jjx44iMTFR9O/fX2zfvt1pu4cOHRKjRo0S6enpIj4+Xlx++eXi9ddfF2az2bbMjh07BACxYMECIURT2xw7dkwAEI8//rht2bNnz4qpU6eKTp06Ca1WKzIzM8UDDzwgiouLHbb7v//9T9x+++0iIyNDxMfHi549e4q5c+dK8h5V4vs9VMrKyiK9C+QjtpV8sW2Ug20lX2wb5WBbKQfbSl7YHsoRLW1lMpnFtkN6h/9+KzV5f6CPfj1nclr/6Qrp1u9KtLSNEEKohHAxYwpJzmAw4Ny5c2jbtq3LyWzkqL6+HgkJCZHeDUXq1KkT2rRpg6+//jok65d72yjx/R4q5eXlyMjIiPRukA/YVvLFtlEOtpV8sW2Ug22lHGwreWF7hN7Ro0cBwDakW6Cipa0MJoHdPxgdbsvN1ODCNtKMHnn8rAm/nHUs8+zZXoPMVqEbnTJa2gZgl3YiIiIiIiIiIvJi69atAIIPPKOFq/E6TRIOvMkxPIPDSYuIiIiIiIiIiIj84CqQ5KRF8sHAk4iIiIiIiIiIyA+uJhXipEXywS7tRCFw/PjxSO8CEREREREREYWIqxlxpKzKlLJatCVihScREREREREREZEfQl7hyS7tQWGFJxEREREREREReRQts3dLxeyixFPSMTxZ4RkUBp5EREREREREROTRmDFjIr0LATGbBaobgMo6gax0FWI1KknW66qa01UIGvj6WeIZDAaeREREREREREQUdUqrzPj+N5Ote3jb1BjEaqRZt6s8Uspu6BJmpy0Sx/AMM8F3LLUAfJ8TERERERFFl3379mHfvn2R3g2/6I2OIWSoJxWSshu6kV3ag8LAM0zUastLbTKZIrwnRKFnfZ9b3/dERERERESkbIWFhSgsLIz0bvilecAp6Ribrio8Q7x+8h3TiDDRaDSIjY1FXV0dq98oqgkhUFdXh9jYWGg0EvUVICIiIiIiIvJT8wBSSRWenLQoOBzDM4ySk5NRUVGB8vJyJCYmQqPRQKWSZrDcUDAYDIiJ4VtEjuTYNkIImEwm1NXVQafTIT09PdK7RERERERERC1Y84l/zGYBQKpJi5zTUynH8JSyWrQlkldiEuUSEhIAADU1NaioqIjw3nin0+mg1WojvRvkgpzbJjY2Funp6bb3OxEREREREVEkNO9gK2mFp4t1Sdmh18w+7UFh4BlmCQkJSEhIgMlkglnm9ckVFRWs0pMpubaNWq1mN3YiIiIiIiKShZB2aXc5hqd0G5CyWrQlYuAZIRqNRvbBUExMDGJjYyO9G+QC24aIiIiIiIjCqWvXrpHeBb+FctIiV13OzcIy3FuwwxeyujN4DDyJiIiIiIiIiMijYcOGRXoX/OYUeEqYI7rrvm4yAzFB1rexujN4nKWdiIiIiIiIiIiiTvMqTCknAnK3LilCVZmPgKgIDDyJiIiIiIiIiMijLVu2YMuWLZHeDb807xou7aRCrm+XIlTlDO3BY5d2IiIiIiIiIiLyqKioKNK74Lfm1ZZSTipkdpOeSlLhyS7tQWOFJxERERERERERRZ3mVZhSBonuxtmUojs6KzyDx8CTiIiIiIiIiIiiTvNQUsou7SKEXdrdVY+S7xh4EhERERERERFR1GlebSnppEXuKjwlCCs5aVHwGHgSEREREREREVHUad6FnZMWtRyctIiIiIiIiIiIiDzKy8uL9C74rXlw6K4qMxDuxgOVojqTkxYFj4EnERERERERERF51Ldv30jvgt9Es5JOSSctYoWnrLFLOxERERERERERRZ3mwaG7iYYCYXaTnkoRqnIMz+Ax8CQiIiIiIiIiIo9WrFiBFStWRHo3fGY2CzTPHk0SDuLptku7BJswsU970NilnYiIiIiIiIiIPCovL4/0LvjFVWYoVY7oKky1kiKsZN4ZvIArPE+ePInJkyejY8eO0Gq1yMzMxD333IOioiKnZc+cOYNp06YhNzcXCQkJ6NKlC5566ilUV1e7XLfZbMbbb7+Nq666CqmpqWjTpg1GjhyJ/fv3u92fb775BqNGjUJ2djYSExPRu3dvLFq0yGm8Bquqqir8+c9/Ro8ePZCQkID27dvj4YcfxtmzZwN7QYiIiIiIiIiISBZchYZSdWn3FEhy0iJ5CCjwPHjwIC6//HIsXrwYSUlJGDFiBJKSkrBs2TJcc801OHHihG3Z06dP4+qrr8bChQuRmJiIESNGwGQy4aWXXsJ1112Hqqoqp/U//PDDmDBhAn755RcMGTIE3bt3x9q1a9GvXz988sknTsvv2LED11xzDdatW4du3brhpptuQlFRER5++GH88Y9/dFq+uroagwcPxt/+9jcYjUbcfPPNSE5OxqJFi3DllVfi5MmTgbwsREREREREREQkA64m/pFqlnZPgSQnLZIHvwNPvV6PsWPHory8HP/4xz9w+PBhrFq1Cj/99BOmTJmC0tJSPProo7blp06dihMnTmDWrFn43//+h48//hg///wz7rzzThw6dAizZ892WP+6detQUFCAXr164eeff8aqVauwZ88efPzxxzCbzbj//vtRV1dnW16n02HcuHEwGo3YsGEDdu7ciTVr1uDHH39Ejx498O6772L16tUO23j++edx4MABjB8/HkeOHMFHH32Ew4cPY/r06SguLsbUqVP9fVmIiIiIiIiIiEgmQtul3b/tSrl+8o3fgedHH32EI0eOYPTo0Xjqqadst2s0GsydOxcXXnghjh8/DpPJhKNHj2LNmjXo0KEDXnjhBduycXFxKCgoQGpqKpYsWYKamhrbfS+//DIAYO7cuWjdurXt9tGjR2PcuHE4ffo0/v3vf9tuX7ZsGU6fPo0//OEPGD58uO32rKwsvPHGGwCA+fPn226vqqpCQUEBEhMT8eqrryImxjKMqVqtxty5c9GlSxesW7fOZdd8IiIiIiIiIiKSP1ehoVRBoqdKUUkqPNmlPWh+B54ff/wxAGDGjBlO9yUmJuLEiRP45ptvoNFosHnzZgghMGLECFuwaJWWlobBgwejvr4e27dvBwBUVlZiz549SE5Oxg033OC0/lGjRgEANmzYYLtt48aNAICRI0c6LT9w4ECkp6fj888/x/nz5wEAu3btQm1tLQYMGID09HSH5TUaDW655RaH9RIRERERERERtXRDhw7F0KFDI70bPnMVPIalwlOKMTxZ4Rk0vwPP/fv3Q61W46qrrsLp06cxb948PPTQQ3jiiSewa9cuh2W///57AMCll17qcl09e/YEABw6dAgA8MMPP8BsNqNHjx5OAamr5b1tQ61Wo0ePHhBC2Jbzd5+IiIiIiIiIiFq63Nxc5ObmRno3fOZqEms381r7zVMVp0mCjZil2tEWzDlV9ECn0+G3335DmzZtsHHjRtx3330OM63PmzcP9913H958803ExMTg1KlTACzdy12x3n7mzBkA8Hv5QB4TyDaa0+l00Ol0DrdptVpotVq3jyEiIiIiIiIiovBwOWmRRCWersJUK47hKQ9+BZ7WGdVramowZswY3HLLLXjhhRfQvn17/Pe//8XkyZPx7rvvIjs7G3//+99RW1sLwNLV3ZWEhATb+gD4vLx1OX8e4+827McVbW7OnDkOY5ICwFNPPYWZM2e6fYwSVVRURHoXyA22jXKwrZSDbSVfbBvlYFvJF9tGOdhWysG2khe2R+i9+eabAIAHH3wwqPWEq61Kq8yoqjQ53V5eHhv0usvcrBsAhE6F8jS/4jYn5eVG1Oick9OKJA3izH531vaZHD5HGRkZkqzH7wpPAGhoaMCgQYPw0Ucf2e67+eabsWbNGvTt2xfz58/HzJkzodFoAAAqlcrjes2N0bU/ywshoFKpoNFoYDabQ7INd5555hmnMUyjtcJTqjcaSY9toxxsK+VgW8kX20Y52FbyxbZRDraVcrCt5IXtEVpJSUkApHmdres4U2lGRrIKsRrPGU0g9GozUmucQ8lWrWKgVge3PaPG9boBIEmrQkZGcIFncpkRaheBZ3q6BhmtQhd4AtHzOfLrVbKvipw6darT/VdddRXy8vLQ0NCAvXv3Ijk5GQBQX1/vcn3W263L+bp8UlKSLbAM1Tasy7mi1WqRmprq8F80hp1ERERERERERKFSUy9w6FcTzFLNJmTH3TibUsyA7ml3OWmRPPgVeKalpSEuLg4A0LlzZ5fLdOrUCQBQWlqKnJwcAEBJSYnLZU+fPg2gadxMf5cP5DGBbIOIiIiIiIiIiKRlFsD5WoEfiqVP+NyFhlLMB+QpkJQiUPU0KRL5xq/AU6PR4OKLLwYAFBcXu1zGGiRecMEFtpnQDx8+7HJZ64zpvXr1AgBcfPHFUKvVOHLkiMsu5c2XB+BxG2azGUeOHIFKpcIll1zidXl32yAiIiIiIiIiImlZo58zlWb8csZ1F/GA1+0meJQiTPRc4SnBLO0hqHhtafzu+P/73/8eAPDvf//b6b6zZ89i//790Gq1uPrqqzFs2DCoVCps2LABJpPjG7eyshI7duxAYmIiBg4cCAC2vysrK7Fz506n9a9evdphHwBg+PDhAIA1a9Y4Lb9z505UVFTg2muvRatWrQAA/fv3R1JSEnbu3InKykqH5U0mE9avXw+VSoVhw4b59oIQEREREREREZHf7KshT5yTtqzR3YzsUmSJnkJTSWZpZ94ZNL8Dz8mTJyM5ORkrVqzAW2+9Zbu9trYWDz74IGpra3HfffehVatW6NixI2655RYcO3YMM2fOhGisG9br9Zg0aRKqq6sxadIkpKWl2dbzyCOPAACmTZvm0O181apVWL58ObKysnDvvffabr/99tuRnZ2N5cuXY9WqVbbbS0pKMG3aNABwmD09MTEREyZMsG1br9cDAIQQmDlzJo4dO4aRI0eiW7du/r40RERERERERERRacyYMRgzZoyk67QP9gRgy42kXrc9Sbq0e1iJWQT3PMxmAeadwVOJAFph1apVuPvuu2EwGNCrVy907twZ+/btQ0lJCS6//HLs2rXLFmL+9ttv6NevH4qLi9GjRw9ceuml2LdvH3799Vf06dMHO3fudJogaPz48Xj//feRmpqK66+/HqWlpdizZw/i4uKwZcsWDBo0yGH5LVu24NZbb4XRaMR1112HNm3aYPv27aiqqsKkSZOwaNEih+Wrqqpw3XXX4bvvvkPHjh2Rl5eH7777DkeOHEGnTp3wxRdfcAxPAOXl5VEzO1e0YdsoB9tKOdhW8sW2UQ62lXyxbZSDbaUcbCt5YXsoh31b/e+ECaXVTeWSAy6OQYxEM7b/dMqEk+XOpZh9usQgLTG4bRSVmHCi1H2ZZzDPw2AS2P2D0eV9PdtrkBnCWdqj6XMU0Kt0++23Y//+/bjrrrtw5swZbN26FSkpKXjuueewZ88eh4rNDh06YN++fXjwwQdRWVmJ9evXIz4+HrNmzcL27dtdzoa+dOlSLFiwAJ06dcKWLVtw9OhR3Hrrrfjyyy+dwk4AGDZsGPbs2YPf//73+O677/DZZ58hNzcXb775Jt544w2n5VNTU7F79248/vjjAID169dDr9djypQp2Lt3L8NOIiIiIiIiIiI75eXlKC8vl3SdzSslpezK7W5dnqozfeVtHNBgngdnaJdGQBWe1DJEU7Ifbdg2ysG2Ug62lXyxbZSDbSVfbBvlYFspB9tKXtgeobdw4UIAwNSpU4Naj31bHfjFiPN1TbFUv24xSIiTpsLz+99MOFPpnB5e3lGD1inBVUkeKTbhVIX7ZDKY51GnE/jyZ1Z4Bit0rxIREREREREREZEbzSslpZhB3cpdpWQ4JhUK5nlwwiJpMPAkIiIiIiIiIqKwax7uSdkH2V3X9VDP0g4E9zzYpV0aDDyJiIiIiIiIiCjsmod7UlZ4mtyN4SnBNrwFmsE8DxNHnpQEA08iIiIiIiIiIgq75tWWUkwoZCVC2KXd5GUlwTwPVnhKg4EnERERERERERGFXfPckBWe0r4GLVlMpHeAiIiIiIiIiIjkLdjZ2V1pXikpZXVjKCct8hZKBvM8OGmRNFjhSUREREREREREYRfSCk+3gWfwiaK76lFv2/Zp3azwlAQDTyIiIiIiIiIi8ujo0aM4evSoZOszuyhllLK60d26pKgi9baOYJ4Hx/CUBru0ExERERERERGRR1u3bgUA5ObmSrI+V1WSkgaeblYmxTa8rSO4Ck/2aZcCA08iIiIiIiIiarFqamqwfPlynDp1CtnZ2Rg7diySk5MjvVtRz1Ulo5RhX2grPEM4SzvzTkkw8CQiIiIiIiKiFqmmpgbjxo1DYWEhhBBQqVTYuHEjli1bxtAzxFwFe1J15zabBdzlhuGo8AzmeXAMT2lwDE8iIiIiIiIiapGWL1+OwsJCtG7dGjk5OWjdujUKCwuxfPnySO9a1HMV7EkV9nmaVCjYwFMIEdIu7UZT4I+lJgw8iYiIiIiIiKhFOnXqFIQQ0Gq1AACtVgshBE6dOhXhPYt+rnp9S9Wd21OP8mCrSH3Zx2CeBwNPabBLOxERERERERG1SNnZ2VCpVNDpdNBqtdDpdFCpVMjOzo70rslORkaGpOsLaYWnh/UEM76mt3X7s4w7Rg7iKQkGnkRERERERETUIo0dOxYbN250GMMzLy8PY8eOjfSuyc6YMWMkXZ+r4FGqrM9T4Oipu7svfKkQDaaKlBWe0mDgSUREREREREQtUnJyMpYtW8ZZ2iPAIRQ0NQCle2EuOwWY1UD724GYwNvAUxGnCEOXdlMQVaQMPKXBwJOIiIiIiIiIWqzk5GQ89NBDkd4N2du3bx8AoG/fvpKsz1ZpaWoAihYB+nKY4moA/Aic3QZcuTDg0NNkhi1EhaECiE0H2vQDNPHBV3j6MoZnUF3aA38sNWHgSUREREREREREHhUWFgKQLvC0FUGW7gX05QCss5+bgfrTwMlVQKfxAa3bbKixhaiACoAAKr8Fuk6GWSQEtd++jM8Z3KRFHMNTCpylnYiIiIiIiIiIwsoWHBoqYAklAZOwxlQqoKEk4HWbizfZQlTAbPm/vhwo3StBl3bvgWSgkxYZTAKMO6XBwJOIiIiIiIiIiMLK1u07Nh1ojPmEUDXeKID4zIDXbWo4B2uI2kQFGCpkPWmRieN3SoaBJxERERERERERhZWt23ebfkBcBgAVTCIWgBpIyLJMXBTouuPaAU61kgKITfc4oZFP6/Zp0qLA1s3xO6XDMTyJiIiIiIiIiCisTNbkUBMPdJ1smaXdVAZ07BX0LO3mdsOAn487juEZlwG06de03UDX7VOFZ2DbMHD8Tskw8CQiIiIiIiIiIo+6du0q6focKi018UC7wTCpAHSKDXrdZnWSLURtPkt7kHmnT+NzBjqGp5Fd2iXDwJOIiIiIiIiIiDwaNmyYpOtzFQqaBSCEgErVfPxNf9ctbCGqK2azgFod2DZ8CUwFAnseDDylwzE8iYiIiIiIiIgorNwFh8FWYPqyjmC24etjA6nyZOApHQaeRERERERERETk0ZYtW7BlyxbJ1ucuOAy0O7gv6/b1fk98HQM0kG1wDE/psEs7ERERERERURjV1NRg+fLlOHXqFLKzszF27FgkJwc+QQtROBQVFUm6PnfBpi+TAgW6bim24ess74EEt1KEvWTBwJOIiIiIiIgoTGpqajBu3DgUFhbaxvjbuHEjli1bxtCTWhR3waEURY7eQsngKjylXc4eu7RLh4EnERERKQ4rY4iISKmWL1+OwsJCtG7dGlqtFjqdDoWFhVi+fDkeeuihSO8eUdi46xoelgrPYMbw9HH/AnkeDDylw8CTiIiIFIWVMUREpGSnTp2CEAJarRYAoNVqIYTAqVOnIrxnROHltsJTijE8Q9il3dewNKAxPKWYsYkAcNIiIiIiUhj7ypicnBy0bt3aVhlDREQkd9nZ2VCpVNDpdAAAnU4HlUqF7OzsCO8ZUXi5HcPT10EyPa3byzq83e/5sb4tF8jzYIWndFjhSURERIrCyhgiIlKysWPHYuPGjQ49FfLy8jB27NhI7xqRR3l5eZKuz11wKEWXdm+FksFkqiYfE0+O4RlZDDyJiIhIUewrY6xjn7EyhoiIlCI5ORnLli3jWNSkOH379pV0fcJNICjFpEXeQtNgus37GkoGNoYnu7RLhYEnUZTgBB5E1FKwMoaIiJQuOTmZExRRixfJCs9ghso0+Bh4+huqCiFglOC5kwUDT6IowAk8iKglYWUMERERUfitWLECADBmzBhJ1hfJSYuC6dLua+Dpb6jKsFNaDDyJooD9BB7W7p3WCTx45ZiIohErY4iIiIhCx1UPwvLyckm3YXKTCEoxUblTaGpqAEr3AoYKIDYdpgv6AUgJaN2+djv3N7jl+J3SYuBJFAU4gQcREREREdnjkFcUKHc9CAcOHGg75wyWEMJtsGmWIPF0WIWpAShaBOjLAagACIjvtgAD/gLE+PeZMJoEfN07d4Gu+3X7tTh5wcCTKApwAg8iIiIiIrLikFcUDHc9CBMTE/G73/1Okm14ygKlqPB0CE1L9zaGnaLxP8BUXwqcXAV0Gu/Xev0JJf3tNs8Ji6SljvQOEFHwxo4di7y8PJSVlaG4uBhlZWWcwIOIiIiIqIWyD6xycnLQunVr25BXRN6460FYWVkp2TY8hZpSjOHpkB0aKmCp7LTfvhpoKPF7vf6Ms+nv8/B1bFDyDSs8iaIAJ/AgIiIiIiIrDnlFwXDXgzAtLU2ybXiaVCjYWdqdusTHpgPNOqILAIjP9HvdBj+qMP2etIiBp6QYeBJFCU7gQUREREREAIe8ouCMHTsWGzdudBgSIS8vD08//TSSkpIk2Yan6sdge3Y7Pb5NP6DyW4cxPE1x7YD2t/u9bn9CSb8nLeIs7ZJi4ElEREREREQURdwFVhzyinwRjh6EoezS7lQhqokHuk52mKXd3K2f3xMWASEOPDmGp6QYeBIREZHicSZaIiL/8HszunHIKwpWqHsQeuq27u9kP07rdvV4TTzQbnDTMqrAprTxJ/DUGzlLeyQx8CQiIiJF40y0RET+4fdmy8Ahr0hqCxcuBABMnTo16HV5rvAMLvH0pbIy0E34M4anzuDfutmlXVqcpZ2IiIgUjTPREhH5h9+bRKFXU1ODgoIC5Ofno6CgADU1NZHeJVkxCwGYGoAzO4CTqyz/NzVY7pO6S3uAy7jiz0zq/oSjACs8pcYKTyIiIlI0zkRLROQffm8ShRarqL0z6WuBokUOEwmh8lug62SYREJQ6/aletMcYL95f0JJs7CEnrEalY/r5hieUmKFJxERESma/Uy0ADgTLRGRF/zeJAotVlF7Zz61uTHsFADMlv/ry4HSvcFXePoQZgbapd3fUFJv9H1Zf6pHyTtWeBIREZGicSZaIiL/8HuTKLQTd7GK2jtzwznYKjttVIChIuhZ2n15fKATI/nb7VxvFEjS+lrhGcAOkVsMPImIiEjROBMtEZF/+L1JLV2ou5zbV1FrtVpWUbtgjrsAwMlmtwogNj3g6kvbun14fKChqsHPx+n9mLiIXdqlxcCTiIiIFI8z0RIR+YffmySlUFZLhoJ9l3NrIGntci7F5yJaq6jHjBkj2brM7YYBR487juEZlwG06QdzkImnT5MWha3C07flhBBg3iktBp5ERFFAaQeZRERERBQdlDhBT6i7nEdrFXVGRoZk6zKpEoGuk4HSvYChAohNB9r0AzTxQQd/vlRvBjpOqP9jePq2PMfvlB4DTyIihVPiQSYRERERRYdQV0uGQji6nEdjFXV5eTkAaYJPswCgiQfaDXZ5v8ksoFH7NvZlc76MzxlIhafRJPx+nK8Vnhy/U3qcpZ2ISOE4CyQRERERRYoSJ+gZO3Ys8vLyUFZWhuLiYpSVlUVFl/NQW7FiBVasWCHJurwFh8H0ajf58ODAAk9PG20AzuwATq6y/N/UAMD3Ck8GntJjhScRUYiFuru5Eg8yiYiIiCg6KHGCnmjtcq4k3rqUm80ANAGu25cKzwAST6O7fTY1AEWLHMcjrfwW6DoZemOCj+vmAJ5SY+BJRBRCNTU1eOSRR0La3VyJB5nkH47RSkRERHKl1Al6wt3lnMdzjrzle4HOou7rYwPJFw3uxu8s3dsYdorG/2D5d+le6LXX+7hu//eHPGPgSUQUQitXrgz5mEZKPcgk33CMViIiIpIzVkt6x+M5Z95CyWAKHn15rIClylPtxzihbrudGypgq+y0UQGGCp+7tJsYeEqOgScRUQiVlJSEvLs5DzKjmxInAiAiImI1W8sSjRP0SInHc85CWeHp6wzsZuHfxDZuA8/YdDiGnbD8OzYdApZxPONiPAerrPCUHgNPIqIQyszMDEt3cx5kRi+O0UpERErDajYiRzyec+ZtDE3L/YHN0u5rdai/VaRuA882/SxjdtqP4RmXYbkdlpna47ykb75MtET+YeBJRBRCo0ePxs6dO9ndnALGMVqJiEhporGazVXFaksXzipepVcMR8vx3NSpUyVblywqPP3chtsxPDXxQNfJlrE8DRWWis82/Sy3AzAYvYe3rPCUHgNPIqIQYndzChbHaCUiIqWJtmo2dxWrCxYsQEZGRqR3LyLCWcUbDRXDPJ5z5nWW9iAKHk3Ctwf7uw2PoaQmHmg32OVdeqP3dbutHqWA+TNcgc3q1auhUqnc/nf33Xc7LP/LL79g/Pjx6NixIxISEnDxxRdjzpw5MBgMLtev0+kwb9489OrVC0lJSWjXrh3uueceFBUVud2nXbt24aabbsIFF1yAlJQUXHvttfjwww/dLn/mzBlMmzYNubm5SEhIQJcuXfDUU0+huro6kJeEiMgta3fz/Px8PPTQQ4o5MCN5sIbm+fn5mDRpEvLz8xV1gE9ERC2PfTUbAMVWs1nZV6zm5OSgdevWKCwsxMqVKyO9axHj7jVZvny5orcVKtFyPHf06FEcPXpUknW5K5a0CmrSIj/G8PRHoKGkb4Enu7RLLaAKzwMHDgAABg4ciPbt2zvd369fP9vf33//Pfr374+KigpcffXVuOqqq7B7927MmjUL27Ztw5YtWxAT07QbRqMRt99+OzZt2oTs7GwMHz4cv/zyC5YtW4Z169bh888/x2WXXeawvWXLluHee+9FTEwMrr/+emg0Gmzbtg133XUXvv/+e7zwwgsOy58+fRr9+vXDiRMn0KtXL4wYMQKFhYV46aWXsHnzZnz++edITU0N5KUhIopKSu9GpHQco5WIiJQk2qrZ3FWslpSURHjPIiecVbzRUjEcDcdzW7duBQDk5uYGva6Qdmn3dQxPP7cRaCjpy0ztrPCUXkCB58GDBwEACxcuxCWXXOJx2fHjx6OiogIFBQWYOHEiAKCqqgq33HILtm3bhgULFmD69Om25d944w1s2rQJQ4YMwdq1a5GYmAgAePXVVzF9+nTcd999OHDgAFQqy/gHZ86cwcSJE5GUlIRdu3bhyiuvBAAcOXIEgwYNwosvvojbbrvNdjtgGXfixIkTmDVrFv72t78BAPR6Pe699158+OGHmD17Nl577bVAXhoioqgTDd2IiIiIKHyibUgfd+MvZmZmRnrXIiacY1JGy/iX5Mhrl/YgAk9fw0M5VXgagni+5FpAXdoPHDiAxMRE9OjRw+Ny27dvx4EDB9CvXz9b2AkAqampePvtt6FSqfDqq69CNI6vIITAvHnzAACvv/66LewEgMceewwDBgzAN998gx07dthuX7hwIerr6zFt2jSHULNHjx6YM2cOhBB49dVXbbcfPXoUa9asQYcOHRwqP+Pi4lBQUIDU1FQsWbIENTU1gbw0RERRJxq6EREREVF4RdOQPmPHjkVeXh7KyspQXFyMsrIy5OXlYfTo0ZHetYhx95qEooo3nNui8PFe4Rl4F29fJwAy+zjWp229AYaSOlZ4RoTfFZ5nzpzB6dOnce2110Kj0XhcduPGjQCA2267zem+rl274rLLLsO3336LQ4cO4bLLLsN3332HX3/9FT169ED37t2dHjNq1Cj897//xYYNG3D99dc7bGPkyJFOy48cORITJkzAhg0bbLdt3rwZQgiMGDHCoSs9AKSlpWHw4MFYu3Yttm/fjltvvdXzi0FE1AJESzcipeEwAkRERPLgrmJVr9dHetciJpxVvNFWMUwWZi+BZqB5pxDC/WzqTvvg37o5hqey+B14WsfvbN++PZ588kmsW7cOJ06cQFZWFkaPHo1nn30W6enpACzjdwLApZde6nJdPXv2dAg8fVkeAA4dOgTA8kY+fPiw28ekp6cjMzMTp0+fRnFxMXJycnzaxtq1a3Ho0CEGnkREYDeiSAj3MAIMV4mIiDxzNf5ieXl5hPZGHsI5JmU0jH9JjrwFmoEGnr5WdwLeJ05qLvAxPD3fX1FjDmqSJnIt4MDzww8/RGpqKgYMGID27dvj66+/xrx587Bu3Tr897//RWZmpq36Jysry+W6rLefOXMGAPxevqKiAg0NDUhJSUFSUpLbx5w+fRpnzpxBTk6O39sgImrpom3iASWwH0bAGjJbhxGQ+mCfY7QSERERkS8yMjIkWY8QImSTFvkTePrTo91k9r7P7hiMwnac7crJch9WbGoASvcCDT8DbVKA9rcDMTxW98TvwNM6YdHvf/97LF++HGlpaQCAc+fO4e6778b27dsxceJErF+/HrW1tQDgMBanvYSEBACwjZcp9fJSbMMVnU4HnU7ncJtWq7V19yQiiibsRhR+4RxGIJzhqpKxCja6sX2JiIi8GzNmjCTr8SU4DHTSIoMP3ccD2UYwY2wKWILYOBcJnM4gUFrlZUdMDUDRIkBfDuiPA7VlwNltwJULGXp64HfguWzZMvz1r3/FhRde6BAatm3bFu+//z66deuGDRs24Pjx47YxPt2l2FbmxndZqJYPZhuuzJkzx2HCIwB46qmnMHPmTK/7oSQVFRWR3gVyQ25tU1NTg5UrV6KkpASZmZkYPXo0TxQbya2tgnHHHXfY/tbr9VHXjUtObZWWlgYhBGpra20hpBACaWlpkr/uRUVFMJlM0Gg0MBqN0Gg0MJlMKCoqkk0bR7ptampqMGnSJBw8eNB2dX716tVYvHgxv+uaiXRbBaKltK8S26alYlspB9tKXtgeylFaVoGqSs/JpFaoUZ7oed4Yl+uuMqOq0rd0sixeg3gf5/KuaRBe99mTkrMxSI53zqGOnzWhstJL4Hl2N1BeDECgQl2POKMOqPoV+O494MK7A94nV+TwOZKqktjvwFOr1bqdnT07OxtXXnkldu/ejf3799sOEuvr610ub73dupzUy0uxDVeeeeYZzJgxw+G2aK3wlOqNRtKTS9vU1NTgkUcecegOu3PnTnaHtSOXtiLv5NJWEydOxM6dOx0+V1dffTUmTpwo+eeqa9eutpDTGq5qNBp07dpVNq8HENm2+fjjj/Htt9+ibdu2ttfo22+/xZYtW1gF64Kc3je+aEntq7S2acnYVsrBtpIXtkdo7du3DwDQt2/foNajMwikGlI9LpOSokZGhv+BZwPMSK3xLfBMa6VBRoZvgaeq1ozUtMDLPJNTNchIdtyWEAI/nDUhNc1LyWu1CUhOBGBGeooWGYmxADRAfA0Qgvd8tHyO/A48vcnMzAQA1NXVIScnBwcPHkRJSYnLZU+fPg2gadzMnJwcAPB5+ZSUFKSkpKCyshL19fW27uhSbsOVaA03iQLB7rBE0gvnMAIco9W7cA4xQOHH9iUiIvJNYWEhgOADT1/G5zT7M8CmHX/G8PQ2U7y9YLq0A64nLjpbJaAz+rAPsemwdIy3J4D4zOB2Ksr5FXg2NDTgT3/6E86dO4fly5e7DBh/+eUXAJZZ3C+99FJs2LABhw8fxrBhw5yWtc6Y3qtXLwBNM6dbZ173trxKpcIll1yCL7/8Ej/88AOuvPJKh+XLy8tRUlKC9PR0W9Dp7zaIyDOeKLpmHQ+uqKgIXbt25Xhw5LdwzUbKMVq9y87Ohkqlgk6ns13YUalUyM7OjvSukQTYvkREROEVyjE8/ZlJ3Z9JiEIReBaX+/gk2/QDKr+1jOEJteW/hCzLxEXkll+BZ3x8PDZu3IhTp05h69atGDlypMP9//vf//DNN98gLS0N11xzDTQaDf7xj39gzZo1Tl3Ai4qKcOjQIXTo0MEWLnbv3h1dunTBd999ZwsJ7K1evRqAZcIkq+HDh+PLL7/EmjVrnALPNWvWQAjhsPywYcOgUqmwYcMGzJ8/3zamJwBUVlZix44dSExMxMCBA/15aYhaLJ4oOrOf9do6NiJnvSY5C1e4qlSsgo1ubF8icoWTmRGFji9hZjhmadcZfF82+MDTMV2taRA4X+tj4qqJB7pOtszSnsZZ2n3ld5f2yZMn47nnnsNjjz2Gyy+/HJ07dwYAnDlzBn/84x9hMpnw5JNPIiEhAf3798cVV1yB3bt341//+hf+9Kc/AQCqqqrwwAMPQAiBJ554wmECoWnTpmHGjBmYMGEC1q9fj5SUFADAa6+9ht27d6N3794YMmSIbfkHHngA//znP/HKK69g2LBhuPbaawEAP/74I5599lkAwJNPPmlbvmPHjrjllluwbt06zJw5Ey+//DJUKhX0ej0mTZqE6upqTJ8+3Tb7PMkfD0YiiyeKzuy7+VvHRmQ3fyLlCkcVLH/LIodVzkTKFMrvTfuL19bjW168JpKOL93V/SjUdODPLO1lNb5vxBDoDjVqXuF54pwvqW+DJeQ0VFi6tbfpB3QcArTybdzRls7vwHPmzJn473//i88++wyXXHIJfve730Gr1WLnzp2oqanBHXfcgaeffhqApcv5O++8g4EDB+LRRx/Fu+++i86dO2P37t04e/YsRowYgSlTpjis/5FHHsGGDRuwfft25Obmon///jh27BgOHDiA9PR0fPDBBw7Lt2/fHv/6178wceJEDBgwAIMHD4ZWq8W2bdvQ0NCAOXPm4PLLL3d4zOuvv479+/fjlVdewaZNm3DppZdi3759+PXXX9GnTx/85S9/8fdloQjhwUjk8UTRmX03f6PRyG7+RArk6kQ6VBcs+FsWeaxyJlKWUH9vcox6otDyZXjOQLu0uwsmzULgs2+NqKoXuLlPLOLjVNAbBarqBFITnWdPb07KCs+KGjPOeJuZ3dQAFC1q7MauAiAs3dpzJgNICW5nWgi/Y2GtVovNmzdj/vz56NGjBz7//HPs3LkTl1xyCd588018+OGHDt3Er7jiChQWFuKuu+7Cr7/+ik2bNuGCCy7A3LlzsXLlSsTEOGauMTEx2LhxI/7yl7+gVatW2LBhA86dO4d77rkHhYWF6Nmzp9M+TZgwAZs3b0b//v3x5Zdf4vPPP8eVV16JlStX2sJXex06dMC+ffvw4IMPorKyEuvXr0d8fDxmzZqF7du38+RCQewPRnJyctC6dWvbwQiFj/VEMT8/Hw899FCL/wzZd/MHwG7+RApjPZHOz8/H4sWLkZ+fj3HjxqGmpiYk2+NvGRGRf0L9vckx6olc69q1q9PQg4Hwpbt6oF3a9W6Cyb0/mrCm0Ijt35mw72jTQqXVvm3In67yLverscLTbBb48bQP2yzd2xh2CgBmy//15cDpzcHtSAsS0CztMTExeOyxx/DYY4/5tHy3bt3w73//2+f1x8fHY/bs2Zg9e7bPjxk6dCiGDh3q8/LZ2dlYsmSJz8uTPPFghOTIvpu/dQzPlt7NP9LYXZj8Ee7KHv6WERH5J9Tfmxyjnsg1V5NRB8K3SYsCnKXdRZd2vVFg44GmATsr7MbOLK0W6NLO+3r9mQzJFWvg+WupGXU6H9ZlqICtstNGBejOBbUfLUlAgSeRP0IZNPBghOTIvpu/Umdpj6aAkN2FyV/hDiD5W+ZdNH0nKRXbgOQk1N+bHKOeKLR8CjwDzBddBZM7vzfifG3Tvw123ctrGgR0BgFtrOdu7S67tLsaY1MT7/LxBqNAnU74NnYnYFkfmj8XAWjb+vZ4YuBJoRXqoIEHIyRX1m7+5eXlyMjIiPTu+CXaAkKOw+Uawwv3wh1A8rfMs2j7TlIitgHJTai/N6NxjHr+7pMUtmzZAiD4Sk9rd3WzEDhXKdAmVQWN2jFwFLBUearV3sfXtDKYhFNEWKcT2PqNY9mnrlkVaGm1QE6G5+0YmueULsbY/K34JE4l3YEru8YjNsb5+fxw0uT7ZExt+lnG7LQfwzMuA8ga7uMKiIEnhVSog4ZoPBgh8kUoD1qjLSBkd2FnDC88C3cAyd8yz6LtO0mJ2AYkN+H43oymycz4u09SKSoqkmZFjaHfvz834PMjJlzWUY1JN8ZBpXIMCU3Cv4lnXHVn3/qNEfV6x9uaz5heWmVGTobnLTlVeDqMsSlwoKwH3vppJASAKp0RN14W67SOyno/ylY18UDXyTCc+RIaUznUcY0VpDFJvq+jhWPgSSEVjqAhmg5GiHwR6oPWaAsI2V3YGcMLzyIRQPK3zL1o+05SIrYByRG/N33H332SG1Njf/XvfrOkiP87YcaXP5nQr7tjRGU2A9A0f7R7zScWqqgR2Pm9Jd20Hw1Tb3AMHitqBUxm4VRlas+pq7zdGJvfVXTFOz/fBtEYzxaXBTfep9XJ83GY/+nVUKuA6y+NwaB0Rnj+4KtFIcWggUh6oT5ojbbPLbsLO2N44R1PpOUj2r6TlIhtQErALtvuKfV3n20avazjczbYVV6u/MqASzpokJrYFDr6O1N781Dy0/8ZbCHotd012POj5R/NKzzNAiivEWib6jrwNJmF85iijWNs/lR5IZb8eDtMoimZ1RulCTz/d8Jkq05dv9+Ibd8Zcfd1cXj4Ji1SEnzv6t9SMfCkkGLQQFLgwY6jUB+0Rtvnlt2FnTG8iDx+r/ku2r6TlIhtQHLHLtueKfF3n23qPyUdW5gFIIRwGEuzTgd8tNeACTfEOSznj+YVnr+VNq3g5qti3QaeAFBWLdA21c16XSyPNv1w+tQJ/N+RkTAIx+7rrtYfiOZd8et0wNvb9Vj5pQGfzk52CIfJGQNPCqlIBA1K+qIn73iw4yzUB63R+LlltZ6jcIcX/F52xO81//CiReSxDUju2GXbMyVetGCb+kdpxxYmsyWcFM0Czf2/mJCXa8JlHS3VkmY/KzybB5O6xkrLGDWQlqiCWmUJUV1VYJZVu09XK+tc3KeJx+66u6Br3Meebc/j8LlWAKQLPBvsut5fnKPGj6fMMAtLtSrDTu8YeFJIROrkVmlf9OQdD3acheOgNZwBoVI/t0oO8cIZXii1fUOJ32v+40WLyGMbkJwptct2uCjxogXb1D/hOrbIy8uTZD1COHZnj48FGgyWv/+9x4Bu2WrEx6pgEgKWcTJ9Y2jWpV3XuE5tbNP/6/WuA0mdUaC6XrjsKl5e4zoMraxXA7AknmNuaIfn/6NzG6gGwv41Gts/FiYz8OVPJjx8k1aS9Uc7Bp4kuUie3PIkMnzCFfbwYMeZEg9aPVHi5zYaQrxwhRdKbN9Q4/eab0L9O6PkixZEUlP650GJXbbDTWkXLdim/gnXsUXfvn0lWY9ZNFVfAsClF2pQ2yDwQ7EZ52sFjpw044rOmuArPBsrJLWxlhAzLsYSeOrcVGCWVpuRkuA8S1JFresA0z6QTNSqEBdjCW5DUeGpjVUhOV6Fp0fFIrOVP3PXt1wMPElykTy55UlkeIQz7OHBjmtKO2j1RImfW4Z4vlNi+4Yav9e8C/XvTDRctCCSSjR8HpTYZZs8Y5v6R2nHFmZzU0UnYKnw7J6twQ/FloSzVmcJ+vydtMjQbHlrsKltTL7iYiyzqhvcVGCWVwt0vsDxtjqdcAge7TkEkjGQPvBsVgVL/mHgSZKL5Mmt0r7olSqcYQ8PdqKfEj+3DPF8p8T2DTV+r3kX6t8ZXrQgahINn4do6/1CbFN/hevYYsWKFQCAMWPGBLUesxC26kvAUr0YZ5dOWQPD5mN8emMfZJqFgL5Zl3brNtxVeFbVC+iNojEYtXDXnR1omlQoPhZQq1WIbQxUJevS3vgaxWqAGA3H7PQXA0+SXCRPbnkSGR7hDHt4sBP9lPi5ZYjnOyW2b6hF4/eatTtsUVERunbtGvTzCfXvDC9aeKf0Ls7ku2j5PERT7xeyYJv6LlzHFuXl5ZKsxySaxtcELIGkfcho7e7ud4Wn3SztBiNgjR2t67ZWeBpNgNksoFY7hogCloAzs1XT7e66swNNgWR848Ty1kpSl7O6B8Ba4alldWdAGHiS5CJ5chuNJ5FyFO6whwc70U2Jn1uGeL5TYvuGQzR9r9l3hzWZTNBoNEF3hw317wwvWngWDV2cyXf8PMgDLzJQsJR0bCHMjoFnfCwcKjytgaF9gOkL+6CxeaAKOG5DbwLiXQyFWVYtkNmqcT+FwHkPgae1wjPBboxQwFKhav39DIY1UE2IY3VnIBh4kuQifXKrpC96pWLYQ1JT2uc20t9zSqO09iX/2HeH1Wg0MJlMQXeHDfXvDH/HPIuGLs7kO34eIo8XGailsVR4NuvSblfFaO3SrnMzdqY79rO020+KpG0WSAKA3uB6XMzyGmH7HFbXO8/8bmU2C1uoaq3wtHZpFwCMJiA2iMRNCGEb55TjdwaGgSeFBE9uoxvDHiJ+z3nDSpWWw747rNFolKQ7bKh/Z/g75lm0dHEm3/DzEHm8yEAtTfNJi5p3abeOgWlfpemNySxgtssmXVV4WoPPpm04V04aTAKVdUCrJKC8xn2fevtxQONdBarG4AJPo6mpS7/9fpPvGHgSUUAY9hCRO6xUaVnsu8NqNBrJusOG+neGv2PusYtzy8PPQ2TxIgO1NKJZhWd8rApxmqb7rWGlzo/Jf5qPm6lrNoM6YJn8x8rTTOrlNWa0StJ4HL+zXt90n7XLuX3gqTMKJLkIVH3lMIt9XMCradFcjFhAREREFDj7SpWcnBy0bt3aVqlC0Wfs2LHIy8tDWVkZTp8+jbKyMnaHVTj7Ni0uLmabEoWY/UUGALzIQLJUU1ODiooK/PjjjygoKEBNTU3A6zI1r/CMcaxitHYj96fCs/l4n44VnqrG/zfd5inwLK0WMJsFKus8TFikb/rbGkjaV6kGO3FRg11gm8AKz4CwwpOIiIgkxUoV+QnlEAP23WGlmqWdIotdnInCK9zjqHLYmegQznZs3nvn448/Drj3jhACJiEcKzDjVE7dwS3/Fz5P/tN8rE37LudNkxY5zwTvSk2DwJlKxy7yzdU7VKha1990v6dA1ReuAlXyDwNPIiIikhS7w8pLOIYYsHaHLS8vR0ZGhiTrpMhiF2ei8AnnRQYOOxMdwt2OUo4zqzcCZtGsAjPGcbxL632icXmtD5P2OFd4epm0yEsg+csZ9+N3Ao6BZFOXdudxSANV72L/yT8MPImIiEhSnPFXXjgZBskRK8yIHIXrIgN/E6JDuNvR2nunsrISAHDBBRcE3HvH1fic8bEqxKgBtcoShtqHhQ0GHwPP5mN42ld4NiZfjrO0ew4kvY0faj+GZ7yLMTyDrfDU2QeqnKU9IAw8iYiIJMSTeHaHlRsOMUBywwozosjhb0J0CHc7WnvvGI1GxMTEBNV7xxok2ldIamMBlcrSrb3B4BheWio1A+jS7rLC064Cs1lFqL8cKzzRuP6m24Lu0m5wDlTJPww8iUiWGBqREvEkvgm7w8oHhxgguWGFGVHk8DchOoS7Ha29d3bu3AkhBIxGY8C9d5oqPJtu09qNgdlgcLzP1+DQeZZ21+v3d73u1DebZd6yfgm7tNuP4ckKz4Aw8CQi2WFoRErFk3iSIw4xQHLDCrOWiRez5YG/CdEhVO3o7nNq7b0zceJEVFZWYuTIkYF9ho010B3/BDhVgYa63wNIQqwG0KgtQWFsjAqAaNal3bfg0PMYnpb/OwSSPq7XHdeztDfdFnSXdlZ4Bo2BJxHJDkMjUiqexJMccYgBkhtWmLU8vJgtH/xNiA6haEdvn9Pk5GT87ne/A4DAzsmMNcCBqWg4FQ9UJkKnHwIgyWF8TutYm45d2n1cvadZ2huDTvttBV3haTeGZ0IIxvBssHverPAMDANPIpKdaA+NWOEQfaxtun//ftTX16O+vh4JCQk8iSfZ4BADJCesMGt5eDFbXvibEB2kbseQf05PrgLqT0NnvgiAGTpT47meugFAAoCmwFBvhO33wdvkQVbOFZ5Nf1sDQ4eZ4EMQSMZK2qWdFZ7BYuBJRLITzZUfrHAIj3CGyvZtajKZ0NDQgKKiIrRq1QpqtZon8UREzbiqTLr11lt5MTCKRfvFbKJo4MvndMyYMYFvoKEEgAp6UywAAZ3JkhLGa5r6hlu7tAtYAsy4GEDvY4Wn8xiezpMWae0CSUOQgWRDiCs8XQW25B8GnkQkO9Fc+cEKB4tQBpLhDpWbt2nr1q1x6tQp5OXl4ZZbbuFJO0UEK8lJ7uwrk3gxMPpF88Vsomjhy+c0IyMj8A3EZwIQaDBpYRJ6GIQlxbMPIbXNAkPLJEa+Vng2n6W96W9XkxYFW+FZH+IxPBtcTIpE/mHgSYrEE7noFs1jC7HCIfQntuEOlZu3aWJiIhISEtCnT58WFWKTfDA8kh8et3jGi4HRL5ovZhNFC18+p+Xl5QACDD7b3w796Z0wQw29Od52szYxxfa38yznKpiF5W/7+5ozmwWMZsfbrF3hVQBiNdb1N90vVSCpQlNQG7JZ2uOCWlWLxcCTFIcnci1DtI4txAqH0J/YhjtUZpuS3DA8khcet3jHi4GuRVNQHs0Xs4mihS+f0xUrVgAApk6d6v8GYpKh7/UaULcXOmOp7eb4OLXtb3eBpLXa053mYSfQVOGpjQVUKmuXc+kDyfg4+/U33d+8i72/GlzMMk/+YeBJisMTOVIyVjiE/sQ23AEk25TkhuGRvPC4xTteOHIWjUF5tF7MJoomof6cNpiTgHaDoddX2G6z79Ie6ybw1BkEkuPdV3i6qta0Dzxt27Kfpd3HsUHdsY7hmWA3oZCkY3g2BqraWECtYpf2QDDwJMXhiRwpGSscQn9iG+4Akm1KcsPwSF543OIdLxw5Y1BOvoqmSmCKftZxM12Nr2n523UFps5LOGk/gVDTtiy32QeqMZqm+/WmICctatwn+wmFJK0gdbF+8g8DT1IcnsiR0rX0CodQn9hGIoBs6W0aLjyp8w3DI3nhcYt3vHDkrHlQHhMTg9raWixfvhwAWvzrQxbRWAlM0c06c7rOaDdRUaybCkm7kNPTxEUms8DREuc+7a4qPNUqFWI1lhngg6nwNJmFrYIzVBWe1hCXExYFjoEnKU44TuR4Uk0UOuE4sWUAGX14Uuc7hkfywgDaN/zedmQflMfExKCoqAi1tbU4ePAgfvrpJ37/EQDnSuC6ujrs2rULY8aMwS233MLvfpIdawhpHwY6VEjaV2AaXf/d3NESM+qaVXgaTQKmxgxU2yww1MY2Bp5BBJINbiYUipWoglQIYXutOGFR4Bh4kuKE+kSOJ9UtDwPu8OOJLfmL3Tv9w8+YfDCApkDYB+W1tbWora1FUlISunbtCoPBwO8/AuBYCWwymXDq1ClUV1dj586d2L9/P89hSHas3czdVnjad2m3CwzdVXiW15hRXO6+utOyfsf7LN3ORVBdzu33x74CU6VSIS7GEqYGU0GqMwLWLbDCM3AMPEmRQnkix5PqloUBN5EycBxEUjIG0OQv+6B8+fLlOHjwILp27Qq1Ws3vP7KxrwSuqqpCbW0t1Go1MjIykJqaynMYklxAs7PbafBW4emmS7urMTwNJoEfTrqYnh1NwSrgOIan/TaCqfCst6vwTGhWgWkLPENQQUr+UUd6B4jkhifVLYt9wJ2Tk4PWrVvbDg6JSD7sT+oAcBxEIop61qB87NixSEpKgsFgOePn9x9ZjR07Fnl5eSgrK0NZWRnMZjOSkpJshRs8h6FQM/rZbVtvHcPT4MMYnl66tB89bXYINu15rvBsXMZo6ToeCPtJkuLjmgeqln8HU0Gqc1NBSv5h4EnUDE+qWxYG3ETKYH9SV1xcjLKyMo6DSEQtAr//yB1rJXB+fj4GDx6MlJQU5OTkQK1W8xyGQuLo0aM4evSo7d+1Ot8fazQJGBsLMnV2AabWh1nODSYBk1k43Hem0nV1J+BLl3ZACNj2x1/2FZ7NZ1GPlaKC1G7/OUt74NilnagZTi7QsnD2XCJlCMc4iBzPl4jkiOPAkif2lcDWYZrKy8t5DkMhsXXrVgBAbm4uAKCmQSAt0bcKxAb7LupuA8+mv5sHhjoDkGipUcGpcjPMHgoo7Ssk49x0aQcAg9FxoiFf2Y/hmeBU4Wn5f3Bd2t1XkJLvGHgSNRONB5U8iXePATeRcoRyHESO50sUfaLp+IfjwJI30XgOQ/JX0+B7t237ik293aRF8W67tDuuW2cQSNRalj1d4Xm77gLV5v+2D1H9Ue9hjM04jWVSJLMATGYBjdr/wLLBQwUp+Y6BJ5EL0XRQyZN4z3hwSEQAJ6wjijY8/qGWKJrOYUgZGvQCRpNAjMZ7qKfzqcLTvkt7s8c3/ru8xox6N7O2N23LbtKiZmNgxmqad5sPJJB0P8am/fPRG50nNfJp/QZWeEqBgSdRlONJvHc8OCQijudLFF14/ENEFHoClnE80xK9L2sf4tlXeNrPou6tSzsAFJd5CDtNDUDpXujOJQHo1bh+x0WaB5KBsB9js3mgGdvsOQQWeDb9zQrPwDHwJIpyPIknIvKO4/kSRRelHP+46nZPRKQkvo7jqbev8LQP9OwCQfsKz+YzsOsMAjqDQGm1m5mGTA1A0SJAXw5ddV/bzVqNAfbRl7uJkfzhyyzt1n2WuoKUfMfAk0gGQjnGFE/iiYi843i+RNGl+fFPXV0d6uvrsX//fhQUFMhi+Bp33e4XLFiAjIyMiO4bEZErrr6bfB3H01WFp0rlOGlQ8wmF7OkMwKkKM9xurXQvoC8HIKAzN61IW38EQG+X29AFWuFpN8ZmgtMs8E1/B1pB2uAmECb/MPAkirBQjzHFk3giIu84ni9JLZomzFEi++Mfk8mEyspKAEBhYSH2798vi/E83XW7X7lyJaZPnx6x/SIicmfMmDFOt9XqfAs87cNF69/aGEClsuve7qG7eYNBoLrBwwYMFbBUUwroTE0poVZUOiwmTSDpqcLTbpckWH8CKzwDxsCTKMJCPcYUT+KJiHzD8Xz9w6647nHCnMizP/5Zv349du/ejZycHCQkJMhmPE933e5LSkoitk9ERP6q9RRC2nGctMgS4jWfQV2jVkGtAszCuUt7tbdK0th0oLH+0z7wjI93HGDUvsu5IcAu7Q4Vns1naZeky3zT36zwDBwDT6IIC8cYUzyJd8bKGyKiwLErrmecMEcerMc/p06dwtdff42EhAQA8hnP092wQ5mZmRHdLyIid/bt2wcA6Nu3aYxMg0mgQS88ziZuNgsYTE3hn7XC09X4lHExli7dfldftukHVH5rGcPTbFfh2baX0/qb74e/rBWY6mZd8gEgzu7fUlSQNp9lnnzHwJMowjjGZvix8oYouvGCRuixK65nSpkwp6WQ67GWu2GHRo8eHdH9IiJyp7CwEIBj4AlYxvH0FHjaj0kphLAFgc0rPIGmwNPv7uCaeKDrZMss7b+0td2s1cY7rd9KX/EzcPIHS3Vom36WdfjAWoGZEOfYJd9p/RJUeAYyyztZMPAkijCOsRl+rLwhil68oBEe7IrrmVwDtpZKrsda7oYd0uv13h9MRCQjtTqgjYf77bunG0yAENYu7c4hqTZWBdSLxhnO/aSJB9oNhi5GB8DcuD4X62+krzwOpHwLQFiqQ7tO9in0rG+cRd1VhWqsfZd2k9/PAIDnClLyHQNPoghoXn20ePFirFu3jtVIYcLKG6LoFY4LGqwgZVdcb+QasLVUch7P3NWwQ+Xl5RHaGyKiwHibqd1h/E67v11VeMY2plSBdgcHHLuqxzVLvRwqMM2xsAaj0JdbZnpvN9jr+q0Vq66qLyWZFKnxupc21rmClHzHwJMozFh9FHmsvCGKXqG+oMHvcAt2xfVMzgFbS8XxzIlITqLt4qm3wLPObiZ3hxnO3YzhCVgqQc1CQB1A4GetDo3VWCZCcrV+wBp4WqkaZ3r3zGASMDZWbrrqxu9QQRpol/bG/U/wMEwAecfAkyjM2J068lh5QxS9Qn1Bg9/hFuyK6x0DNiIiciUaL57W6QTMZgG12nVAV11vN2GRlwpPbYwK1tnWjSbnCk1fWLfheoxQ+y7n9guIxpnePXOYQd3NGKS29Ruc7/eFtYLU1frJdww8icJMyuqjaLoyGM7nwsobougV6gsaHBKjCbviElFz0XRsShQqSr542rVrV5e3CwB1eiDZzfCXNQ1Nf+u8VHjG2s+ibggw8GysrNTGuK8gBQCdORaAGoAA4jIsExd5YmpAw+kDAHoDABJizU6LxAbZpd1kbprUydNEUOQdA0+iMJOq+iiargxG4rmw8oYoOoX6ggaHxCAici2ajk29sQa7RUVF6Nq1K4Nd8ouSL54OGzbM7X01DQLJ8c4Bnd4oHCYtavBS4elQgWkUAALp0u7j+mOygVaX+zZLu6kBKFqE+vJYWAPP+PrvAdOlDo9z3v/A9h1ghWewGHgShZlU1UeBXhmU45V3JV/lJCL5CeUFDQ6JQUTkWks5nrMPdk0mEzQaTdQGuxQa0Xrx1N04nrXNbnfo0u4ikdIGWSFptquQdB14Nv1tiGkDtL/dtxWX7gX05WgwdbDdlKCqcproKM5uVvVA9t/bGKfkOwaeRD6QMiSUqvookCuDcr3yruSrnFZyDJKJSHocEoOIyLVoOJ7zhX2wq9FoYDKZojLYjRZyPEZX8sXTLVu2AHBd6eku8LTvzg44dmnXepi0CAgsMLR/jKv124egOn/G2DRUAFChwaS13RSv0QOGOofFHAJVkx/rb+QwRqiLWeDJdww8ibwIRUgoRfVRIFcG5XrlXelXOT29R4go+nBIDCIiZ0o/nvOVfbBrNBqjNtiNBnIt9lDyxdOioiK391XWup64qLpZEOqtS3tssF3C7QNPF4mXRq2CWgWYBaA3+bH+2HQAAvUOgafOaaKjYLu01ztUePr9cLKjjvQOEMmdfUiYk5OD1q1b20LCSBo7dizy8vJQVlaG4uJilJWVeb0yKNcr74E8FzmR63uEiIiIKFyUfjznK/tgF0DUBrvRQM7H6NaLp/n5+XjooYcUEXZ6YxJAZZ1zwNe88tPbpEXBVnh6qyC13N64fn8qPNv0A+IyHCo8E+JjnCY6Cnr/HSo82aU9GKzwJPJCriFhIFcG5XrlXclXOQH5vkeIiIiIwkXpx3O+su+ObB3DMxqD3WjAY/Twq6gVSLf7yJvNAnW6ZoGnQ5dz53UEO4anzksFKWAJJev1fq5fEw90nYz6sydsN8XnXO800VGwgWcDJy2SDANPIi/kGhIC/nerlPN4MUruIirn9wgRERFRuCj5eM5X9sEuZ2mXt0gco8txzNBwKq8R6NKu6d91ekvXcXsNem8VnsHOcu69wtOyDeH/+jXxaIjrAsCSZMa7GGRTrVYhRgMYTQF2affy+pDvGHgSeSHnkNBfLeXKe7h5eo/o9XrvK5CJln6ARkREROQLa7BbXl6OjIyMSO8OuRHu8zi5jhkaTtX1AgaTQKzGEtS5msjIW4Vn0F3CvYzhab+NQNZvH0gmuOlyHhfTGHj602W+kX2FKictCg4DTyIvoi0kbAlX3sPN03ukvLw80rvnEx6gEREREVE0Cfd5nFwniJVSXl6ex/sFgIoagQvSLEFgdb2LwNOhAtN5HQ6BpyGQCs+mvz11aQcss6ibhYBa5XslpcMs6h7WX6fzc1Ik6/q9jHFKvgs68NTpdMjLy8OhQ4fw888/Izc31+H+M2fO4MUXX8SWLVtQXFyMrKws/OEPf8Cf//xnpKSkOK3PbDZj6dKleOONN/DTTz8hLi4Ov/vd7zB79mz06dPH5T588803eOGFF/DVV1/h/Pnz6N69OyZNmoRJkyZB5eKNW1VVhZdeegkff/wxTpw4gdatW+OWW27BCy+8gAsuuCDYl4SiEENC8kbp75GWcIBGRMFhFTgRESlNoMfogfzmtYQxQ/v27et1GUvgafnbZYWnwxiV3rq0+7+POqPvXdqt2/BnrEz7WdTdVnhqrF3mfV8vAMDUgPrzxQAswy7Ea/QAEvxcCVkFHXjOmjULhw4dcnnf6dOn0a9fP5w4cQK9evXCiBEjUFhYiJdeegmbN2/G559/jtTUVIfHPPzwwygoKEB6ejqGDBmCM2fOYO3atdi0aRM2bNiAm266yWH5HTt2YPjw4TAYDOjfvz9atWqF7du34+GHH8aXX36JpUuXOixfXV2NwYMH48CBA+jatStuvvlmHDp0CIsWLcL69evx5Zdfon379sG+LEREitISDtCIKHCsAiciopZy4SvQ3zyO629RUdsUCNY0ON/f4KECM0mrcqzwrPgROHPOMhN6s8mB3PGnwhMADH4Gng4Vnm66nAfUZd7UABQtgq66L2yB55mPgMw7fX7u5EgdzIO3b9+O+fPnu71/6tSpOHHiBGbNmoX//e9/+Pjjj/Hzzz/jzjvvxKFDhzB79myH5detW4eCggL06tULP//8M1atWoU9e/bg448/htlsxv3334+6ujrb8jqdDuPGjYPRaMSGDRuwc+dOrFmzBj/++CN69OiBd999F6tXr3bYxvPPP48DBw5g/PjxOHLkCD766CMcPnwY06dPR3FxMaZOnRrMS0IhVFNTg4KCAuTn56OgoAA1NTWR3iWiqGF/gAagxR6gRRK/40jO7KvAc3Jy0Lp1a1sVOBERRT9rCJifn4/FixcjPz8f48aNi8rjlUB/88aOHYu8vDyUlZWhuLgYZWVlip37wZ0VK1ZgxYoVHpep0wvU6wV0Bst4ns1Zu7THaACN2rFC8sI2amg1TYmivq4cOLcDKFpkCQR94NBlPsZ1BaZ95afOz27z1kmXYtSwjVXaXGzjdo0my0z1PindC+jL0WBqSlHjzecst1NAAq7wPH/+PO6//35cdNFFqKqqQklJicP9R48exZo1a9ChQwe88MILttvj4uJQUFCALVu2YMmSJfjb3/5mu0ry8ssvAwDmzp2L1q1b2x4zevRojBs3Du+99x7+/e9/44EHHgAALFu2DKdPn8bdd9+N4cOH25bPysrCG2+8geuvvx7z58/HqFGjAFi6shcUFCAxMRGvvvoqYmIsT1+tVmPu3LlYu3Yt1q1bZ5ttj+SDlSVEob2qHk2TcykRv+NI7lgFHj4tpYKKqCVS8ue7JQ1/FOhvXqBjhirpfeHr/AQVNcJtdaW1wrN5VWVcjArt0lRIqP0GwBUAAL05BoAA9OWW4K/dYK/b9qXCM1bT9Le/3c7rrfvvYUIhhypVExDvS6mhoQKACvUmre2meI2+8XYKRMCB55QpU3Dq1Cns2bMHd911l9P9mzdvhhACI0aMsAWLVmlpaRg8eDDWrl2L7du349Zbb0VlZSX27NmD5ORk3HDDDU7rGzVqFN577z1s2LDBFnhu3LgRADBy5Ein5QcOHIj09HR8/vnnOH/+PFq1aoVdu3ahtrYWw4YNQ3p6usPyGo0Gt9xyC1577TVs3LgRf/rTnwJ9aSgEWtIPLJEroQ7Eom1yLqXhdxzJHbvphQcvfhBFL6V/vlvSha9gfvP8HTNU6e8Ld8prBVLiXVc/Wisqm1dfZqeroFarkIAy2216szWxVPkc/DmM4em2wrPpb38DT2uFp7vxO4EAu8zHpgMQaHAIPHWNt1MgAurSbi1jfuaZZ3D11Ve7XOb7778HAFx66aUu7+/ZsycA2Mb//OGHH2A2m9GjRw+ngNTV8t62oVar0aNHDwghbMv5u0/kO3+6YgbSbbMl/cDKFbvbRlY4upNaD9Dy8/Px0EMPKfogS2n4HUdy1xK66ckBhw4gil5K/3y3pOGPwvmbp/T3hTsVNQLVLiYsApoqMO1DR7UKyMmwxFPxCU3nIHqTdSHhc/Dn2xie9hMj+d6lXQiB+sYe955CzIDW36YfEJcBXWOX9hiVETHxrSy3U0D8rvD87bffMGXKFFx55ZV47rnn3C5nPUnLyspyeb/19jNnzgS0fLi24YpOp7N90VtptVrbiWpL489VKQ4ArUzReuVRSRiIRTd+x5HcsQo8PPhdTxS9lP75bknDH4XzN0/p7wt3DCaBsirnoM9kFjCYLH/bj6PZNlVt+3didlNRnc4cB0AFxLYChAE4ucoSfHqYxMjfSYt0flR4GkyAdUjOeB8rPH2uINXEA10no76w3rL+WAF0ncwJi4LgV+AphMB9992H+vp6vPfee4iNdR9p19bWAgASExNd3p+QkAAAtioxX5e3LhfKbXirXJszZ47DuKQA8NRTT2HmzJkeH6c0FRW+lYy/++67+Oqrr5Cenm47Uf/qq6+wZMkS3HfffQEva2/YsGFYvXo1Dh48aPuB7d27N4YNG+bzOCLRxNe2kUqg7UbStVVaWhqEEKitrbW1gRACaWlpLfIzEAqu2qqmpgYrV65ESUkJMjMzMXr06JAc7PI7zrNwf+eRe3fccYftb71e7/T+DKatwvV5k7NQftfzc6QcbCvl8KetouFYbsGCBU7f065+CyJF6s+Ot988Kfj7voj0b6U1U7HuW+V5I6pqfa+QtFRHWnKXGJUBVZWW9XVpFYPyckuAaNAZAVhCvnqRgqq4y4GqI0DFpwBUAARwci/Q+X6XYWBtfRwAyyCd+vrzqDI774fZGAPAkmdVVlWjqtLFQi5U1Kps+6bVNO2/8wZiYY3bys9XIkntx2tktKw/LlaDqpo6AHUO91ckaRBnDmr+cY/k8BuUkZEhyXr8CjxfeeUV7NixAy+//DIuueQSj8tqNJY3mErlPvUGALPZ7Pfy1pNBjUYDs9kckm148swzz2DGjBkOt0Vrhacvb7TKykqoVCokJSUBAGJiYnD+/HlUVlY6Pd6fZZvvx0cffcTKEjtSfQn4ItB2IwspXqOJEydi586dDlfVr776akycOLFFfw6kZt9WNTU1eOSRRxxe8507d4akspnfcd7xu0Y5AmmrcH7e5CzU3/X8HCkH20o5fG2raDiWy8jIwPTp0yO9Gx4p7bPjz/tCDr+Vt99+O4Cm1zmtyghTjO9hnqlGALDMtp6UEIvUtGSkJajQqX1TNNW22gSNuhYmM2CObYvUtAxAr4M1aLRoAAw/AhnOkxiZoANgyXTaZKTbZky3l5psBGApBY2JS0Zqmm/RWGmDCYClT3ubtDikprl+3ZMTDQAspZ2x8alITdO4XK45o0mgTm95fdKSNJbn3kx6ugYZrUIXeALK+xy543PgeejQITz77LMYMGCAT19y1g9cfX29y/utt1uX83X5pKQkW2CZnJyMiooK1NfXuwwbA92Gty+LaA03A+VPV8xwDgBN0mF328hjd9LwC/dEQvyOo5aME3dZ8LueSPnczbgt1edbSTN6y5HcXj9/3hdy+K3Mzc0N6vEOEwo1dmHPSHEMJDVqS5fwen1jd/PG2csB+2DV/SRG1kmR1Cogxk3OGGiX9kq7Ysu0RN8nLfJVtV1UlZrguVCPvPM58HzmmWeg0+mgVqsxfvx4h/tKS0sBAE888QSSk5Px7LPPIicnBwBQUlLicn2nT58G0DRupr/LWx9TUVGBkpIStGrVKiTbIO/8Gc+lJY39Ek3YbvLAQCy8mo+pFBMTg9raWtsg8pE+QCaKJtE6hlkg+F1PpFzexr0P9vPNcfWDI9fXz9f3RTT8VjbYja9pnfQnVuM+8NQbYZu93JH7SYzsJ0Vy17s30EmLKuualvU18PRnFvjK+qb1p3pYP/nG58DTOq7lzp073S6zdu1aAMCDDz5omwn98OHDLpe1zpjeq1cvAMDFF18MtVqNI0eOwGw2Q61We1wesMy2/t133+Hw4cPo0aOHw/JmsxlHjhyBSqWydb/3d5/IN/5clWLlgjL5025yu2pK0mspbWxf2RwTE4OioiLU1tbi4MGD+Omnn2RxgEwULdiTgIiiQagr8ORQ4adkSn/95PBbuXDhQgDA1KlTA3q8tfoSaKrwjG2WSlkCT0tFp8EoLBMUVX4L6Mthq/TUxAP6c8CZHU4TGFmrSN1NWNT8Pr0BgKkBKN1rqRr1MCmS74FnYIFqlY/rJ9/4HHh6Cjo7deqEEydO4Oeff7aVOHfu3BkqlQobNmzA/PnzbeNnApbxAHfs2IHExEQMHDgQAGx/79ixAzt37sT111/vsI3Vq1cDAH7/+9/bbhs+fDj+/e9/Y82aNbaxJOz3t6KiAtddd52t+rN///5ISkrCzp07UVlZibS0NNvyJpMJ69evh0qlwrBhw3x9WaiRP1crWbmgTL60m1yvmpJ0WlIb21c219bWora2FklJSejatSsMBoOiDpCJ5I49CYhCr6VcsIykUFfgRUOFXyQp/fWLht9KVzOoxzXrdm6t8AQau5s3zl6O0r2A7hxQe9QSUFZ+D0BYwlC72cxtFZ4uxu60sg9Z9QYjULTYMVBttk6rUFd42geerPAMXshGOu3YsSNuueUWHDt2DDNnzoQQlobT6/WYNGkSqqurMWnSJIfQ8ZFHHgEATJs2zaHb+apVq7B8+XJkZWXh3nvvtd1+++23Izs7G8uXL8eqVatst5eUlGDatGkA4DBzemJiIiZMmGDbtl5vGWxWCIGZM2fi2LFjGDlyJLp16xaCV4Qo+tlfNc3JyUHr1q1toRBFh5bUxtbK5vz8fPTu3RvJycnIzc2FWq1W3AFyS1ZTU4OCggLk5+ejoKDA1mOF5MX+8zZp0iTk5+dH5YUUokixXrDMz8/H4sWLkZ+fj3HjxvE7UWL2FXgAJK/AC/X6o53SX79o+K2ssuuynaS1Vng279KuslVIGk2A2dxY0dluMKBtawk7IWCZmEhYgsrSvQAs2Y59l3Z37MNQfXVJY9jpep32Kmt9CzxjAw087V6fNI7hGTS/Zmn31+uvv479+/fjlVdewaZNm3DppZdi3759+PXXX9GnTx/85S9/cVh+1KhRuPfee/H++++je/fuuP7661FaWoo9e/YgLi4Oy5cvd5gsKDk5GW+99RZuvfVW3HHHHbjuuuvQpk0bbN++HVVVVZg0aRJuvfVWh228+OKL2L59O/7zn//gyy+/RF5eHr777jscOXIEnTp1spVoE5H/lH7VlLxraW1sX9n8008/wWAwKLa7bTgqe+RWPdSSKpKjAXuAkBzI7XtMKkrvyqsUoa7Ai4YKv0iKhtdP6b+V5+0Cw/SkxsDTQ4UnAOhNQLy1VM/LBEZGE2BuvMvaZd6VuOYVnj5OimSt8IzRAIke5rGWYoxQVngGL6SBZ4cOHbBv3z48//zz2LhxI9avX4+OHTti1qxZeOqpp1wePCxduhR9+/bFkiVLsGXLFrRq1Qq33nor8vPzccUVVzgtP2zYMOzZswcvvPAC9uzZA6PRiG7dumHKlCn44x//6LR8amoqdu/ejb/+9a/4+OOPsX79euTk5GDKlCmYPXs2MjMzQ/FSELUIchhXhkKrpbax0g+QwxH8yTFc5Ak+EflDjt9jUmlpFywjJdTzFXA+hODw9Ys8+8CzVbJvgafB2DTBkbcJjOxnXNd6SLscAk8R73Gd9qyBZFqiyu2ESE7r92sWeI7hKSVJAs/jx4+7vS87OxtLlizxeV1qtRrTpk2zdUn3RV5eHjZs2ODz8q1atcLLL7+Ml19+2efHEJF3cg6ForViI9zk3MahpPQD5HAEf3IMF+V2gs/vISJ5k+P3mFRa6gXLSAhFBR5/P/zj6fVSeoWk0jWv8IxRA2q18yzt9tWZOoNAirV7t6sJjOIyLLfD9aRIrthXYOo0bSzrcLNOK4NJoNYyGoLXMNIx8PRj0qLGLu0qACkJPj+M3AhphScRtSxyDYWUVLEhxQFtKA+K5drG4aDkA+RwBH9yCxcBeZ3gK+l7iALHUELZ/P0eU1J7y+2CpZJeu0jj74d/wv16tbT38pgxYwBYJtfZd9SI3UeMaJOiwiUdNF4eaVHRGHjGqAWStECsxjk4jNE4Vn06VEjaT2DkYkZ1V5MiueJQQWpSe1ynleMM6p6fp2OXds/LOm7D8v+keMtYphQcBp4UUS3tB0JpAmkfOYZCSqnYkOIALRwHeXJsY/IsHMGfnMJFKzmd4Cvle4gCFw2hREs/LvPne0xp7S2nC5ZKe+0ijb8f/gnn69US38sZGRkAgEMnTJj2Vj0A4NruGp8DT2uFZ2qC5fWKdZFIqVUqpzE8HVgnMHKh2m7Sn0Stb5MK6YzC4zqt/OluHkiXdiGELVRld3ZpMPCkiGmJPxDBsp6IFBUVoWvXriE9UA1F+0TqREqOlWeuSHGAxoNieZPqM+DvesIR/MkpXLSS0wl+OL6HWnpYFWlK/P61f89kZGRg69atOHjwYIs9LvPne0yJ7S2XC5ZKfO0iSSnHsXIRzterJb6Xy8vLAQBdMpvGtzxdYfbpsQ0GgXq95e+0REuw13z8TsBS4elQIWnw0iXc1GCrzqwouQhALoCmSZFcUatUiNUABpPvgWRlXdPf3gNP+1ngi4Ezx11Wjdqr0wHGxpcylTO0S4KBJ/lFypOplvgDEQz7ANJkMkGj0YT0RETq9olkwC3HyjNXpDhA40Gx/8J1IUGqz0Ag6wlH8CencLH5fsnhNyXU30NyvojYUoJYpX3/Nn/P1NfXo6GhAV27dkVCQkKLPC7z53tMae3tr1B+bqP9tZOaUo5j5SKcr1dLfC+vWLECADB16lTkZKhQXC5wukLYjj08qbQbvzMtoTHwjHF+jFrl2B3dYyBpagCKFtnG3zxfmgBfAk/Asg2DCdAbPC5mYz/+qNfAU9Vg+9vQUAec22EZe7TrZLehZ1U9JyySGgNP8pnUJ1Ny+4GQ+wmZfQCp0WhgMplCeiIidftEMuCWY+WZK80P0Orq6lBfX4/9+/ejoKDAp/ckD4r9E84LCVJ9BgJdTziCP7mEi74K5/d+qL+H5HoRUc5BrNQi/f3r7/u5+Xvm119/RXV1NWpqapCQkBDx47JI8fV7LNLtHUqh/txG82sXCko5jpWLcL5eLf29nJupQXG5EQ0Gy9icGcmeQ7oKu8AwNcFzhWesr13CS/c2hp0CgECFPsV2Vysv+2OpwhQ+TypkH0i28hKmxlV+BeBqAIDeHGPZP325ZX/ddJ237zKfysBTEgw8yWdSn0zJ6QdCCSdk9gGk0WgM+YmI1O0TyYBbrpVngHN3wt69e+PgwYMwmUyorKwEABQWFmL//v0+vSd5UOyfcF5IkOozILeLRaEUyurbcH/vh/p7SK7vC7kGsaEQye/fQN7Pzd8zCQmW6WDr6y1jsrW0E3d/RfPvbag/t3J+7eRYABGO41i5PG8p9iMUr5e7/ZLzezkccjPV2HXY8vfpCoEMLy/xeVcVni4CT7Wq+aQ/HgJJQwVsM6sDOG8XeHqr8EzSAuU1QE2DZRtxLqpN7VU6BLael9UYy6GGGWaooTdby1VVjfvrZv0MPCXHwJN8JvXJlJx+IJRwQmYfQGo0Gq8D6Qf7Iy91+0gZoEbLZEquTlB79+6Np59+Gp9++il2796NnJwcv7oWyjnclaNwXkiQ6jMgp4tFoRTq6ttIfO+H8ntIru8LuQaxoRDJ799A3s/N3zNJSUmIjY2FXq9HcXFxRI/L5BK+eBLNv7eh/tzK9bWTcwFEKH8/5PK8pdwPKV8vb/slx/dyuORmqW1/n64we524yLFLuOVvVyFjjEYFra8VnrHpsIadAHBenwoAUKvMSHE/XCYAoEMbNX4rM8EsgJNlZnRp52b/G8cIPV+eC6ANAO8Vnqq4dMRpDGgwae0CTwHUnwbO7HA9C3y9faDqed/JNww8ySP7A86jR48CgGQnU3L6gVDCCZl9AGk9+Xd1IuLvwYK7kwqp28dVgNq7d280NDQgPz/f5/XL5aBMCq5OUA8ePIjbbrsNffr0wddff22ruPHnPSnHcFeu/LmQECypLiI0X48QAu3atcPx48d9HvpACUJdfauE731/yOkioj25BrGhEqnv30Dez67eMzfccAOGDh2K8vLyiB2XKel3Plp/b8PxuZXja6eEAohQkMvzlst++LtfcnwvA+G5cJSb2RQQnq7w3i28wuUYnq6XjY/1scKzTT/L2JiNY3hW6CwVnq0S1VCrPYeSF7ZR44sfLVPAnzgn0KWdi4XsxgitqusCAIhRGZGgaQDgIZVs0w9x6lo0mKxd2gFAALozwLkzLsfzrPJjUiTyDQNPcqumpgaPPPKIw0k1AJSWlgKAJCdTcvmBUMIJmX0A6al7pz8HC95OKqRsn+YBqnU22H/84x9+ndDI9WAoEJ5OUEP1nlRC1Uw4+XohQQpSXUSwX8/x48exceNGnD17Fu+8846sgwF/hbr6Vgnf+/6Q00VEe3INYqNNIO9nub5noul3Xqla6uc22i6E+SpUz9vfY05/98PV+kNBie+LcF046trOscLTm/Mux/B0HezFx9kHnh5Wqom3BIele2FoqESNMQkA0CpZ7eFBFh3bNi1z4pyb/bcbI7TSYHnt0uKqoSr70e1YnNb9itUKwADozVo0dbtvfA1cjOdZVWdfAcvAUwoMPMmtlStXOh1wnjt3DjfccANyc3Nlc2AsBaUc2FkDyPLycmRkZLhcxp8f5XCfVNgHqAUFBTh48KDf21biQYc7nk5QQ/GeVFLVTLj4eiFByu1J8dmyrqegoADnzp1DmzZtoi4YCHX1rVK+9/0hl4uI9uQaqkWbQN/PcnzPRNPvvFK11M9ttF0I81Uonncgx5z+7Ie79S9YsMDtOVKglPi+COU53tSpU21/J2pVuCBVhbNVAqfPe5+p/XxjoKdWwdbdPMZNL/KEuKa/PQaegCX0bDcYlVVmADoA3rucA0B2hgoxasBoBk6Uugk8G8cINZjVqDUmWtYdV+NxLE4rbWzjpEjmWNiPM2rhPJ6nwxieXsYIJd8w8CS3SkpKnA44VSoVcnNzkZ+fH9mdk1g0Hdj586McyZOKQLetxIMOdzydoIbiPcmqGdd8uZAgV9EcDIS6+jaavvflTo6hWrSJpvdzNP3OK1lL/NxG44UwX4TieQdyzOnPfrhb/8qVKzF9+vSA99vX/QpkWK5wCufxYfvWlsBT58NM7dYKz9REFdSNxZVxbru0N/3t6yzq9l3mWyV6Xz5Wo0J2hgq/lgqcPS9QrxdIiGu2/41jhFbZTYaUGlvTeLtn1uemN8U0vnfs7xVO67CO4amNcaxwpcAx8CS3MjMzW9QBZ7Qc2PlzsBDJk4pAtx1NB6PeTlClfk9GOhxjd3rpRXMwEI7q22j53m+J+H3iLFrez9H0O0/KEk0XDvwRiucdyDGnP/vhbv0lJSUB77Ov+xXosFzhFMrjQ+u8Hrm5uQCA9q3VOHDMUh3paaZ2g0mgut7yd3pS0+2uZmkHgERfu7Tbse8y722GdquObdX4tdQEAeC3UjO6ZTfbocYxQs9XN43XmZZgtNzuRWxMU1WnIaYt4kznYKv0jMtwWoe1wpMztEuHgSe5NXr0aOzcuZMHnArjz8FCOE4q3J2UBtP9LpoORsN5ghrJcIzd6UMj2oMBJVffUujw+yS6RdvvPCmLXC4chPuijtTPO9BjTl/3w936MzMzpXoKbvcr0GG5wimUx4dbt24F4Bh4Wp3yMFO7/fiU1u7msRqV2y7w8f50aW/kUOHpR+C5+4fGiYtcBZ6NY4RW1f7YtO6sXk4zrLuSpG36+3zmg7hA/6WlG3tsutMs7XqjQL3e8je7s0uHgSe5xQNO5fL1YCHUbeztpDTQbcvlYFRpIhmOKaU7vdKqxvg9TS2RUr5PKHDh/p1X2nc/oMx9Jt9Ew0WdUB9zulv/6NGjJVm/J+HoMRXs5zucx4f2gaenmdpdhZHuqjsBIFHbtF5fu7TbV3i28tC13t6FbZq28+s5N9vRxON87CUADACA1KRY18s1k52hxjfHLdWvxefjcEFn95McVdfbjd/pQ3d88g0DT/KIwVL0C2Ubezsp5fsr9JofMC1evBjr1q0L+wlSpLvT+6KmpgaPPPKI4k4w+DkiOQplGKOE7xNSDiWGS0rcZ/JdpC/qSPH9HerAzVU3cwBYuHBhyCegDHWPKak+3+E6PszJUNmm4/E0U/t5vwPPpr8DqfD0tUt7VroKsRrAYPIwUzscJxTytXo0J6MpTD1ZZkbvzu6fcCVnaA8JBp5EFDI8KY0sOZ0QKWGsyZUrV7JqjCTVUiuwQv3do4TvE3JNjp+JSIdLgVDiPpPvInn87Or7e+3atRg6dCjKy8tl1SPLun77fbZOchjK491QV68q7fOtjVWhdYoKpdUCJRUCZiGgdtFN3VUYaRnj0jX7Wdp1Bt/2pbJxGyr4Hhpq1Cp0aKPGL2fMKK0WqG0QSIp3fmxVAIFk+9ZNyxWXuw9TLetv+ptjeEqHgScRhQxPSiNLTgdMkRwv1lclJSUM6EkycrrgEG6h/u4J9PtEjmFbSyLXz4S3cEmO75uWfEFZju0htUgePzf//q6rq8O2bduwZ88eJCQkyOZza89+nzUaDUwmU0iPd0NdvarEz3dWuiXw1BmBihqB1inOgV2lwwzq3is8tTEqxMcCDYam2cu9qbDNAm8JMn11YRsVfjlj+ftEqRk92zvv2PkAAs/WKU3P4WSZ5+dQafcc0ziGp2QYeBJRyCh1QpVoOZiW0wFTpMeL9UVmZmZITjCU+H5S4j7LjZwuOIRbqL97Avk+kWvY1pIE85kI5XeSp3BJru+blnpBWa7tIbVIHj83//6ura2FwWBAamoqcnJyZPlbZr/PRqMxLMe7oaxelfvn29Ukklnpahz6tWmm9tYpzo9zGsNTALEe0ii1GshspcLxcwJl1QI6g4A21n0QaDILW5Wkr13OrTq2VQOwTFz06znXgae1y3msxrH61BO1SoWcDDWKzphRXiNQpxNI1LreN/sKUlZ4SoeBJ1GYKCFAkHoflTihSjQdTHs7YFL6DKD2pAiXRo8ejZ07d0p6gqHE95MS91mO5HTBIdzCcbLm7/dJSw6g5SLQz0Sov5M8hUtyfd/4E4gp4fjTV3JtD6lF8vi5+fd3fX09ACAhIQGAPH/L7PdZo9HILiD0l9wLRsaMGWP5w1gDnFwFHDcjS30ZAMus7acrzLj0QhcVknaBZ1qSCvU1llna3YlRq5CVrsbxc5YgsuS8QMf/b+/O46Oq7v+Pv+9M9g2SsCagaFJBgSpgaFEBERUQkUVbC7i07hX0W5dK1aq4lf7qRqv4VVS0VUhrXbCCwrcFpIhSIouKqDVBRdmEDCQEQtbz+yNmyCSTyayZJa/n48Gj9s6de8/kM8u973vOPV1bX7/ssFHjHpoHnolxlqo8THzUEHg2aO0+no2BZKeU1meWdyc321LJ971Hdzjq9YOebc9iT+AZPASeQDuIhgAhVG2MtglVYulg2tMBUzS8J33hy4l0ayd/oTjBiMb3UzS22RftdfIf6T00QikST9Y6cgAdKfz9TIT6O8nTd3+kvm+8/b1qj9/69gxUI7UeoRCu4+fm39/V1dWKj4931jQSf8uatrnxHp7h/s0JRFR0GKmtkDbOkCp3SftPUE7dAR0NPN0HiwcONfxvenJD0Fkpz0PabbaGofKNdu6vdwkmW26/9QmLumZY2luuVkPPbp2ODj3/el/LdWpqjQ5VNfy3rxMK9co62nv021KjH/R0v57LpEUMaQ8aAk+gHURDgBANbWwPsXQw7emAaf78+TFVb29PpD2d/EnBP8Hg3nCRJVQn/+7qGImhX3uJxJM1X8O2SPxsRjt/PxPt8Z3U2nd/JF+48Ob3KtTHdu198TSS6xEr3M1+vnz5cm3atEkOhyMif8uatrmkpCTks7S3h0juMLJ+/Xpp51INzdwlqV5Svbon7ZMlIyNLO93M1F5fb5yBXucmgaGnIe1xNikn82jA2VqQ2qgxUJVa9vCMj2sYHu8uzJQahp737mLTF7vqdeBQQ1ubBpuBzKCem+06U3trGu9TallSWpJPu4AHBJ6IWLF0shENAUI0tLE9xNrBdGsHTLFWb29PpD2d/F100UVBbxf3hossoTj591THSAv92lOknaz5OgQ4Ej+b0c7fIDyc30nRfuEi1L/17X2xPNrrES2af39fccUVEf9b1thmh8Ph9h6TCJ6ioiJp94caevbR4C/BXqsuiQe0typTuw+0nKm9vFKq/z4zbBpGehrS3tDDs2ng6XmWc3ezwDfdT2YnS1/va30bx3ZtCDwl6cOv6jTipKNRmcuEQqkem9FCTqYlS5KR55naG0PVjGTJ5sOES/CMwBMRKdZONqIhQIiGNraHjnIwHWv19vZEur2D3li/N1y0CUX926pjJIV+HZkvYVukfjZjgT9BeFvfSf5cIPf2OZHYW9kXof6tb+/f1GitR7R34oi0C1jwT1Dfh/EZkg67LOqZsld7qzJVXSt9s8/1fpsHmk9Y9L2ENnp4dk6Vc6h52z083e9DkuLsUmqipYxkq9UZ30/MtelfHzX89yvratQ726bjujcErmVNeo/6Otw8Md5S106Wvisz2rnfqK7etJhBvt4YHWy4Xa4yGM4eVASeiEixdrIRDQFCNLSxPUTrwbSvYrHe3hyQt3fQG8v3hotGoah/KOoY7SfHkcrbk/ZI/WxGkvZ8j3r6TvLnArmvz/H2fRPOz21r+w71b304Lp5GW/gWa504EJ2C/j7sfIqU/GHDPTxlk2RpQJfd+mj/CZKkdf+t1bFdj05l3loY6WlIu93WMDlQz0ybvvyuYZbzymqj5AT3gaDnHp4N/9ujs03llXVun98v16bT+9m19rM61dZJT/+zSrdNSlRKgqWPvj76nE4+zgAvSb2ybfqurGG735WZhnuT1h2R9r0v1ezXofquqjenSmLComAj8EREaTxgW7RokQ4dOqQePXpIiv6TjWgIEKKhje0l2g6m/RHMekdTOOPp5K+6ujok+4zVe8NFo1Cc/Ae7jpwch18kfzYjQTjeo619J/lzgby9b20R6s9tW/sO5bFdLF48DbZY68SB6BT096E9URo8r2GWdlMv1eVoSN6P9fdtUk2dVFRSpyk/MoqPawjv3IWRlhp6cbbG9v1jOZmWvvyu4b937zc6rrv7QNBlFviU5j08G/5/986WincfHV7flGVZuvi0eH1XZvTFrnqVV0p/fMOhytoEVVTHO9frku5H4JllaeO2hv/+trRePTOqpZKnpGqHJEtlh7pIOtVt2xEYAk9EjKYHbIcOHVJFRYWKi4uVn5+vmpqaqD/ZiIYAIRraiOAJRr1DcZIXygDV08mfw+EIyj68xYli+wvFyX+w69geJ8fRdJEiHNrjsxnNNYikAMef3rjhuLVFKLW172D91rt7v3KxvG30GEckCMn7MC5N6nOZpFqpwihZ0qDjqrW+uE6Hq6SPvq7TkLyGuMldD8+473twtrr57wPPnk1mOd+5v945zLy5xn2kJckZtDq39X0Pz3i7pex0m/aWu7+XZpzd0tWj6vXQa2Xae6ST9h4+esNOmyWd2d+u47p5SGlb0XTioh0Oo4L0978PO40ko31HOjsfz6j/WqrrLdmZuSgYCDzht2AfrDc9YOvevbtKSkp06NAhlZSUKDU1lSAAiEDBPslrj14ykRLsc6IYHsGuf7DrGOqTY3qQti3Un81or0EkBTj+9MaNlltbRMq+23q/Rspvari1dl5Ej3EEKhjn3MF8H+bl5bX62LAT7Fpf3BBOvv9f94FnYw/PuDaSKPv3IWVO5tHwsrWJi+rrjXMfze/fKR0d0i41zNa+t7z1/aZVrNMv+32ohz6+XJV1DaHjqdmf6PyTq9Ut7zTPjW5Fr6xmM7Xn7Jdkqbbe0j93/lhvf3uG8/HOtVukkqVS3nWEnkFA4Am/hOJgvfkBW15enrZt26ZBgwZp2rRpERMERHOvDCDYgn2iFUk9h9oDJ4qxIZh1DPXJcUf7jPkrlJ/N9q5B43FLSUmJ8vLyAj5uiaQAx5/euNFwa4tI2jffGW3zdF7EaA4EIljn3M3fh8YYde/eXV999ZXmz5/v0+/C2LFjW33sBzk2ZadZKq0w+vTbeu2vaJix/bOdLe+BmWBvbSsNGidwd52p3f2EQwePuJ8FvlHTwLNLuqV4u6WaulYmQarZrx7J+/XrgX/Wh44T1L9ziXql7pMST/bcYA86p0qpidKhqu9nao/PVHF5rhZtG6fdlV2c62UnHtAPs/4rVR9quL9n91F+7xMNCDzhl1Ac/DQ/YKutrVVqaqqmTZsWMQdU0d4rAwi2YJ9oRVLPISAcQn1yHGmfsY54EbE9a9D0uKWurk52uz3g45ZICnCa98bNysqSJD388MOtvp+i4dYWkbTvSPvOiERtnRcxmgP+CtY5d9Pvva+++kpLly7Vd999p+effz6o57M2y9KPT7Br6cZaGUkrt9Rqyzd1zhnI+3f9TkmObVKXYYq3e75Xpc1myWZJGclSSqJ0uKphSLs7niYsirPJZei8ZVmKtzfca9St+ExJRj2SS9Uj9/3GZ32/3D+WZSk3y6b/7qpX2WHpz1t/rP8U/9j5uE31GtVzvcb3XqMke40km1Sz3+/94SgCT/glFAc/kXQA3RqucgOugv25jaSeQ0A4hHo4dSR9xjrqRcT2rEHT4xa73a66urqAj1si7XYcjb1xfXk/RfqtLSJp35H0nRGp2jovYjSHZx3xwpe3gnnO3fg+nD9/vvbu3asuXbr4dT67bNkySa339GwMPCVpxce1zuXdk0p1+bGLpL2VUtmHisv/aZv7stss1RupZ2ebSvY0hIWHq4xSEl1DzQMV7meBl45OWOS6zMNOuwyTyj50TigkGSkhq2G5F+LtlhLjpYojrj1Ie2Vb+u+uhv/+T/HR5X3S92rqcf9Q79Q9TdY2AQWsOIrAE34JxcFPex8s+vPjylVuwFWwP7fRcOEDCLVQnhxH0meso15EbM8aND1uqa2tDdpxSyQGOOF+P4Xzb9JRvjN80Z4hGqGw/zrqhS9vReI9h0tKSjw+np1uU98cmz7febQ3ZkZ8hWae+FelxR9uWFDtUPy+dyRd7nFbNuewdksl3+eBu/bXK6+Ha2LpsYenm3DTY+BpT2q4f+a+9xt6WcZnNoSdXt5P85guNpUdNi0Cz4aJi452K02Mky4oiNfIvl1l+7JWqrbkT8AKzwg84ZdQHfy018Givz+uHNAALQXzcxtpPYcQGvTmCJ9I+ox11IuI7VmDpsctdrs9po9bOur7KdQi6TvDW+0dokVrKBwJwn2hItJFwz2H3Q1MH9bX7gw8E+21mnHi35WdVObyrPjafW1u226XVCvlNJmpfdd+o7weR9epqTXatudouNq8h2e8m3DTbvs+WGx1x0l+3T8zMc5S72yrRdgpSSfm2hVvr1FNnTSgt00/OyNeWWk2SXEBBazwjMATfonGg5+m/P1x5YAm8ngKTghVolMk9hxC8MR6b45o+N6JlM9YR76I2LQG/r5nvHle0+OWxnt4RvtxS7TPhh0N3xHNRcp3hrfaO0SL9vOicOJChWfRcM/h+LiW4eGg4+wqKq7Trv1Glw78r3prd7NnGcWlZLW57bjv5yvq2WSm9sb7eFbXGr37WZ3++WGNyg4ffU5WWttD2t2FoMFwXDebbDbL2e6mOqdauvsniTp0ROrdxXK5r6i/ASvaRuAJv0XbwU9T/v64ckATWTwFJ5JiOlQBvBGJJ9ax3Jsj1sPcYOMiov/vGW+f1/S4JViztIdTtM+GzXdE+whHiBbN50Xh0Hh8smHDBlVWVqqyslLJyckRe6EinEJ9z2FvJnvzJCm+5bJ4u6UZYxs+f6rrL5WsaXFPzPics9rctu374DCn2UztJXvq9MKqGpUedA1aT+trV9cMb3p4trlrn6UkWs5g1t5KoJqdblN2evD3jdYReKJDCqQXQKgPaCIxoIhUnoITSTEbqgDeiNQT61juzRHLYW4ocBHR//eML89rPG5xOBzOE9toFe2zYfMd0T6ipbdvR9L0/CYrK0vLly/Xpk2bVFdXpyNHjqikpESdO3eWzWaLuAsVscifyd5ak5zgebb11u6JGZ9Q3ea2474fep6ebCktSao4IpXsqdejb1bLNMk6T+lj07hB8erdpWWS6fM9PP2U193m7LXprocnwoPAEx1SpPYCiNSAIlK1FZzEaqgCeCNST6xj+UQ0lsPcUOnovaL8fc901PdatM+G3VHr1t4i9Ti/o2p+flNZWakjR44oLy9PycnJys7O1s6dO1VQUKAJEyZE3IWKWObPsWJBQYHL/09K+P4/6o60fh9KN0O24+xtB562JsFhz0ybvthVr9qj8/4or7tNF58er17ZrSeM8e5mabe1EdL6KCXRUteMo21wN4we4UHgiQ4pUnuVRGpAEanaCk5iNVSJZPRQDr/GGixatEiHDh1Sjx4Nd3aPlBPrWD4RjeUwF6Hh73smkPdaqL+nQ7n9aP+MRXv7o0WkHud3VM3Pb7Zv366DBw+qoqJCycnJSklJUXJysoYMGcL5Tjvz5yLM0KFDXf5/crzVEHaWPOU6bL3sw4aena1MvpPgRRLVtKdkTqalL3Y1/LfNksYPjtO5p8R9PwGRh220Qw/PlGa9XEMxZB7+IfBEhxWJvQC48u+btoKTUIcqhHuu6KEcfk1rcOjQIVVUVKi4uFj5+fmqqanx68S6+fv8ggsu0D/+8Q+/3/exfCIay2EuQsPf94y/zwv193Sotx/tn7Fob380icTj/I6q+flNcnKyJKmyslKSCP7DKBgXYRLjJVvpe6qvdqhh8qLvx5pXOxp6fLqZjCfO5l0vyKbB4en94rT5qzplJFuaekaC+nTzLlVsj8CzeXhL4Bk5CDyBCMKVf9+0ddPtp59+OqBgxhPCvZbooRx+TWvQvXt3lZSU6NChQyopKVFqaqrPJ9bN3+fGGN19993OexT5+76P1RPRWA5zI1k0X3zy9z3j7/NC/T0d6u1H+2cs2tsP+KP5+U1qaqri4+NVXV2tHTt2EPyHkT8XYQoLCyVJU6dOldRwLJhYv0+Vaj5bu9UwvN2NxHjvhnw3DQ57Zdv0u2lJrrObe8HdpEXBvsdm832E4h6h8A+BJxBBuPLvu2DedNsXhHst0UM5/JrXIC8vT9u2bdOgQYM0bdo0n0+sm7/Pd+3apd27d6tHjx7q2bMn73s3Yi3MjfQw0d13/xtvvKExY8a4TNLjcDgisv2S/+8Zf54X6u/p9vgdiPbPWLS3H/CVu/Ob0aNHO7+nI/W7uSPw5yKMw+FosSwpJUOVMs2WmoZ7ebqR6GZmd3eaD1f3NeyUWrmHZ5DvsRkf13xIO/fwjBQEnkAE4cq//9o7gCTca4keyuHXvAa1tbVKTU3VtGnT/PocNH+fm++nxGz8X9737SNcoWM09GRv/t1/+PBhrVixQmvXrlViYqLKysokyTn7b6S1v72F+nua3wEAzXF+E9mCcREmudeZ2r/7M9d7eCZkNUxc5EaSHz08/eWut2Wwh5wzpD1yEXgCAQjFSShX/v3T3gEkJ3Ut0UM5/IJdg+bv86ZD2SXp8OHDqqys1IYNGzR//nxOYEIgnKFjNPRkb/7df+jQIdXU1CgjI0NJSUnOniiJiYnKyMiIuPa3t1B/T/M7AMAdzm9iW1JSSsMERa3N0t6M9z08A29be9zDs/mQdnfD6APR/GYB8B6BJ+CnaOj50pGEKoBsLdTmpK4lruCHX7Br0Px9boxR165dVV9fr2+++cbZe66oqEgbNmzgOzAEwhk6RkNP9ubf/Y2TYCQnJ6umpsa5Xk1NTUS2v72F6nu66W/l6NGjNXr0aIaqAkAHkZxgNYSbbiYocicxrn16eFpqZUh70O/hGdpZ2pPiLaUlWdp7sD64G+4ACDwBP0VDz5eOJBQBZFuhNuFeS1zBD79g1sDd+7xxlvY333xTa9asUW5urpKTk/kODJG2QsdQDnePhp7szb/7q6urFR8fr7S0NFVUVDjXi4+Pj8j2h0Owv6fd/VYWFBRw8QMAOghvJyFqlJQgqbrt9QINDlu7V6fNZslmSfVB6jYZ6iHtcXbp+B427TtYT09PHxF4An6Khp4vHUkoAsi2Qm3CPXQE7t7n11xzjXbu3KkPPvhAycnJkvgODBVPoWOoRxpEQ0/25t/9WVlZWr58uTZt2qS6ujrZ7Q3jyqqqqlRaWhpx7Y8FXAAOnsYLGCUlJcrLy/P7OCbSJxtrS7S3H4hlY8aMabEsOcG3bSTEWar2KvAMbPIfT0PL4+yWqmuDEx/GN0vVLMtSnE2qDVKHzDi7lJpoqWemTTv308vTFwSegJ+ioedLRxPsAJJQOzCcsMQ2vgPbh6fQMdRBU7T0ZG/+3X/FFVe4BKBSZM/SHu34rQyOphcwGsN6fy5gRPstl9q7/RyrAL7Jz89vsSwx3rcek0nxXnXwDEIPT8+PVdcGtn1Jslvug1m7zVJtkLqQxn2//eO62bTnAIGnLwg8AT+11fOFA6joR6Djv2g/4ULboqH3Xyxw14NRkh5++GFt2LBB9fX1AQdNnn6vorEne3u2md96fiuDpekFDLvdrrq6Or8uYER7j9v2bD/HKkDwJCVYOlz1fcBXd6TVCYzibK0PNW8upIFnkIadx7dyP1K7XVIQAlXp6OtIjLfUuwtTwPuCwBPwk6eeLxxAxQYCHf9F+wkX2hYtvf9iQWOA1/y3pbKyUkeOHFF2drbzPqq+Bk38XvmPv10DfiuDo2lP2draWr8vYLRHj9tQBv3t2WOYYxXAd/PmzZMkzZgxw2V5crx0uEoNYWfJU1K1Q875xcs+bJjF3Z7k0/0+Aw083U1Y1ChYM7U3v3+nc/tBzCWbtvWYLjaVV3InT28ReAIBaK0XSTAPoNwdVKJ9EOj4r71OWOhdFV7R2PsvmjX/bTl8+LC2bdumHTt2KDk52a+gqb1P+GPhM9v4Gpi4q0Fbv5WxUPP20LSnrN1u97unbKh73IY66G/PHsPcjgEInqT478PNfe9/H3aa7/+p4f/ve1/qPkqJ8d5vM5Q9PBuGoQceHLZ2n9BgTlzU9HXE2S1lpQV2b9OOhMATCIFgHUC1dlD5+OOPO4c1IrQIdPwTqhOWpifOTScn6ci9q9BxNP9tSUlJUadOnTR06FANGTLErzCpPU/4Y6FHZNPXUF5ersrKSu3YsUPHH398RIUl7X2xtLXfylioeXtp2lO28R6e/vSUDXWP21BcJGn+2z5o0CCX3/ZQ9RjmdgxA8CQlfB/C1eyXs2enk/X98sZg1DuB9/D07zFfJLQypD0uSIHq0W3BHwSeQAgE6wCqtYPKV199VTfddFOIWg8ELhQnXK0N583Ly+vQvavQcbj7bbHb7ZowYYLf7/n2POGPheGjTV9DfHy8du7cqUOHDqm0tFQZGRkREZZE0sXSWKh5MHjTy7VpT9lAZmkP9eiUYF8kcfd+HTRokH7zm9+EfLIxbscABE9S40zt8ZlqGfSZ75fLpx6elmXJbkl1fuaGnoa0B6sHZvMZ2p3bD1KgKgVv+H1HROCJmBauYVTBOoBq7aBy9+7doWg2EDShOOFqfuK8fft2HTx4UBUVFUpOTo6o3lVAKITi5Lw9T/hjYfho09eQlZWlsrIyHTx4UA6HQzU1NRERlkTSxdJYqHmgfOnl2thT1uFwBBROh3J0SrAvkrh7v27atEkTJ07U7NmzQ3osz62LgOBJbuy52WVYwz07m97DMyGrYbmkxFZ6RLbGZrNU52fi2dYs7cHQWqgaqnt4wjcEnohZ4RhG1fSgbPTo0Ro9enRAV6dbO6js0aNHSNqPtnEvMu8F+4Sr+YlzcnKyJKmyslKSGIqGmBeKk/P2POGPheGjzV9DTk6Odu7cqeHDh2vChAkR8ZsQSRdLY6HmgYq1Xq7BvkjiKRRvj2N5bl0EBIezh6c9qWGColZmaXeu56U4m1RT51+bPM/SHpxh4q1OWhTMHp5MzO43Ak/ELHcHmP/5z3903XXXKT8/P+gnde4OygoKCgI6KGvtoPLCCy8MSpvhG+5FFl7NT5xTU1MVHx+v6upq7dixg6Fo6BBCcXLeXif8sTB81N1rGDlyZET9DkTSxdJYqHmgYq2Xa7AvkngKxWMtLAZiwdSpU90uT4izFGeTauvVEG52H+V2PV9maZe+Hxpe42Mjv9ces7S3PmlR8O67GefhdcAzAk/ErOYHmHFxcTpw4IDeeOMNpaWlBT2sCsVBWWsHldXV1QG3F77jwDs8GnvVfvXVV+ratau+++47SQ339Rk9erTGjBkT8vt8AQhcLAwfjYbXEEkXS6Ph7xVqsdjLNZgXSTyF4g8//HBIwmJG6wD+83S7jcR4S7VVnoefJ/qYQAWS9bXLkPZWhuiHapZ2+IbAEzGr+QHmd999p5qaGmVnZ6tnz55BD6tCdQXf3UGlw+EIaJvwT6z10ogGzXvVGmPUvXt3jR8/Xn369OEkJQa19+zScBXqICAWho9G+muItIulkf73CjV6uXrmKRQPRVjMaB0gMI3noe6Cz+QES4c8BJ7xdsvn3oqBTP7jaSb2YA0Tb5ch7QSefiPwRMxqfoBZUVGh+Ph4de/eXVLww6pYvIIPV9S4/bnrVbt371716dOnQ59Ax6pIml26IyIIiB1cLI0c9HJtW2uheCjCYkbrAIEpLCyUJM2YMaPFY0ltzMDeWjjoid36fuIjP3ju4RmcYeKtD2kPyuYlcQ/PQBB4ImY1P8AsLi7WihUrVFNTE5Kwiiv4sY8atz961XYskTS7dEdEEACERkfv5eqvUITFHFcAoZOc4DlEbCsQdcffHp42y/N9NIMRSNqs1oPTYIWUcbaG23jBPwSeiGlNDzBbm1QoWGEVV/BjHzVuf/Sq7VgiaXbpjoggAECkCXZYzHEFEDptzcDu64RFkv/BoacJi6TgDBP3PClScELKYE5+1BEReKLDaI+wiiv4sY8aty961XYskTS7dEdEEAAg1nFcAYROpxTP4VyiHz08UxL9C/zaCjSD0QPT0xD9YA1p5/6dgfGrDMYYPfPMMzr11FOVnJysTp06afjw4XrxxRfdrr9nzx7NnDlT+fn5Sk5O1vHHH69Zs2bp4MGDbtevr6/XggULdOqppyojI0NdunTRpEmTtGHDhlbbtHnzZk2ePFk5OTlKSUnRoEGD9NRTT8kY9/d7KC8v129/+1v169dPycnJ6tWrl375y186Z/9FbGoMq2bPnq1rrrmGnnlAhGu8UDF79mxde+21mj17NvcTjDEVFRWaP3++Zs+erSNHjmjQoEEqLS3Vjh07VFpaGrbZpTuiadOmqaCgoMXfnyAAQKzguAIInYQ4S2lJrQeUSX708ExJ9K8tniYskiSbzQpoBnhJivcQeAZtFngCz4D41cPzhhtu0Lx585SSkqIRI0bIZrNpzZo1uuyyy7Rq1SotWLDAue6uXbs0bNgwff311xo4cKDGjx+voqIi/eEPf9Dbb7+td999VxkZGS7b/+Uvf6n58+crMzNTZ599tvbs2aM33nhDb731lpYsWaJzzz3XZf1Vq1Zp3Lhxqqmp0fDhw9W5c2etXLlSv/zlL7Vu3Tq98MILLusfPHhQo0aN0saNG5WXl6fzzz9fH3/8sZ566im9+eabWrdunXr16uXPnwYAEGT0qo1d7m41MmjQIP3mN7+Rw+EI++zSHQ237QAQqyoqKvhuA9pBdpqliiPuO53508Mz1c8enm0NaZcku91SXa1/EyK1tY/g9fBkSHsgfA483377bc2bN0+9e/fW2rVr1bt3b0nSN998o9NPP13PP/+8fvKTn2jcuHGSGmbv+vrrr3XHHXfowQcflCRVV1fr0ksv1csvv6y77rpLf/zjH53b/8c//qH58+dr4MCBWrVqlbKzsyVJr776qi6++GL9/Oc/V3FxsVJSUiRJVVVVmj59umpra7VkyRLnfnft2qWzzjpLf/7znzVx4kRNnjzZuY977rlHGzdu1GWXXabnnntOcXFxqq+v16233qrHHntMM2bM0BtvvOHP3xMAAHjJ3SQ5mzZt0sSJEzV79mzneswu3X64wAAg1ri7uLZ06VJ6dgJ+cDc7e1NZaZa+3uf+MX/u4ZkYbynOJtXW+/Y8b3pYxtml6lqfm+TkeUi7Jf/nl2+6nQA30MH5/Od76aWXJEn33XefM+yUpN69e2vmzJmSGkJRSSouLtbixYvVu3dv3Xvvvc51ExISNH/+fGVkZOiZZ55RRUWF87GHH35YkvTQQw85w05JuvDCCzV9+nTt2rVLf/3rX53LFy5cqF27drmErJLUs2dPPfnkk5Kkxx57zLm8vLxc8+fPV0pKiubOnau4uIZ3qc1m00MPPaTjjz9e//jHP1RSUuLrnwYAAPiASXIAAKHW9OJabm6usrOzVVRUpEWLFoW7aUDM6ZTS+lDxRD9nkPHnPp5eBZ4BholtDTcPRu9M7uEZGJ9L/MILL+iTTz7RT37ykxaPNQaXjSHi22+/LWOMxo8f71zWqFOnTho1apQqKyu1cuVKSVJZWZnWrl2rtLQ0jR49usX2G3tpLlmyxLls6dKlkqRJkya1WH/kyJHKzMzUu+++qwMHDkiSVq9erUOHDmnEiBHKzMx0Wd9ut2vChAku2wUAAKHRdJIcSUySAwAIOi6uAcFTXFys4uLiVh+32Sx1Tm0Z9MXbLb8DwJAFngGGiQlxntsVjN6Z3MMzMD6XID4+XieddJJSU1Ndlr///vuaN2+e7Ha7pk+fLkn65JNPJEkDBgxwu62TTjpJkvTxxx9Lkj799FPV19erX79+LQJSd+u3tQ+bzaZ+/frJGONcz9c2AQCA0GCSHACxqumEbPPnz3cZ0Yb2xcU1IHiWL1+u5cuXe1wnK61lzORp+HdbUhJ8f45X9/C0BdYD09OkRQ3bD2jz32+De3gGIoC3XYNp06bp008/1ebNm5Wdna3CwkINGTJEkpxXzXr27On2uY3L9+zZ49f67bWP5qqqqpw/mI0SExOdVw0BAEDbmCQHQCzinpGRZdq0aVq6dKlLPbi4BoROVrol7XZdluTHhEWNUj3M/N4ab3pGBtp7sq1QNdAh8xJD2gMVUOBZWlqqwsJC5/+3LEsff/yxpkyZIrvdrkOHDkmSc4Kh5pKTkyUdHQrv7fqN6/nyHF/34ekq7Jw5c1zuSSpJs2bN0m233dbqc6LR/v37w90EtILaRA9qFT2oVfhcdNFFzv+urq5uMUkRtYke1CpyUZv28+c//1n/+c9/lJmZ6ZyQ7T//+Y+eeeYZXX755W0+n1oF3+OPP65XX31Vu3fvVo8ePXThhRe6/b3xFbWKLNQj9BrzlLY+O9WHanWkyQzoXRPtcjiOJoC+1OpIlVF5mW+zCx1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\n",
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       "text/plain": [
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@@ -481,7 +466,7 @@
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-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {
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@@ -496,14 +481,6 @@
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     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/towinazure/edwinnglabs/orbit/orbit/utils/dataset.py:112: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
-      "  df[\"date\"] = pd.date_range(\n"
-     ]
-    },
     {
      "data": {
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@@ -568,7 +545,7 @@
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-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -590,7 +567,7 @@
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-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {
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@@ -604,7 +581,7 @@
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+   "execution_count": 10,
    "metadata": {
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@@ -638,7 +615,7 @@
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   {
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+   "execution_count": 11,
    "metadata": {
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@@ -655,7 +632,7 @@
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    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {
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@@ -677,7 +654,7 @@
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+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
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@@ -691,7 +668,7 @@
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-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {
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@@ -702,7 +679,7 @@
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        "version_major": 2,
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@@ -717,38 +694,38 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "20:28:01 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:01 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:03 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:03 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:05 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:05 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:07 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:07 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:09 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:09 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:11 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:12 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:14 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:14 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:16 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:16 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:18 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:18 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:20 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:20 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:22 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:22 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:24 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:25 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:27 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:27 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:29 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:29 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:31 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:31 - cmdstanpy - INFO - Chain [1] done processing\n",
-      "20:28:33 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:28:34 - cmdstanpy - INFO - Chain [1] done processing\n"
+      "2024-01-21 15:15:33 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.006737946999085467, 'slope_sm_input': 0.006737946999085467}\n",
+      "2024-01-21 15:15:33 - orbit - INFO - tuning metric:-5.0761e+05\n",
+      "2024-01-21 15:15:33 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.006737946999085467, 'slope_sm_input': 0.01831563888873418}\n",
+      "2024-01-21 15:15:34 - orbit - INFO - tuning metric:-5.0791e+05\n",
+      "2024-01-21 15:15:34 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.006737946999085467, 'slope_sm_input': 0.049787068367863944}\n",
+      "2024-01-21 15:15:34 - orbit - INFO - tuning metric:-5.086e+05\n",
+      "2024-01-21 15:15:34 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.006737946999085467, 'slope_sm_input': 0.1353352832366127}\n",
+      "2024-01-21 15:15:35 - orbit - INFO - tuning metric:-5.099e+05\n",
+      "2024-01-21 15:15:35 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.01831563888873418, 'slope_sm_input': 0.006737946999085467}\n",
+      "2024-01-21 15:15:35 - orbit - INFO - tuning metric:-5.1169e+05\n",
+      "2024-01-21 15:15:35 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.01831563888873418, 'slope_sm_input': 0.01831563888873418}\n",
+      "2024-01-21 15:15:36 - orbit - INFO - tuning metric:-5.1168e+05\n",
+      "2024-01-21 15:15:36 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.01831563888873418, 'slope_sm_input': 0.049787068367863944}\n",
+      "2024-01-21 15:15:36 - orbit - INFO - tuning metric:-5.1156e+05\n",
+      "2024-01-21 15:15:36 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.01831563888873418, 'slope_sm_input': 0.1353352832366127}\n",
+      "2024-01-21 15:15:36 - orbit - INFO - tuning metric:-5.1085e+05\n",
+      "2024-01-21 15:15:36 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.049787068367863944, 'slope_sm_input': 0.006737946999085467}\n",
+      "2024-01-21 15:15:37 - orbit - INFO - tuning metric:-5.0634e+05\n",
+      "2024-01-21 15:15:37 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.049787068367863944, 'slope_sm_input': 0.01831563888873418}\n",
+      "2024-01-21 15:15:38 - orbit - INFO - tuning metric:-5.0571e+05\n",
+      "2024-01-21 15:15:38 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.049787068367863944, 'slope_sm_input': 0.049787068367863944}\n",
+      "2024-01-21 15:15:38 - orbit - INFO - tuning metric:-5.0378e+05\n",
+      "2024-01-21 15:15:38 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.049787068367863944, 'slope_sm_input': 0.1353352832366127}\n",
+      "2024-01-21 15:15:38 - orbit - INFO - tuning metric:-4.9828e+05\n",
+      "2024-01-21 15:15:38 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.1353352832366127, 'slope_sm_input': 0.006737946999085467}\n",
+      "2024-01-21 15:15:39 - orbit - INFO - tuning metric:-4.8061e+05\n",
+      "2024-01-21 15:15:39 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.1353352832366127, 'slope_sm_input': 0.01831563888873418}\n",
+      "2024-01-21 15:15:39 - orbit - INFO - tuning metric:-4.7859e+05\n",
+      "2024-01-21 15:15:39 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.1353352832366127, 'slope_sm_input': 0.049787068367863944}\n",
+      "2024-01-21 15:15:40 - orbit - INFO - tuning metric:-4.727e+05\n",
+      "2024-01-21 15:15:40 - orbit - INFO - tuning hyper-params {'level_sm_input': 0.1353352832366127, 'slope_sm_input': 0.1353352832366127}\n",
+      "2024-01-21 15:15:41 - orbit - INFO - tuning metric:-4.5657e+05\n"
      ]
     }
    ],
@@ -765,7 +742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-04-27T18:04:39.737192Z",
@@ -804,19 +781,19 @@
        "      <th>0</th>\n",
        "      <td>0.006738</td>\n",
        "      <td>0.006738</td>\n",
-       "      <td>-507607.884170</td>\n",
+       "      <td>-507607.881617</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>0.006738</td>\n",
        "      <td>0.018316</td>\n",
-       "      <td>-507905.255722</td>\n",
+       "      <td>-507905.255726</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0.006738</td>\n",
        "      <td>0.049787</td>\n",
-       "      <td>-508597.167150</td>\n",
+       "      <td>-508597.166761</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -828,7 +805,7 @@
        "      <th>4</th>\n",
        "      <td>0.018316</td>\n",
        "      <td>0.006738</td>\n",
-       "      <td>-511692.191066</td>\n",
+       "      <td>-511692.181136</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -836,14 +813,14 @@
       ],
       "text/plain": [
        "   level_sm_input  slope_sm_input        metrics\n",
-       "0        0.006738        0.006738 -507607.884170\n",
-       "1        0.006738        0.018316 -507905.255722\n",
-       "2        0.006738        0.049787 -508597.167150\n",
+       "0        0.006738        0.006738 -507607.881617\n",
+       "1        0.006738        0.018316 -507905.255726\n",
+       "2        0.006738        0.049787 -508597.166761\n",
        "3        0.006738        0.135335 -509900.493056\n",
-       "4        0.018316        0.006738 -511692.191066"
+       "4        0.018316        0.006738 -511692.181136"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -854,7 +831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-04-27T18:04:39.779752Z",
@@ -869,7 +846,7 @@
        "  'slope_sm_input': 0.006737946999085467}]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -887,7 +864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-04-27T18:12:40.667845Z",
@@ -900,14 +877,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "20:28:36 - cmdstanpy - INFO - Requested 8 parallel_chains but only 4 required, will run all chains in parallel.\n",
-      "20:28:36 - cmdstanpy - INFO - CmdStan start processing\n"
+      "2024-01-21 15:15:41 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 125 and samples(per chain): 125.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3577b9d384ce4a6ea8a7a779d4f1a5b9",
+       "model_id": "56722770ee1749538a417fbf4d3fc959",
        "version_major": 2,
        "version_minor": 0
       },
@@ -921,7 +897,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e2f5b156d3b745e68cbf21fc31b7164c",
+       "model_id": "07c3827b0e124b258c0ebd6880aefda6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -935,7 +911,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4e3b7c13d1c14475b5128e37dbc677fb",
+       "model_id": "1b3b9e87b9cd4cfdab7b23bad0db1568",
        "version_major": 2,
        "version_minor": 0
       },
@@ -949,7 +925,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a18149eed6694b66b8ae14fe569ef409",
+       "model_id": "cbd69791c0d64977bcbd262dbfc7a7e7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -964,52 +940,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                                                                                                                                                                                                                                                                                                                                "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "20:31:21 - cmdstanpy - INFO - CmdStan done processing.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
+      "                                                                                                                                                                                                                                                                                                                                \n",
+      "CPU times: user 2.69 s, sys: 159 ms, total: 2.85 s\n",
+      "Wall time: 2min 50s\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "20:31:21 - cmdstanpy - WARNING - Some chains may have failed to converge.\n",
-      "\tChain 1 had 71 iterations at max treedepth (56.8%)\n",
-      "\tChain 2 had 80 iterations at max treedepth (64.0%)\n",
-      "\tChain 3 had 77 iterations at max treedepth (61.6%)\n",
-      "\tChain 4 had 60 iterations at max treedepth (48.0%)\n",
-      "\tUse function \"diagnose()\" to see further information.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 3.09 s, sys: 195 ms, total: 3.29 s\n",
-      "Wall time: 2min 48s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x17554fbe0>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -1037,7 +971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-04-27T18:12:41.462555Z",
@@ -1047,7 +981,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -1087,9 +1021,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "orbit39-cmdstan",
+   "display_name": "orbit39",
    "language": "python",
-   "name": "orbit39-cmdstan"
+   "name": "orbit39"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1101,7 +1035,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.9.18"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/examples/eda_orbit_style.ipynb b/examples/eda_orbit_style.ipynb
index 423a6ccd..4d5cf162 100644
--- a/examples/eda_orbit_style.ipynb
+++ b/examples/eda_orbit_style.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "traditional-madison",
    "metadata": {
     "ExecuteTime": {
@@ -22,7 +22,7 @@
     "import numpy as np\n",
     "from orbit.models import LGT, DLT\n",
     "import arviz as az\n",
-    "from orbit.diagnostics.plot import plot_param_diagnostics, plot_predicted_data\n",
+    "from orbit.diagnostics.plot import plot_predicted_data\n",
     "from orbit.utils.plot import get_orbit_style\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "traditional-meditation",
    "metadata": {
     "ExecuteTime": {
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "coated-cabinet",
    "metadata": {
     "ExecuteTime": {
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "purple-region",
    "metadata": {
     "ExecuteTime": {
@@ -124,7 +124,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "biblical-lunch",
    "metadata": {
     "ExecuteTime": {
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "compatible-stable",
    "metadata": {
     "ExecuteTime": {
@@ -261,7 +261,7 @@
        "4  0.487404  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "inappropriate-greece",
    "metadata": {
     "ExecuteTime": {
@@ -290,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "figured-profile",
    "metadata": {
     "ExecuteTime": {
@@ -308,16 +308,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "expressed-process",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-09-03T00:52:02.687545Z",
      "start_time": "2021-09-03T00:51:56.794456Z"
     },
-    "jupyter": {
-     "outputs_hidden": true
-    },
     "tags": []
    },
    "outputs": [
@@ -325,9 +322,81 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
+      "2024-01-21 17:25:15 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7638fd67284747399c1493034f3e1447",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "chain 1 |          | 00:00 Status"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d67b20a156a944d3991d428be9898ea1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "chain 2 |          | 00:00 Status"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba5648673c6141418c5cb18f29256cec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "chain 3 |          | 00:00 Status"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "768a0f25d0a743a4834667f4c8085d55",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "chain 4 |          | 00:00 Status"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                                                                                                                                                                                                                                                                                                \n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x17f68b9d0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -336,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "pretty-restaurant",
    "metadata": {
     "ExecuteTime": {
@@ -385,15 +454,15 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>2010-01-03</td>\n",
-       "      <td>13.282945</td>\n",
-       "      <td>13.387017</td>\n",
-       "      <td>13.496885</td>\n",
-       "      <td>12.974152</td>\n",
-       "      <td>13.077699</td>\n",
-       "      <td>13.171040</td>\n",
-       "      <td>0.272877</td>\n",
-       "      <td>0.306664</td>\n",
-       "      <td>0.339836</td>\n",
+       "      <td>13.280258</td>\n",
+       "      <td>13.386934</td>\n",
+       "      <td>13.494152</td>\n",
+       "      <td>12.971734</td>\n",
+       "      <td>13.065477</td>\n",
+       "      <td>13.169708</td>\n",
+       "      <td>0.285983</td>\n",
+       "      <td>0.313343</td>\n",
+       "      <td>0.357919</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -401,15 +470,15 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2010-01-10</td>\n",
-       "      <td>13.512545</td>\n",
-       "      <td>13.613519</td>\n",
-       "      <td>13.719359</td>\n",
-       "      <td>12.989363</td>\n",
-       "      <td>13.082902</td>\n",
-       "      <td>13.160715</td>\n",
-       "      <td>0.457050</td>\n",
-       "      <td>0.541612</td>\n",
-       "      <td>0.613547</td>\n",
+       "      <td>13.505674</td>\n",
+       "      <td>13.620594</td>\n",
+       "      <td>13.740260</td>\n",
+       "      <td>12.959251</td>\n",
+       "      <td>13.072118</td>\n",
+       "      <td>13.150729</td>\n",
+       "      <td>0.454911</td>\n",
+       "      <td>0.555630</td>\n",
+       "      <td>0.630481</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -417,15 +486,15 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2010-01-17</td>\n",
-       "      <td>13.261676</td>\n",
-       "      <td>13.376100</td>\n",
-       "      <td>13.481059</td>\n",
-       "      <td>12.980930</td>\n",
-       "      <td>13.069294</td>\n",
-       "      <td>13.198988</td>\n",
-       "      <td>0.233724</td>\n",
-       "      <td>0.303712</td>\n",
-       "      <td>0.382880</td>\n",
+       "      <td>13.239633</td>\n",
+       "      <td>13.376727</td>\n",
+       "      <td>13.491780</td>\n",
+       "      <td>12.982969</td>\n",
+       "      <td>13.064959</td>\n",
+       "      <td>13.188750</td>\n",
+       "      <td>0.197720</td>\n",
+       "      <td>0.297731</td>\n",
+       "      <td>0.384834</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -433,15 +502,15 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>2010-01-24</td>\n",
-       "      <td>12.997246</td>\n",
-       "      <td>13.161258</td>\n",
-       "      <td>13.373597</td>\n",
-       "      <td>12.933521</td>\n",
-       "      <td>13.079675</td>\n",
-       "      <td>13.184074</td>\n",
-       "      <td>-0.043989</td>\n",
-       "      <td>0.085492</td>\n",
-       "      <td>0.329129</td>\n",
+       "      <td>13.001890</td>\n",
+       "      <td>13.151968</td>\n",
+       "      <td>13.271914</td>\n",
+       "      <td>12.938928</td>\n",
+       "      <td>13.064210</td>\n",
+       "      <td>13.150783</td>\n",
+       "      <td>-0.010906</td>\n",
+       "      <td>0.080342</td>\n",
+       "      <td>0.147278</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -449,15 +518,15 @@
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>2010-01-31</td>\n",
-       "      <td>13.053523</td>\n",
-       "      <td>13.187187</td>\n",
-       "      <td>13.280910</td>\n",
-       "      <td>12.965601</td>\n",
-       "      <td>13.068511</td>\n",
-       "      <td>13.163614</td>\n",
-       "      <td>0.038108</td>\n",
-       "      <td>0.109961</td>\n",
-       "      <td>0.183865</td>\n",
+       "      <td>13.039371</td>\n",
+       "      <td>13.182339</td>\n",
+       "      <td>13.298785</td>\n",
+       "      <td>12.956090</td>\n",
+       "      <td>13.070954</td>\n",
+       "      <td>13.154630</td>\n",
+       "      <td>0.005171</td>\n",
+       "      <td>0.107658</td>\n",
+       "      <td>0.201739</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -481,15 +550,15 @@
        "    <tr>\n",
        "      <th>438</th>\n",
        "      <td>2018-05-27</td>\n",
-       "      <td>12.128130</td>\n",
-       "      <td>12.225532</td>\n",
-       "      <td>12.349437</td>\n",
-       "      <td>12.161564</td>\n",
-       "      <td>12.257416</td>\n",
-       "      <td>12.386220</td>\n",
-       "      <td>-0.056662</td>\n",
-       "      <td>-0.024462</td>\n",
-       "      <td>0.000079</td>\n",
+       "      <td>12.105644</td>\n",
+       "      <td>12.232086</td>\n",
+       "      <td>12.315658</td>\n",
+       "      <td>12.117114</td>\n",
+       "      <td>12.257510</td>\n",
+       "      <td>12.337148</td>\n",
+       "      <td>-0.054384</td>\n",
+       "      <td>-0.026241</td>\n",
+       "      <td>0.000361</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -497,15 +566,15 @@
        "    <tr>\n",
        "      <th>439</th>\n",
        "      <td>2018-06-03</td>\n",
-       "      <td>12.053329</td>\n",
-       "      <td>12.164517</td>\n",
-       "      <td>12.298734</td>\n",
-       "      <td>12.140461</td>\n",
-       "      <td>12.256120</td>\n",
-       "      <td>12.393392</td>\n",
-       "      <td>-0.115286</td>\n",
-       "      <td>-0.083959</td>\n",
-       "      <td>-0.058784</td>\n",
+       "      <td>12.040424</td>\n",
+       "      <td>12.164513</td>\n",
+       "      <td>12.281393</td>\n",
+       "      <td>12.125013</td>\n",
+       "      <td>12.248700</td>\n",
+       "      <td>12.375470</td>\n",
+       "      <td>-0.115741</td>\n",
+       "      <td>-0.086977</td>\n",
+       "      <td>-0.063800</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -513,15 +582,15 @@
        "    <tr>\n",
        "      <th>440</th>\n",
        "      <td>2018-06-10</td>\n",
-       "      <td>12.164289</td>\n",
-       "      <td>12.253961</td>\n",
-       "      <td>12.365318</td>\n",
-       "      <td>12.131554</td>\n",
-       "      <td>12.239526</td>\n",
-       "      <td>12.357624</td>\n",
-       "      <td>-0.023095</td>\n",
-       "      <td>0.011267</td>\n",
-       "      <td>0.037542</td>\n",
+       "      <td>12.123452</td>\n",
+       "      <td>12.243904</td>\n",
+       "      <td>12.379812</td>\n",
+       "      <td>12.103380</td>\n",
+       "      <td>12.237064</td>\n",
+       "      <td>12.376783</td>\n",
+       "      <td>-0.017700</td>\n",
+       "      <td>0.008715</td>\n",
+       "      <td>0.034552</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -529,15 +598,15 @@
        "    <tr>\n",
        "      <th>441</th>\n",
        "      <td>2018-06-17</td>\n",
-       "      <td>12.126587</td>\n",
-       "      <td>12.246147</td>\n",
-       "      <td>12.329005</td>\n",
-       "      <td>12.141949</td>\n",
-       "      <td>12.261935</td>\n",
-       "      <td>12.357400</td>\n",
-       "      <td>-0.052046</td>\n",
-       "      <td>-0.018340</td>\n",
-       "      <td>0.007806</td>\n",
+       "      <td>12.120224</td>\n",
+       "      <td>12.222411</td>\n",
+       "      <td>12.348094</td>\n",
+       "      <td>12.118776</td>\n",
+       "      <td>12.241921</td>\n",
+       "      <td>12.383204</td>\n",
+       "      <td>-0.047615</td>\n",
+       "      <td>-0.020991</td>\n",
+       "      <td>0.005054</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -545,15 +614,15 @@
        "    <tr>\n",
        "      <th>442</th>\n",
        "      <td>2018-06-24</td>\n",
-       "      <td>12.164442</td>\n",
-       "      <td>12.286841</td>\n",
-       "      <td>12.371278</td>\n",
-       "      <td>12.139730</td>\n",
-       "      <td>12.266347</td>\n",
-       "      <td>12.349687</td>\n",
-       "      <td>-0.003398</td>\n",
-       "      <td>0.030753</td>\n",
-       "      <td>0.056143</td>\n",
+       "      <td>12.123663</td>\n",
+       "      <td>12.269786</td>\n",
+       "      <td>12.377512</td>\n",
+       "      <td>12.073790</td>\n",
+       "      <td>12.243468</td>\n",
+       "      <td>12.359611</td>\n",
+       "      <td>0.001664</td>\n",
+       "      <td>0.027824</td>\n",
+       "      <td>0.054327</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -565,30 +634,30 @@
       ],
       "text/plain": [
        "          week  prediction_5  prediction  prediction_95    trend_5      trend  \\\n",
-       "0   2010-01-03     13.282945   13.387017      13.496885  12.974152  13.077699   \n",
-       "1   2010-01-10     13.512545   13.613519      13.719359  12.989363  13.082902   \n",
-       "2   2010-01-17     13.261676   13.376100      13.481059  12.980930  13.069294   \n",
-       "3   2010-01-24     12.997246   13.161258      13.373597  12.933521  13.079675   \n",
-       "4   2010-01-31     13.053523   13.187187      13.280910  12.965601  13.068511   \n",
+       "0   2010-01-03     13.280258   13.386934      13.494152  12.971734  13.065477   \n",
+       "1   2010-01-10     13.505674   13.620594      13.740260  12.959251  13.072118   \n",
+       "2   2010-01-17     13.239633   13.376727      13.491780  12.982969  13.064959   \n",
+       "3   2010-01-24     13.001890   13.151968      13.271914  12.938928  13.064210   \n",
+       "4   2010-01-31     13.039371   13.182339      13.298785  12.956090  13.070954   \n",
        "..         ...           ...         ...            ...        ...        ...   \n",
-       "438 2018-05-27     12.128130   12.225532      12.349437  12.161564  12.257416   \n",
-       "439 2018-06-03     12.053329   12.164517      12.298734  12.140461  12.256120   \n",
-       "440 2018-06-10     12.164289   12.253961      12.365318  12.131554  12.239526   \n",
-       "441 2018-06-17     12.126587   12.246147      12.329005  12.141949  12.261935   \n",
-       "442 2018-06-24     12.164442   12.286841      12.371278  12.139730  12.266347   \n",
+       "438 2018-05-27     12.105644   12.232086      12.315658  12.117114  12.257510   \n",
+       "439 2018-06-03     12.040424   12.164513      12.281393  12.125013  12.248700   \n",
+       "440 2018-06-10     12.123452   12.243904      12.379812  12.103380  12.237064   \n",
+       "441 2018-06-17     12.120224   12.222411      12.348094  12.118776  12.241921   \n",
+       "442 2018-06-24     12.123663   12.269786      12.377512  12.073790  12.243468   \n",
        "\n",
        "      trend_95  seasonality_5  seasonality  seasonality_95  regression_5  \\\n",
-       "0    13.171040       0.272877     0.306664        0.339836           0.0   \n",
-       "1    13.160715       0.457050     0.541612        0.613547           0.0   \n",
-       "2    13.198988       0.233724     0.303712        0.382880           0.0   \n",
-       "3    13.184074      -0.043989     0.085492        0.329129           0.0   \n",
-       "4    13.163614       0.038108     0.109961        0.183865           0.0   \n",
+       "0    13.169708       0.285983     0.313343        0.357919           0.0   \n",
+       "1    13.150729       0.454911     0.555630        0.630481           0.0   \n",
+       "2    13.188750       0.197720     0.297731        0.384834           0.0   \n",
+       "3    13.150783      -0.010906     0.080342        0.147278           0.0   \n",
+       "4    13.154630       0.005171     0.107658        0.201739           0.0   \n",
        "..         ...            ...          ...             ...           ...   \n",
-       "438  12.386220      -0.056662    -0.024462        0.000079           0.0   \n",
-       "439  12.393392      -0.115286    -0.083959       -0.058784           0.0   \n",
-       "440  12.357624      -0.023095     0.011267        0.037542           0.0   \n",
-       "441  12.357400      -0.052046    -0.018340        0.007806           0.0   \n",
-       "442  12.349687      -0.003398     0.030753        0.056143           0.0   \n",
+       "438  12.337148      -0.054384    -0.026241        0.000361           0.0   \n",
+       "439  12.375470      -0.115741    -0.086977       -0.063800           0.0   \n",
+       "440  12.376783      -0.017700     0.008715        0.034552           0.0   \n",
+       "441  12.383204      -0.047615    -0.020991        0.005054           0.0   \n",
+       "442  12.359611       0.001664     0.027824        0.054327           0.0   \n",
        "\n",
        "     regression  regression_95  \n",
        "0           0.0            0.0  \n",
@@ -606,7 +675,7 @@
        "[443 rows x 13 columns]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -626,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "7febe9aa-ca17-443b-a340-988b9d78afbd",
    "metadata": {
     "ExecuteTime": {
@@ -656,7 +725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "continent-ground",
    "metadata": {
     "ExecuteTime": {
@@ -667,7 +736,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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IiIiIiEZVQ0NDoZdAVLQOqDy8o6MDl1xyCU4//XREIhFLoNCstbUVmzdvxqxZsywBSwC47rrrEAqFsHnzZkQikQNbPYArr7wS27Ztw2WXXYbPfOYzlvP0TEunLEsiIiIiIiIiokIKh8MIh8OFXgZRUTqgoOXNN9+Mu+++G3PnzsWGDRuwZMkSx8vNmzcPHR0deOaZZywBSwBIpVJIpVIAALfbfSDLQEtLC+69914ceuihuPnmm7POf/XVV+F2u3H88ccf0O0TEREREREREY2WSCRyUIlcRKXsgIKWs2fPxl133YU333wTp5122qCXnTx5MubNm2c5LRaLYdmyZUilUjjnnHNQU1NzIMvAFVdcASEEfvKTn6CystJyXn9/P95++23Mnj0b9913HxYsWIBQKIQpU6agoaEB27dvP6D7JCIiIiIiIiIaCc3NzWhubi70MoiK0gEFLS+//HJ897vfhdfrHdb1/vCHP+CMM87AjBkzcN999+Hss8/G/ffffyBLwLp16/D3v/8dRx11FL7yla9knf/aa69BVVW0trbiiiuuQHV1NZYsWQKfz4cHHngAJ510EjZu3HhA901ERERERERERESj54CClgfq2Wefxfr169Hd3Q1Ay4ZsbW09oNu68847AQDXXHNNVuk5kBnCM2fOHLz55pt47rnnsHbtWrzzzju46qqrEI1Gcd5556G/v9/x9pPJJHp7ey3/ksnkAa2ViIiIiIiIiIiI8ndQ08OH69prr8Xtt9+OtrY2/OIXv8Cdd96JJUuWoKWlBfPnz8/7drZv344//elPOOSQQ9DY2Oh4mUsuuQRnn302AoEApk6dapzu9Xpx2223YePGjXjllVfwyCOP4IILLsi6/i233IIbb7zRctqKFStw9dVX573O8aarq6vQS6Aiw22C7LhNkB23CbLjNkF23CbIjtsEOSnX7UJPpGJfy2zluk2Ug3yHT41p0HLatGkAtJ6Yd9xxB2KxGFavXo1bb711WGXiDz/8MIQQaGxshMfj/BBcLhdmzpyZ87zPfOYzeOWVV/Dyyy87Bi2///3v46qrrrKc5vf74ff7817neMSpZWTHbYLsuE2QHbcJsuM2QXbcJsiO2wQ5KcftQp/PUY6PPR98XsrbmJaH233ta18DAGzZsmVY13v88ccBwLGXZb70AGosFnM83+/3o7q62vKv1AOWRERERERERERExWBUg5YvvPACLrroItx7772O5+tBQFmW877Njo4OvPLKKzj88MPx4Q9/OOflbr75Zpx77rnYvHmz4/m7du0CABxyyCF53zcRERERERER0UhZtmwZli1bVuhlEBWlUQ1adnZ24pe//CVuv/12qKqadf7TTz8NAFiwYEHet6kHIU899dRBL7d161Y88sgjaGpqyjovHo/j4YcfBgAsXbo07/smIiIiIiIiIiKi0TeqQcszzzwTM2fOxPbt23HNNddYApdr167Fj3/8Y7jdbkvvSFmWsW3bNmzbts0xA/Pll18GAJx44omD3vfFF18MALjrrrvwzDPPGKenUiksW7YM7733Hj71qU/hIx/5yEE9RiIiIiIiIiKiA7Fjxw7s2LGj0MsgKkqjOojH7/ejubkZS5cuxW233YbHHnsMxx13HHbt2oXXX38dHo8Hq1evtgQg29racNRRRwEA3nnnHcyaNctym++88w4AWCaCOznttNNw/fXX40c/+hGWLl2KU045BTNmzMCLL76ItrY2zJ8/H7/73e9G9gETEREREREREeVp/fr1AIC5c+cWeCVExWfUB/GceuqpeO211/CNb3wD8Xgca9euxe7du3Huuefi73//O771rW8N6/b27dsHAJgwYcKQl73pppvwxBNP4JOf/CTeeust/PGPf0QoFMJ1112HlpaWIQOfRERERERERERENPYkIYQo9CKoOEQiEYTD4UIvg4oItwmy4zZBdtwmyI7bBNlxmyA7bhPkpFy3i1WrVgEAh/E4KNdtgjJGPdOSiIiIiIiIiIiIaDgYtCRHaYUJuEREREREREREVBijOoiHxi9ZATzuQq+CiIiIiIiIqHSx/JkoNwYtyVFaKfQKiIiIiIiIiEpbQ0NDoZdAVLRYHk6OZJaHExERERERERFRgTBoSY5kZloSERERERERjarNmzdj8+bNhV4GUVFi0JIcsTyciIiIiIiIaHS1tLSgpaWl0MsgKkoMWpIjOc3ycCIiIiIiIiIiKgwGLclRWi30CoiIiIiIiIiIqFwxaEmOWB5ORERERERERESFwqAlOZLThV4BERERERERERGVK0+hF0DFRwgBWWVPSyIiIiIiIqLRNGfOnEIvgahoMWhJWVQBCPa0JCIiIiIiIhpVZ5xxRqGXQFS0WB5OWRQVUJhoSUREREREREREBcKgJWVRVe0fEREREREREY2edevWYd26dYVeBlFRYnk4ZVGF9o+IiIiIiIiIRs/OnTsLvQSiosVMS8qiCq1EnIiIiIiIiIiIqBAYtKQsigqoTLUkIiIiIiIiIqICYdCSsqiqYHk4EREREREREREVDIOWlEVRAQFmWxIRERERERERUWFwEA9lUQZilapgVJuIiIiIiIhotNTX1xd6CURFi0FLyiJMQUsiIiIiIiIiGh0LFy4s9BKIihYT6SiLPjlc5QRxIiIiIiIiIiIqAAYtKYserFSYaUlEREREREQ0apqbm9Hc3FzoZRAVJZaHUxZloC5cMGhJRERERERENGoikUihl0BUtJhpSVn0XpYKy8OJiIiIiIiIiKgAGLSkLJmelky1JCIiIiIiIiKiscegJWVROT2ciIiIiIiIiIgKiD0tySIajeLhh5/Cv/Ym8NoM4KILvohQKFToZRERERERERERURlh0JIM0WgUl112GV5pjUEKTMZTPf/ExmcexZo1axi4JCIiIiIiIhphS5cuLfQSiIoWy8PJ8Oijj6KlpQU1EyaitrYWEyfWoqWlBU1NTYVeGhEREREREVHJmTt3LubOnVvoZRAVJQYtybBnzx4IIeDz+QEAPn8AQgi0t7cXeGVERERERERERFROGLQkw7Rp0yBJEpJpFcJTg5SchiRJqKurK/TSiIiIiIiIiErOqlWrsGrVqkIvg6gosaclGb70pS/h+eefx9bKb0OdvADJ7fehfrofjY2NhV4aERERERERERGVEWZakiEUCuE3//c7eCYvAADMOO6LHMJDRERERERERERjjkFLsvD4MwHKUPVEBiyJiIiIiIiIiGjMMWhJFomUMA6n0gVcCBERERERERERlS0GLckiIWcOJxm0JCIiIiIiIiKiAuAgHrJIyuZMSzHIJancCCEgSVKhl0FERERERFQyGhoaCr0EoqLFoCVZxJOmoKU8yAWprCiqgBCAx13olRAREREREZWOcDhc6CUQFS2Wh5NFzJxpqWjZdUSq0P4RERERERHRyIlEIohEIoVeBlFRYtCSLBKpzGEhAFkp3FqoeKiq9o+IiIiIiIhGTnNzM5qbmwu9DKKixKAlWcRS1uNJlogTAEVlpiURERERERERjR0GLckilrRGpsyDeah8sTyciIiIiIiIiMYSB/GQRcKWaZlgpiVBKw1nzJKIiIiIiIiIxgqDlmQRTzHTkrKpAhAQAKRCL4WIiIiIiIiIygCDlmSRsAUpmWlJAKAKAQ6SJyIiIiIiIqKxwqAlWdjLw5NpRqpIG8TDoCUREREREdHIWrZsWaGXQFS0GLQkC3umZTKV44JUVlQBNrUkIiIiIiIiojHDoCVZ2DMtYylGqspdNBrF7373OPbs3Yt5h1ShsbERoVCo0MsiIiIiIiIa93bs2AEAmDt3boFXQlR8GLQki0Ta3tOSQctyFo1Gcdlll+HlrW0QkIBYG5588kmsWbOGgUsiIiIiIqKDtH79egAMWhI5cRV6AVRc7JmWcZaHl7VHH30ULS0tmDixFtOmTUdtbS1aWlrQ1NRU6KURERERERERUQljpiVZJG2ZlfEkMy3L2Z49eyCEgM8fACDB7/dDCIH29vZCL42IiIiIiIiIShgzLckiIVuPJ9OFWQcVh2nTpkGSJKTkNCBJSCaTkCQJdXV1hV4aEREREREREZUwBi3JIivTkoN4ytqXvvQl1NfXo7u7F3s79qOzsxP19fVobGws9NKIiIiIiIiIqISxPJwsUrbMSnuPSyovoVAIa9aswc/vexIdHftw9GE+Tg8nIiIiIiIaIeFwuNBLICpaDFqShb0cnNPDKRQK4Ytf+jJUARx9iLvQyyEiIiIiIioZDQ0NhV4CUdFi0JIs7OXhSTnHBamsKCrA8DURERERERERjRUGLckiqzycmZYEQBWA4KZAREREREQ0ojZv3gwAWLhwYYFXQlR8OIiHLJJpZlpSNlUVUBm1JCIiIiIiGlEtLS1oaWkp9DKIihIzLckiZQtS2svFqTwx05KIiIiIiIiIxhIzLclCVqzH7YN5qDypqtbXkkiXVhjFJiIiIiIiotEzIkHLjRs3wuVy4d5773U8f+fOnbjgggswffp0+P1+zJw5ExdffDHef//9Yd3P73//e0iSlPPfV77ylazr7Nq1C1//+tcxc+ZMBINBHHXUUbjlllsgy6x7tks7BKaYaUkAoAgt25JIxyA2ERERERERjaaDLg/fvn07GhoaIHLUjra2tuLkk09GV1cX5s+fj1NPPRXbt2/H6tWr8cADD2DTpk047rjj8rqvLVu2AAAWL16MQw45JOv8U0891XJ869atOO2009DV1YWTTz4ZJ510El544QX84Ac/wJ///GesW7cOHg8r5HVO/SvZ05KAgfJwBqnIRFYAv7fQqyAiIiIiIqJSdVARuw0bNqChoQEdHR05L/PVr34VXV1duPHGG3H99ddDkiQAwE033YQf/vCHuPDCC/HSSy/ldX+vvvoqAGDVqlU45phjhrz817/+dXR1deGee+7BhRdeCADo7e3F2WefjT//+c/4n//5H/z7v/97XvddDpxKwe3TxKk8qSozLckqrQx9GSIiIiIiGtycOXMKvQSionVA5eEdHR245JJLcPrppyMSieCwww5zvFxrays2b96MWbNmWQKWAHDdddchFAph8+bNiEQied3vli1bUFFRgfnz5w952Q0bNmDLli049dRTjYAlAFRXV+O+++6DJEn42c9+ljNDtBw5Z1ry+SEO4qFsCqPYREREREQH7YwzzsAZZ5xR6GUQFaUDClrefPPNuPvuuzF37lxs2LABS5YscbzcvHnz0NHRgWeeecYSsASAVCqFVCoFAHC73UPe5969e7F7926ccMIJeV3+ySefBAB8/vOfzzpvzpw5OO644/Dee+/hjTfeGPK2yoVTpiUH8RAAqKpgkIosmGlJREREREREo+mAgpazZ8/GXXfdhTfffBOnnXbaoJedPHky5s2bZzktFoth2bJlSKVSOOecc1BTUzPkfer9LA855BB873vfw5FHHolAIIDDDz8c//Ef/4Guri7L5bdu3QoA+NCHPuR4e0cffTQAMGhpEktmB6VkBi0J2tAVLduSgUvSpNnjlIiIiIjooK1btw7r1q0r9DKIitIB9bS8/PLLD+jO/vCHP+Duu+/GSy+9hO7ubpx99tm4//7787quHrR86KGHUF1djUWLFuGQQw7Byy+/jJUrV+KJJ57Apk2bMG3aNABAe3s7AGD69OmOt6efvnfv3gN6LKUonpKyTkumGaQqd0II6FuBKgB39mZCZYiZlkREREREB2/nzp2FXgJR0TqgTMsD9eyzz2L9+vXo7u4GAPT396O1tTWv6+pDeD7zmc/gvffew9q1a/HnP/8ZO3bswCc+8Qm0trZaelf29/cDACoqKhxvLxgMAgCi0ajj+clkEr29vZZ/yWQyr7WOV049LTmIh+S0wJ9el/HMazJSDGLTALYLICIiIiIiotF0UNPDh+vaa6/F7bffjra2NvziF7/AnXfeiSVLlqClpWXI4Tpr1qzBf/3Xf+Gwww6zBCInT56M3/72tzjiiCPwxz/+Ee+++y5mzZpl9L2099K0U1XnGsdbbrkFN954o+W0FStW4Oqrr87noY5L+yJRABMspykqsG9/BO4xDW9Tsejq6sIL24Dfb9beKhYe3o2PH1XgRVFB6a049u1XMME7dH9hKn329ixE3CbIjtsE2XGbICflul3oCVf5DiguJ+W6TZSDcDic1+XGNGipl27Pnj0bd9xxB2KxGFavXo1bb711yDJxv9+fM7BZV1eHE088ES+88AJeeeUVzJo1C6FQCAAQj8cdr6Ofrl/O7vvf/z6uuuqqrDX4/f5B1zmeeXI8tMqqiajwsya4XHW/XwlAyzLuTlYiHC7d1wDlJxwOoyqmIBxm0JI0+X7poPLBbYLsuE2QHbcJclKO20VlZSWA8nzs+eDzUt4Kmj/3ta99DUCmX+XB0AOisVgMADBjxgwAwJ49exwvv3v3bgC5e176/X5UV1db/pVywBLIPSk8KbMMtJyZ2wYkHFoIUHliT0siIiIiIiIaTaMatHzhhRdw0UUX4d5773U8Xw8CyvLgkZBEIoGLLroIX/jCF3JmTu7atQuANl0cyEwNf+uttxwvr08XP/bYY4d4FOUjlXbOpmSgqrwlTEHrRIoBbNKk2dOSiIiIiOig1dfXo76+vtDLICpKoxq07OzsxC9/+Uvcfvvtjr0jn376aQDAggULBr2dQCCAJ598Eo8//jjWr1+fdf4//vEPvPbaa6ipqcEpp5wCADjzzDMBAI8//njW5Xfu3Ik33ngDhx56KIOWJubgpLlVHTMty1s8lTmc4LZAA5hpSURERER08BYuXIiFCxcWehlERWlUg5ZnnnkmZs6cie3bt+Oaa66xBC7Xrl2LH//4x3C73ZbekbIsY9u2bdi2bZslA/Piiy8GAFx55ZV45513jNP37t2Lb3zjG1AUBd/73veMqeCnnXYaTjjhBLzwwgv4+c9/bly+t7cX3/zmNyGEwH/8x38MOainnJgnhVcFM8+L01RxKh9J08RwbgukU1VACAaxiYiIiIiIaHSM6iAev9+P5uZmLF26FLfddhsee+wxHHfccdi1axdef/11eDwerF69GieeeKJxnba2Nhx1lDae+J133sGsWbMAAFdffTU2bdqEZ599Fscccww+9rGPwe/34/nnn0c0GsWXv/xlXHPNNcbtSJKEX//611i8eDGuuOIK/OY3v8Hhhx+OF154AR0dHTjrrLNwySWXjObDH3fMPS2rgxIiUS0gwey68pZgpiU5UFRAFYCbv/sQERERER2w5uZmAEBDQ0OBV0JUfEZ9EM+pp56K1157Dd/4xjcQj8exdu1a7N69G+eeey7+/ve/41vf+lZet+P3+/H000/jzjvvxPz58/GXv/wFzz//PI455hjce++9eOihh+B2WyfZnnDCCWhpacF5552H9957D0899RSmTJmC2267DY8++ig8njEdnl70zJmW1RWZw3H2MSxr5kxLcwCTypsqtH9ERERERHTgIpEIIpFIoZdBVJRGJGp3//334/777895/uzZs3HffffldVuzZs3KWXLo8Xhw5ZVX4sorr8x7bUcccQQeeOCBvC9fzsyZljUVmfSp/kQBFkNFI2nJtCzcOqi4qKoAq8OJiIiIiIhotIx6piWNHyk5E6i0BC2TjEyUM3OmZSrNbYE0qtD6WhIRERERERGNBgYtyZAr0zLG8vCyZh6+w0xLAgayLMHycCIiIiIiIho9DFqSwdzTcoI5aJkswGKoaJiD2UkO4iFkgpUMWhIREREREdFo4SQaMpiz6GoqM0FLDuIpbynZXB5ewIVQ0dCDlexpSURERER0cJYuXVroJRAVLQYtyZAz05ITo8uaJdOSPS0JmV6WzLQkIiIiIjo4c+fOLfQSiIoWy8PJkExrgUqXBIQCmaBlgpmWZc08fCfFnpYEQDHKw/neQERERERERKODQUsy6JmWPg8Q9JnLwwu0ICoK5kAly8MJyJSFM2ZJRERERHRwVq1ahVWrVhV6GURFiUFLMiRNQUu/L3N6gsNXylqK5eFko+jl4Wph10FERERERESli0FLMhiZll4JAW8m0zLJkuCyllIygUqZmZYEYMeeNH7zfAqb/skNgoiIiIiIiEYHg5Zk0IOTAQ/g95hPZ3ZdOTNnWsoKoHL6Stlb/UwKL7UquPX3CW4PRERERERENCoYtCSDOdPSb8q0TDDTsmypAkgr1tOSTK4re/t6tUBlb5zbAxEREREREY0OBi0JAJBWBNKqFqj0e4GAN3NeKi2YTVWmnAbvsMcpmQOVHM5EREREREREo8Ez9EWoHJizKf0eWDIt5bRWFuxniLvsOAWk2OOUUrK5z6kAIOW+MBERERER5dTQ0FDoJRAVLQYtCYC1b6XfK8FvyrSUlYGgpdfhilTSnEp/GbSklKllADMtiYiIiIgOXDgcLvQSiIoWc+cIgC3T0ivB6wakgeSpVFpAVlgSXI6cApQsD6eUpTyc2wMRERER0YGKRCKIRCKFXgZRUWLQkgBYMy0DXkCSJPjc2vG0opWIU/lheTg5MQcqZWWQCxIRERER0aCam5vR3Nxc6GUQFSUGLQmANdNSH8LjG2geoJeHU/lxKg9npiXJHMRDREREREREo4xBSwJgDUQFfFpduG9gGE8qLQaGbVC5YaYl2Qlh/RGD5eFEREREREQ0Ghi0JABAMpU5bGRaDpSHywqQVsd+TVR4STl7KnQ8xY2hnKVsWdfMtCQiIiIiIqLRwKAlAQDilp6WeqaldlxOs6dluXIqBY+nHC5IZcP+XsDWEURERERERDQaGLQkANaS3+BAebjXpQWsZAV48OHfIxqNFmJpVEAJh1JwBi3Lmz2zkuXhRERERERENBo8hV4AFQdrT0sgGo3ig/d2Av45AIBf3b8Grz7/W6xZswahUKhQy6Qx5tS/MpFikKqc2YczsTyciIiIiOjALVu2rNBLICpazLQkAPaelhKampoQ7e0yTqsKT0VLSwuampoKsDoqFKdMS6fTqHzYy8HZOoKIiIiIiIhGA4OWBMCaaRn0Ae3t7RBKwjjN66+CEALt7e2FWB6NMFnJL1sy4TCIh5mW5Y3l4UREREREI2fHjh3YsWNHoZdBVJRYHk4AgN7+TKrlXzb9CYeFw4CSOU1WPZAkCXV1dYVYHo2wVBrwuoe+nL0UGADizLQsa/agJTMtiYiIiIgO3Pr16wEAc+fOLfBKiIoPMy0J0WgUDz78e+P4g7+7H+vXr8eE6qBxWn8shZPqT0FjY2MhlkgjLN/suKTD0B2nieJUPphpSbmk88zgJiIiIiIiygeDloSmpia0740Yx6tDPrz66qs4ZPpk47Qzzjobv/r1bziEp0TkOzzF6XJJBi3LWiptbRnwwt9eQjQaLdBqqJjY+50SEREREREdDAYtSetT6Q4Yx31uFUIIpFP9xmkL6k+BL8CAZanIt6TXqTycg3jKW280bjn+pz8/j/PPP5+BS2KrACIiIiIiGlEMWhLq6uqQfOMO4K9fga/1dsjdb0OSJNSE/MZl5DQgswy0ZORdHu6UaelQMk7l44W/vmQ5XlE5AS0tLWhqairQiqhY5Dvgi4iIiIiIKB8MWhIaGxtR/+Gj0L17G7ratyKybzfq6+tx/LHzjcvICkv/Skm+5eGOQUsGr8taZ7c1o9LtDUAIoWVsU1njZwQRERHR8IXDYYTD4UIvg6gocXo4IRQKYc2aNVj1v/fh3a5KfHjmUjQ2NuK3f/UCSALQMvO4Q1o68i4PdygFdzqNykdVdRjozBxXhAuSJKGurq5wi6KiwPJwIiIiouFraGgo9BKIihaDlgRAC1w2NJ6PPbEaLJyrbRZBX9I4X1ZYHl5K8i0PTylS1mkcxFPejj1+ATZuyBxPpBTU19ejsbGxcIuiglNVAUXlewMREREREY0cloeThcsUowr4MkfkdP4lxVT88p4e7pRpye2grAmX33L8wwtOxpo1axAKcVBXOVMEwJglERER0fBt3rwZmzdvLvQyiIoSg5Zk4TJtERW+zGGWh5eW/DMtM4c9A9sGMy3Lmz1officIxiwJCgKoKiFXgURERHR+NPS0oKWlpZCL4OoKDFoSRZuU6Zl0J85kkrnH+ii4qaqQsuKyiMtSu9R55KAioEEO2bcljd7T1NuDwQw05KIiIiIiEYeg5Zk4ZIygUpzpqWsMDhRKvS4QjqPrCg9q87nAXwebdvgdlDeUraMa/a6JQBQVe0fERERERHRSGHQkiys5eHmTEvBTMsSoWdD5VPubw5a+r3aYW4H5c2eackepwRopeEK3xqIiIiIiGgEMWhJFm7TFmEuD5fTWrArzb3ScU8M/AnTeQQtU2ltG/B6JPi92mF70IrKiz3Tlpm3BACqEMy0JCIiIiKiEeUp9AKouFimh3szh/XARCoNeNxjuyYaWXpgQQtASzkvl0oLI7Dp92jZloCWoSmEgCTlvi6VLj2QnTnOHzJIy7RUBbcFIiIiouGaM2dOoZdAVLQYtCQLc3l4wFYerv2fGchC45OaZ6ZlLJkpIfd5pKwgtt/rfD0qbfYgpcxMS8JA0JKZlkRERETDdsYZZxR6CURFi+XhZGHNtDSVhyv6/8ykGe/0v+BQPS37E2om09ILozwcABIsES9byaxMywIthIqKomr/iIiIiIiIRgqDlmThNkUtg6bp4ebycBrfMj0tBw9A9yUExED5uN9jzaxMyAxelyt7sJvl4QRoWZb8TYuIiIho+NatW4d169YVehlERYnl4WRhzrT0ebSOhwLm8nDulY53Rk/LIbKiuvszh/1eCX5PZuPgMJ7yxUE85ERROYiHiIiI6EDs3Lmz0EsgKlrMtCQLc09LSZLg1YevpK3/0/iVb0/L3ngmQO33WnucJlIMXpcre5ByqDYDVB5UkXlvISIiIiIiGgkMWpKF27ZF6BOjjfJwBijGPTHQ1XKwYJMQAr3xTNqU32sdxJNk8Lps2bcbmdnXBO1HEJVRSyIiIiIiGkEMWpKFyzpjwxi+og/gYXn4+JdPT8uEbM2o83thDVqyp2XZStkH8fCHDIKWZclBPERERERENJIYtCSLrKClPdOSGXbjnt53brBMy1hSWM73eyQEfZweTtnbzVBtBqg8bHorjYf/nsK+Xm4QREREREQ0MjiIhyyyy8O1UTwp9rQsGXoFpzJo0NJa9hvwWaeHx5NMqSpX9qClompDWNz2XzyobPTEgP9+KglFBQ6blMI1XwgWeklERERE40Z9fX2hl0BUtBi0JAuXLfCgB6r0wASg9TuUJAYoxiujPHyQuGMsZcu09DLTkjROP1yk0kDQN/ZroeKwvy9TGt7exdYRRERERMOxcOHCQi+BqGixPJws7JmW5j6GMkvES4IeUpAVASGcAwzxlLAEp4JeoMIUtIynRnGBVNSc2grwPaG8mX/ESKQYtCQiIiIiopHBoCVZ2BMo9UE8QCYwMVgvRCp+qinDMle2ZSwJW6Yl4Ddl0sUZmChbTq9/ThAvb8k0s7CJiIiIDlRzczOam5sLvQyiosTycLJwZwUtM4e1CeLSwARxloePV6opvpRWAK/ber6iCiRkYUyMB4CAV4Lfw0zLcpdWBFSR/dpnpmV5s2RaygxgExEREQ1HJBIp9BKIihYzLcnCZdsinDItGaAY34QtaGkXS2r/W8rDfdZWAQxMlKdcr/0UMy3LmjVoWbh1EBERERFRaWHQkizsA4Cdelpygvj4Zg4vmbMpddGEdpo5oBnwSZYANoOW5SmZIzjJHzLKW1I2H+Z7AxERERERjQwGLckgSdmDeMwTo/VsqnLKqlLV0nus5sfk1J9QD1qmTAFNv1eyBLCTzKYqS6kcf3f2uS1v5veDXNsIERERERHRcDFoSQYJgGSbxOM3dT0tx/Lw7lgJBi1ND0lxCDb1JwcyLU1/Z78H8JkzLdnTsiyxPJycmHvcJsvo84GIiIiIiEYXB/GQwZ5lCWhlwbrUQICrnAIUvTGBUEDA5ymdwUPmnpbOmZba/ylzebgt05Ll4eWJ5eHkJM7ycCIiIqIDtnTp0kIvgahoMWhJBvsQHkAbwKKTjfLwMVpQEUiltcDlpOrSCFpGo1E8+NAf8c6eFMK1YXz1C4uByVXG+am0MILS5p6Wfq8WuNSxPLw85fq7l9N7AmWzZFryvYGIiIhoWObOnVvoJRAVLQYtyWAfwgPYMi31QTxl1L8upQC9cYFJ1YVeycGLRqM4//zz8cr2bkgVMyBJEl77+zo8ev+tCIVCAID+hLnfZeawzyvBb+lpyWyqcpQry9ppoBOVD/PE8LQKKKqA2+kDhYiIiIiIaBjY05IMTjuZQVOgKtPTsnwCFClZoC9eGo+3qakJLS0tmDBhImpra1FVVYWtb21HU1OTcRm9NBywTokPeGGZHs6+deXJ/Hf3mD49mGlZ3hLy4MeJiIiIKLdVq1Zh1apVhV4GUVFi0JIGZZ4erpeHqwKIp0Y/kFcMwdFUGuiNF3oVI6O9vR1CCHj9fgCA1+sFJDfa29uNy0QTzpPF/R779PDC/21o7JkD2ZUByfF0Kj+JlPUHrxTfH4iIiIiIaAQwaEmDCjoM4gGA3V3qqN93MWRvyYpW+joWQdrRVldXB0mSIA88sbIsQ3L7UVdXZ1xGD1om4gl80L7XOF1J9cPn0SbMA8y0LFf9yczrviqYOb0YfmCgwknY3g+YaUlERERERCNhRIKWGzduhMvlwr333ut4/s6dO3HBBRdg+vTp8Pv9mDlzJi6++GK8//77w7ofVVVxzz334NRTT0V1dTUCgQCOPPJIrFixAt3d3VmX7+rqgiRJOf9NmzbtQB5uWanwZw6bg4h7ugWEGN1ARaEDIaoqjF59pVAi3tjYiPr6evT5jkRv1WnoSwBHHP1hfOacBgCAEAKxpEAinsDKlSvR1pYJWl667CL09/fDO9AFl5lU5anf1D4gxExLGmAfvpPg+wMREREREY2Agx7Es337djQ0NOQMYLW2tuLkk09GV1cX5s+fj1NPPRXbt2/H6tWr8cADD2DTpk047rjjhrwfVVXx5S9/Gb///e9RUVGBhQsXorKyEps3b8ZPf/pTPPbYY/jLX/6CqVOnGtfZsmULAGD+/PlYsGBB1m1OmDDhwB50GQk4lIcD2k5pV79AODR6wxYKnWlpvv++uMCUmsKtZSSEQiH8772/wyd/nIYq3Dj66GNwyecPw+7eIA6ZAsRSgCKAjZs2orW1FRUnfN647paWv6OpqQk+z1eQSnNCcLkyZxybg5aF/oGBCsueWcn3ByIiIiIiGgkHFbTcsGEDGhoa0NHRkfMyX/3qV9HV1YUbb7wR119/PSRJ29G96aab8MMf/hAXXnghXnrppSHv69e//jV+//vf48gjj8S6deswa9YsAEBfXx/OP/98rF27Fpdddhkeeugh4zqvvvoqAODSSy/FsmXLDuKRlq8KX+awPYjY3iUQDo3efRc6aGkuge4tgUxLAIirFVBFPwCgMjwTgaAffQmBPd2qMT0+0hmBEAKSywv9UQslifb2dvg9EqIQBf/bUGHEk5nDlX7n1hFUfphpSUREREREo+GAysM7OjpwySWX4PTTT0ckEsFhhx3meLnW1lZs3rwZs2bNsgQsAeC6665DKBTC5s2bEYlEhrzPX//61wCAlStXGgFLAKiqqsJ9990HSZLw+OOPIx7PTE3RMy2dsiwpPxWmwIRsC0zs71VHNcOq0Nlb5vsvhfJwwBoINmdH7dqrGoHZcG0YkiRBSAO/aagpSJKEuro6+AeG8SSZWVeWYqZMS2tPywIshoqGvccte1oSERER5a+hoQENDQ2FXgZRUTqgoOXNN9+Mu+++G3PnzsWGDRuwZMkSx8vNmzcPHR0deOaZZywBSwBIpVJIpVIAALfbPeR9Tpw4EfPnz8cpp5ySdd6kSZMwceJEyLKM/fv3G6e/+uqrcLvdOP7444fz8MjEmmlpDVSpAujoGc2g5ajddF7MffrSKtCfHP+BOvNjMk8AT8gCH3RqQ1YWL1qMefPmQRXa61IoKdTX16OxsRE+r/Y6LvTfhgojLjuXh8sMYpe1pGz9fE+WwOAyIiIiorESDocRDocLvQyionRAQcvZs2fjrrvuwptvvonTTjtt0MtOnjwZ8+bNs5wWi8WwbNkypFIpnHPOOaipGbpZ4Nq1a/HPf/4TtbW1Weft3LkTkUgEPp8PkydPBgD09/fj7bffxuzZs3HfffdhwYIFCIVCmDJlChoaGrB9+/ZhPOLyZc60dApU7e8bvSnihc60tGcTJkug5DFl6UtqPU8dOCsQDGD58uWorNJelxUBD9asWYNQKAT/QPKlrGDUBzFR8UmkMoetPS0LsBgqGvZMS/txIiIiIsotEonkVX1KVI4OKGh5+eWX47vf/S68Xu+wrveHP/wBZ5xxBmbMmIH77rsPZ599Nu6///4DWYLFD37wAwDAZz/7WQQCAQDAa6+9BlVV0draiiuuuALV1dVYsmQJfD4fHnjgAZx00knYuHHjQd93qQv6B58Q3BsbvSniclqb4F0o9kCMMnrx2TGTqzzcLhAMwO3VXkvVVUGEQlrzUt9A0FIILfuUSkO+r7Pcg3hGfEk0jtj//hzEQ0RERJS/5uZmNDc3F3oZREXpgIKWB+rZZ5/F+vXr0d3dDUDLhmxtbT2o27zzzjvx0EMPoaKiAj/+8Y+N0/UhPHPmzMGbb76J5557DmvXrsU777yDq666CtFoFOeddx76+/sdbzeZTKK3t9fyL5lMOl62lHndkjGgJaVkBzbSKtAXzzp5RCgFDozZd8TVEgjSyaa/4WCZo4oqEBvY3CtNE+T93sxhBiZKR75BR/PfvCrI6eGksW8/HMRDQGF/dCQiIiKi0nBQ08OH69prr8Xtt9+OtrY2/OIXv8Cdd96JJUuWoKWlBfPnzx/27f3sZz/DVVddBUmS8Ktf/cpyG5dccgnOPvtsBAIBTJ061Tjd6/Xitttuw8aNG/HKK6/gkUcewQUXXJB127fccgtuvPFGy2krVqzA1VdfPex1jhddXV2Op/s8biRkCYmkgt6e7LT1d9vcOKR25OPfXZ1p7KtyI+iThr7wKNi/P22ZGr4v6IZXHdM4/4jr7JYAaL0qE7JAT3cEksPTu79Pgiq0TMvJVWmjXMElXNB/69izrwvhyrFYNY22aEKgwge4XIO/1nqjgPGxke6Hvi1FY0lEIqP06wUVtbQKpFXrV4lIdz8ikWiBVkTFoKurK+/3FSoPub5jUvniNkFOynW70BOpWCKerVy3iXKQbx/XMQ1aTps2DYDWE/OOO+5ALBbD6tWrceuttw6rTFwIgRUrVuC2226D2+3Gr371K3zlK1+xXMblcmHmzJmO13e5XPjMZz6DV155BS+//LJj0PL73/8+rrrqKstpfr8ffr8/73WOR04bjs/Ti4QMKMKN6hqHDcvnQjg89DCl4QrVpFEzwW0pQx1Lwf1pCF8maFlT40Y4PL6Dlv6ADEALLgkhIRiaCJ8n+/l9v1cBoDUwPHyaH+GwNiq6qiIGQEurqqicMO6fD9J4YgJ+rzWT1k4IAbhiABQAwLRJIejbkuT2IRyuGIOVUrGJJgSAPstpbl8FwuHS/qykobn8E+H1AJV+Bi1JwyETZMdtgpyU43ZRWallgpTjY88Hn5fyVtCIw9e+9jUAwJYtW/K+Tjwex5e//GXcdtttCAaDePTRRx2DjkPRA6ixWMzxfL/fj+rqasu/Ug9Y5pKZGO1c6tUTG50SMEUVSCujctN5sT/edAmUusm2En/zYBWzvaap8Ieasmh9pja29kFFNH6pQhuuNJhUGpBNLRIq2dOSACQcJoXnel+h8qIK4bh9EBERERHla1SDli+88AIuuugi3HvvvY7n60FAWc6vOV5vby8++clP4rHHHsPkyZPx3HPP4fOf/7zjZW+++Wace+652Lx5s+P5u3btAgAccsghed13OfMNJFE6DeIBtECYlm0zslQBpB36aI4FRRVZ/TRLoadlvr3n9vVkHuyhkzJvEwFTJl6KPS1LhhAiK6Btl0pbX49VAfOQLgYmypXTQK+/vtiCG264Affccw+iUZaJlytFBeIMYBMRERHRQRjV8vDOzk788pe/xKZNm/DNb34TLpc1Rvr0008DABYsWDDkbcmyjLPOOgt///vfMWfOHKxfvx5z5szJefmtW7fikUcewYwZM7Bw4ULLefF4HA8//DAAYOnSpcN9WGVHKxkVSClacENyaILY3S9GtIxbGchqLNTEbqfMsfE6PVxVhdFTLN8pv+ZMy5mTM69bvyXTcsSWSAWmiqGD8qm0sGRjml/v3BbKV6Qne5jdxhf+jufe+l+4XC48+eSTWLNmDUKhUAFWR4WkBS35gwYREdFQli1bVuglEBWtUc20PPPMMzFz5kxs374d11xzDVTTXvHatWvx4x//GG6329I7UpZlbNu2Ddu2bbNkYN5www34y1/+gmnTpmHjxo2DBiwB4OKLLwYA3HXXXXjmmWeM01OpFJYtW4b33nsPn/rUp/CRj3xkpB5uyfIPhLbFINO8u0e4RFwPEBZqengpBS3Nj8WeEZcr07JjIGgZ8ApMNA3bCZiClgl5nD4hlEUVuTOpdUkZlnYNoUDmMMvDy9fja9dlnSZcQfj9ftTW1qKlpQVNTU0FWBkVGjMtiYiIiOhgjWqmpd/vR3NzM5YuXYrbbrsNjz32GI477jjs2rULr7/+OjweD1avXo0TTzzRuE5bWxuOOuooAMA777yDWbNmobOzEz/72c8AAFOnTsWKFSty3ufKlSsxdepUnHbaabj++uvxox/9CEuXLsUpp5yCGTNm4MUXX0RbWxvmz5+P3/3ud6P58EuGuY+hnAa8DjN3uvtHNmipx7cLl2mZ/XgKVKl+0FJpIODLHDZzKu2U0wJdUe3BTqoS8LgzGXUB0yR37oyWDnWQHyR0qbSwBDargpLlPCpPezq6s05zeYKQZRl+vx9CCLS3t4/9wqjgVBWIMdOSiIhoSDt27AAAzJ07t8ArISo+oz49/NRTT8Vrr72G//qv/8L69euxdu1ahMNhnHvuubj66qtx0kknDXkbGzduNAbmvP7663j99ddzXvaGG27A1KlTAQA33XQT6uvr8d///d9oaWnBq6++ilmzZuG6667DihUrWK6WJ/NE4VQaqHCYR5RKC8RTAkHfyJSI6wHCQvW0dMy0HKdRS61XofMwpaRDpuW+XgH91EkhFeZuAC41E6lc++SfsOCwRXwdlQAhhu5LmUpnhlFJkvbjhdul/bAwVJYmla4JtVMBW9tKyROEx+tFMpmEJEmoq6srzOKooNKqNognV1sZIiIi0qxfvx4Ag5ZETkYkaHn//ffj/vvvz3n+7Nmzcd999+V1W7NmzYIQ1p3nL37xi1mn5evss8/G2WeffUDXJY3ftJVoQS/nnY9oYuSClsy0HBn2gUL5ZFp2mPpZTqoScA3sbEajUTz80BogfD4A4KFHn0DrX1azX10JUNU8pocrmcv4PIAkSfB5tIxbloeXr1M/ugSP/cv65ujyBpFMJtHZ2Yn6+no0NjYWaHVUSKqqZXEn5Uy2PxERERHRcIxqT0sqDeZMy8ECG/0jOEHc6Gk5RCBltDhnWo79Og6WoloHrCTtmZYOpXsdvZkrTKoScA+8SzQ1NeG9d1qN80LV7FdXKlSRR9BSFkgPvC70FhGegf9ZHl6+hCs79X7OEcfg4osvxg033MAfNcqY/oMZh/EQERER0YEa9fJwGv/Mw1cGy6jqT47cfaoDmbVFNYhnHO53Kao1WzVly6wcMtPSVB7e3t4OoWT+yB5/JfvVlQgh9DYCuWnl4dphv0fbKHweCYBgpmUZiyWzt5tJU+pww/Ibxn4xVFRUI2gJTCzsUoiIiIhonGKmJQ3J2tMyd2Cj32Hn9UApRnl4YSKFTv391HE4LDsraGnLpsunPFyfw1NXVweYelqmVTf71ZUIVQyd1ZxKCyOwqQ/n8g1kWg6VpUmlK+7wHpJ0OI3KDzMtiYiIiOhgMWhJQwrYpofnEkuKA+49amf0tCxQMMRpMnah+mseDC1omfmbpGyDd5wG8XT0aA+0Oqj97V0D7xKNjY2YO/tQ43LxlMp+dSVCFWLQ17beG1W/jG8g09I7kKvP8vDy5ZRp6fS+QuVHHfjscQpsExERUUY4HEY4HC70MoiKEoOWNKSAzzo9PBdVADGHYB8wdOmpndHTsgCBwq5+Nav3I1C4rM+DoajCmmk5xCCeeEqgN64dnlKjvT3o5eGhUAjXfO/fjct+5LRPsF9diRACSA/yGk2lgUQ8gZSsbUzR3gii0agRvGSmZfly+oEnyXYBhExLlfgIVmEQERGVooaGBjQ0NBR6GURFiUFLGpJ5enh/PIX169ejuakZ69evRyKesFw21zCe3tgBBi0LEAzZ3eW8VlVgxDJJx4p9EE92ebj18ezrzRyfXK0FpNymgfA1VUHj8JFHHceAZYlQhRZgUHME5iPdUdy+ciXUgY+Mjt3v4/zzz4db0jauwbI0qbQ5lf6yPJyATKUEy8OJiIiI6EBxEA8NKejLHH563Z/Q/o/HIISAJEl4ueVlLF++HIFgAEDuvpa9cYEJlQJul+R4vp2e1TjW2Y1pRWBfT+70zrSamZw8HqiqNVvV3qvTHlzoMD32qTXa38pl+pv5Ta0CmE1VOvTAtqwAfoefsh58+PfYseMdVEzVjrtdKlpaWnDkUfsBTNKCnmr+r28qHY6ZliwPJ2QyLdOq1kJCz8wmIiIiq82bNwMAFi5cWOCVEBUfZlrSkMyDeDr2d6Oqqgq1tbWoqqpCa2srNm7aaJyfa4J4IjW87Bs9VjnWfST39ohBp4SPt2E8aXumZVZ5+CCZljUu2GNQftNOp70/Jo1fegJxrjLv9j37IaRMtN4tZAghoMhx4zROEC9PCVMWnc/ocVqgxVBRURStrcT69etxw0234p577kE0Gi30soiIiIpOS0sLWlpaCr0MoqLETEsaUtDU0xIuL7xeLd3O6/VCCIFIZ8Q4O1d5eEIWSMoCFf58My21/wW07EePe2wyNHZ3DR6VHG/DeFQVUEwl7faBKUlblpS5jH9ipQS3ZH3emWlZmlQjaCkAZL/WaifXQXK/nbm8koQkSQj6PcDAdpBKW7OyqTyY+xhXByXs79OGNjHzlvpjcaxcuRKtra1Id70JJPbhySefZC9kIiIiIsobMy1pSOZAhOTyIxWYC6X6WKRkGZIkIVybmXQWTzlPEE+khhfkMmcHjlWgMJoQ6I0Pnj043oKWaVVYnkt7Jp0907LHFLSsrsgM4dGZs27tQ3xo/NKDlrl6yH78U2fh8NnzjOOpeC/q6+sx89DpmdM4QbwsmaeH11Tw/YEyNjz3PFpbW1FVVYVp0w9DbW0tWlpa0NTUVOilEREREdE4wUxLGpI507Ji+gLI3ilQASR7I5g3L4HFixYb5+sTxCv9mesLIZBMC0sZ4VDMJdppFfDnvuiI2d87dERyvAUt7T0t7WWb9pL93ky1L6qCEoR1zpJlKBPLw0uHkWmZ44eFtBTEN751IW59Qjt+3Ifm47dXr8F1D7ugp1pyGE95Mr+vm4OWSVmgMs/Meio9qirQ2dkFIYRWnZGW4Pf7IYRAe3t7oZdHBRSNRtHU1IT29nbU1dWhsbGRmbdERESUE4OWNKSAKdNS9k4xDn/olM/iu5+baQzh0fUnrDurSVkLigynp6U5OKiM0QTxXP04zVThXD5brOw9Le2BpWRae0yugZRKvTy80g943RJkWy62OdOSfetKQzQaxWOPrcMHHTHMne7Bsm98zrIDmUgJyIqAy+MHoL1IPnT0EQiFgvB5zD0tGcQuR3HT+3q1OdPSYUAPlQ9FAOHaMCRJgizL8EtuJJNaW4m6urpCL48KJBqN4vzzz0dLS4sx0JEtA4iIiGgwDFrSkCq8zkG6UO0sBILZOZD2CeJ6meDwysMzt5FWxyZQmGvyuVmu8tnRdDBTV1XVGgCWHaYMJWWtBYAQwigPrw4OTA7PKg83XY9BqnFP34HcsjMF+CZC9L+Hvz37oGUHMjrQp9acsasPXPFmZvMwiF1i8u1JqWdaet1AhekHLnvrCSovqgosXrQYL7e8jNbWVnT3dUBEO1FfX4/GxsZCL48KpKmpCS0tLaitrYXf70cymTRaBlx00UWFXh4RUcHMmTOn0EsgKloMWtKQAjlqszt6nHdKo7aSYn3ndTg7sZby8DEIFAohLL3ZclELsB8uK5kg0XApQgs+6JwCS3rQMiFnel7Gejuwfv0/sfiUYy2X9bq18LHIcVs0vug7kBMPXwxvRS3S/jhaWl6y7ED26UFLU5BaD6JLIpNm9+DDj+Hfv3Ums2VKhJwG3HkMVtJ/jPJ7rZnYw8msp9KTVoFAMIDly5dj46aNSHa/g6MP9bMUuMy1t7dDCAG/X/tiyZYBRESaM844o9BLICpaDFrSkCp8ztk2+3qFUd5jZp5ADWjDeYDh7cSO9SCeWCq/gGQhMi3lNA64qaeiCMvz57R+LZgsYV9XJtrcuWcXmjc1Y/MLT2PR8T8xdjIlSYLPowUq2NNy/Gtvbwe81cDs/4d0OgaP/HDWDmR0oALcPMTJ79WyNP/8p3VAjfYl655f/hovb/gdy/xKhKwAgSEuk5SF8eOF3ytZhrYxE7u86Z/hgWAAS5cuRd1EF+bPcA9+JSp5dXV1kCQJyWTSyLRkywAiIiIaDKeH05CCOYKWqTTQE8s+3T50Rw9WyoqwZP0NRhnjoKVeAjuUQmRaKqqwlMsP67pCy4rUr+/Ud1Av33/hxdeN0/zuFKqqqvD2tm1Zk171rM9UAQK4NLLq6urgm/UFKOFTkZ7ySaS8h2btQBqZluagpUdCU1MT9ux+zziteuIkTgYuIU6tJOwSMiAPvKf4PdZMS04PL2/2z/pCfHZS8WlsbER9fT06OzvR1taGzk62DCAiAoB169Zh3bp1hV4GUVFipiUNKThIieC+XhUTKrOzJ3piAoGBYGfcFsCsyCNrULVkB47+3k6/Q9BSCIG2iMCkagmBgZ3xsViLnTIwAdx3AD8x6EOMFKH9QiE7BBqTA3+fzp7M5AwpHYXH64UQalbZllYaLFgeXgIaGxtx/9+eQGTgeEypsOxAyopwbO8Q9AHvtrdDKJkXs9dfwTK/EpLPNPiEKdMyYM+0TDFKVc5U24+NY/HjIxW/UCiENWvWcHo4EZHNzp07C70EoqLFoCUNyeOW4HVnAl5TaiSjn2VHj8C86dnX6Y4JTJ2gHTZPkU3KAhX+oYc7WHpajsHOjtPk8D+/kcZjL6UxbYKEa7/kh9slFSRbRBUHvsOnP4+KovWjdC4P1/73BCfqw6EhpfsgyzIAkVW2pWdayqaszYMZFkSFEwqFsPTMz6L5b9rx0z6+FHdftTwzhCee+Rv3ZgaFo7bKhVRdHaDuNk6TFYllfiXE6QcOu3hKGJfze4GAaVAXMy3Lm/33PcEYNg0IhUIcukNERER5Y3k4DcklAV5TePuTx2aO7Ot13hPRp1ALISy9zfKdIG4uh1bGoAzZKdNy63vaYvd0Czz65F+RiCcKki1inwA+HOZMy7QiHIOuegbdlBlzjdN6I23o6+vD/Pnzssq2fAOBCXOmZXuEaTTjlpSJNM076nhLxot5qFafKYBZWyWhsbERh86Yljm/P8UyvxKST3l4n6l/ccAnIejPfKVIsudtWbN/ZuXbGoaIiIiIyIxBSxqSywXMCGubyrzpLhxZl9ls9vU6B6v6EwJpRSApW3tZJfIsGVRUoLtfG/Qz2pmWqiosJewAkIgnsOu9Pcbx9ZvewMqVK9EXdWjiOcqGk2kpbOks+nOvqrkzp/SeozE5U+b/0YXHoaGxAf95/XVZZVt6RqX59vZ059+vlIqLOfgcl61/x76EOdPSGrQMhUK44OuZAOU5XzqPQ3hKSD7l4ebs2wAzLcnE/pnFTEsiIiIiOhAsD6chuSTg25/04c33FRxziBuVAe00VQD7epz3RAS0IIfLNlk8nwniqirw0F/jeO4tYKrnX/jEodsx89tnjFowpD+prdds46aNSKaPgmtgJzwwYTZa334JTz29Dicdce6orCMXRdWzVAYvv1ZUATkNBHzW0wAgrQqowvn6eqaleer7V758FkIBCZWu7qzL64EJfV1pBYilBPoTQHVF/o+LioM5+JyUtRYC7oHfJczZlebDk6q0bSlU4QegpWOe8tGPIxQapAEujSv5lIf3xjORqYA30/sXyD+rnkoTe1oSERER0UhgpiUNySUBVUEJpx7hQXWFBLdLMoIWu7vSaFrTjPXr1yMRT1iu1xMTluEdQH47sr19UWz6RzcAYE9iKu791a9x/vnnIxqNjsjjsetPZgdeI50RSJ5MBE6qqIMQAh37IlmXHW2KKvLa4UulrYEGVRWWTEvz5HCf6ecKPZCsBy3dLqByYL6KyyHOae5dmZS1jFgg/wnsVFzMZcBJObMNpdICsWR20FKSBCZUatuAeTtKsRy4pORTHm5uHxD0aX0tdSwPL2/2zPsCzLAjonHEXilEVG7q6+tRX19f6GUQFSUGLWlILpeUleMXDmlfLhThxjMb/obmpmasXLnSErjsiTmUXeexI9vU/CDSqnaPkieAqgm1aGlpQVNT08E9kByc+llODIcBT9A4rgamQ5Ik1E6aPCprGIwQ2VkrTrSAk6kXqOlhKaq13DMUyPxFjUzLgaBUdVAbqAIAkkPQ0hKYSAsjaOkU/KXiZy4PT8gCsqK1Zdj6vmLJQNZLgav8gNulBy2ztyMqDYpi7S2cdb5qDWoHfBL8piC2eQAblZ94Shtm9+o72q8ggpmWRDSIfLL7iUrZwoULsXDhwkIvg6goMWhJebFn3KV624zDE6bNQ1VVFVpbW7Fx00bj9N6YyNpxzac8vH33HsCVKTP1BmoghEB7e/sBrX0oThmC9acsgiSZXh6eSsw+8sP45CdPH5U1DEZR85ugnkoLS2DSHOhUbJmW1Zl4LBIpLQDRNxCUMpd4ux1SLf2mEtCUDHTFmGk5npm3i6SsBbff3aeiqz9zuhDCmCReXZE53Ztpg5rXa5vGD1UM/r6TkK3bTsAL+H2Z9wb7D1ZUXjZtc+HRF2Xc+2wKu7tUZloS0aAYtCQiolzY05KGFI1GsW7dBuzv7Ea4NozFixYDqU4AMwAAqq8WXu+7EEIg0pkpn06rQGfUuqciK9qgD6dgmG7K1Dqgy5+5jvBBkiTU1dU5Xj6REgj4Bu/3OJj+ZPZpqssPwHrGZ//tIvj8wewLj7J8B/Gk0tZhB+msoGXmeFVQgt7JMylrZZ76VauDmefSYwpK6QKmd41oIpNtFU1kX7YUxVMCu7tUdPcLnDh7/L+FyrZMy329Ah091g0unspsTzWml4ClzQB7GJaUod53krKwvKcE/ZJlEE+cmbdlra1b+xwRAP61T8Vhkxw+TIiIBshpAP4hL0ZUspqbmwEADQ0NBV4JUfEZ/3vcNKqi0SjOP/98bPmXF3BpwcOXW15G3fwl2DWQRSl8tZBlGZIkIVwbtlzfnImjS8pAxSBfTD73hS/jrlszx/sTAvX19WhsbHS8fH/ywIOW73YojmWtToHMjqinIMMEMoN4Bmd/ru2ZluZfsWsqrGW95iE85vPMQQjjNNNzbZ4eLysCSVlYMjFL0bY2BV39Al53aTxOc+ApKQN7e7I3cvMQnpqguTeqOeuWQapSIoYIWiZS1veUCh8sg3hYHl7eYqnMtrC/T+T1GUZE5Utrb1Qa36uIDkQkMvZzE4jGC5aH06CamprQ0tKC6uoQamtrjTLwCnfMuExShNDX14d58+ZpWZhDGGpAg+SptBz/9Gc+j9/+9nc5p4f3Jw+sgff7+1Xs6nDeK485lDq3dWYG24yl4WRamoMIadNiFVVYy8MrrMN0euPO5zkFIM09LTt6rU9IOfS11J/jwfr9jScpyyAe58dk3j5qKsxBS9PtMNOypAz1vpOQre8pQZ9keW9gj9PyFjcFrff1aJ+dHLRBRLnI/A5BREQ5MNOSBtXe3g4hBLxebVPxer0QQkCJ7YdL0nZsa6bOxacWNGDxosUIBAND3mY8BUwc5Hz7YJzjP3wy/MHcqZmptICsSJYAylA6elS07sndQCfm0I/trXe68eRTr+LDh348ZwB1NKiqgKIM/etzUhZwm7L/zJmWqrAGlSr9EjxuIK1owYUeU6aluTzcKdPSHJjo7BOWoT7RBBAeu6emIPTnsVR6tMm2TEsner9TAJhg6nlqfs0xSFVatEzL3JkvWk/LzPGgLdOSPU7LmyVoOZCRrwqgRBLUiWgERaNR/HbNE4h3vo26ujo0NjaO6fdsKk5DtRMjovLBoCUNqq6uDpIkQU4l4Q0EjTLw2kkTMalbQkevgOyqwadO/3TeHyxDDWyJ2coK4ymtz94htc63L6e1nefhBC27ooOvwak8PKZWoumBR/DWpnvRtCZ35udIU0R+AbJUGvCYMlksPS0Vaxam36MFJKOKFnzodQha+jwSXA5/U3Ngwj5soxyG8cim7DJVFY7P0Xhinx7uxFwebg5aVvjN28KIL40KIBqNoqmpCa/8y4cj61y46IIvOr7XJWw9LSv9tkxLDuIpa+by8H0DGfmqANjZkojM9DZUr2yLQOndAUmS8OSTT2LNmjUMXJa5tAK4WRNKRGB5OA2hsbER9fX16O3uQmdnp6UM/JBJ2uaTSgPtkfx3UKNDlBDHbOcnZKC9K3edoqw4984czFBDQ8zl4SLVBwCQJBdCk+bh5Ze3oKmpaVj3dzCEqgUdh2IvD7f0tBT2Sb+SUfqdlIWt/FcauIzz/ZiDlvZynlIPWqbSAuZHWArZlrKlPNz5MubtY2Jl5nClKWhZDq0BSp2+83jDDTfgqaeexso7fobzzz8f0Wg067Ip2RrAD/gkSzuJBDMty5r579+f1Coo1AL0hCai4qa3oZoQnowZM2agtrYWLS0tY/o9m4oTJ8oTkY6ZljSoUCiENWvW4PZ716Oto9+YHh4IBjBnahpbdmmfKDv2qDh0Un4x8P4hpkzHbFmOiZRANKGVMJuHxOjktEAqPbxst6GCnOZsT6XnbXgmLwAAuEKHQEgutLe3D+v+DoaWaTn4eoXQ+8tlngdlkOnhPm8mKJmQYS0PH8ikyzVQx2cKZtq/UMSSAkIISNL4zj7Mxd63UVUx7lOHLIN40sj6+yXiCfyztQ1AHQAg4M7UiptbA9hftzT+6DuPtbW1cE+ahLSny9h5vOiiiyyX1X4syhwP+qzTw4fqXUylLSFbPwP29YmCDLIjouJmtKHyVwAy4Pf7IYQY0+/ZVJzSZRa0XLp0aaGXQFS0mGlJQwqFQvjCOZ9HQ2MDli5davStnDsts/ns3Jv/3og+ZTqXuO28+EDGxu4c2Zb2ned8DNVvzZw1pva8bRxWfFMgSR7U1dUN7w4PgppHpqWsAALWrDlr0FJYsiL9XsnImEwrwLtt3cZ5PkmLPuWVaWlLNVRFdnl/KbE/3lLYCTcHnoWwHk/EE1i5ciV2/KvDOO2un/3YyLyrMLWatWdI0/ij7zz6/X4ALvj8wZw7j2nFWh7u9wJulwTPwMcCMy3Lm71dxP5etSCD7Kg4qargYCYCkGlDpScTJJNJSJI0pt+zqTjZv3OXurlz52Lu3LmFXgZRUWLQkvLicsicqwtnMmt27lGG9QV0sFLSrPLwgd5oe7tVpB0+wGRleFk9mazE3MxrmDExc1gWfpx4Uj0aGxvzvr+DpahDlyHrwQNVwHiOFNMeoqray8OtA3Ui/dpbgUjH8POf3YFEPGEJTpr5TfnZTqUb8VEMXikF3uvNyrQsge9T6bT99ZY5vHHTRrS2tsITmGCctv31vxtlWx63ZGwP9v6mNP7oO4+JpLYRpGTFcecxrWhtEizTwwfeL/RMbGZalq+0og3HM9vXw/JwyuiMCpZ+EgBTG6q+GNra2tDZ2Yn6+rH9nk3FqdwyLYkoNwYtKS+Sw5bidkmYNUU7oyemTZLOV3SQEvGELUNDz9hRBLC3x3ofQoisjJ+hpNLAUCs1l7pecmHmi9OMQw/H/66+Z2ynh4uhMy3NAQJ9R8BeHm7OtPR5JEtQUvJqj0dK96G1tRUbN21EwOd8X/5BeloCo5thVeiJxPbHWwo74SnbtpU0BaIinRGtXNxbpZ2gxCDUpCXzTh/Gw0E845++8xiJaD2Mu3uijjuP+o6EOeigv1/4Pdr2MNzsdyod5h64un29oiR+5KGRsaeLQWzS6G2ovvr1b+A73/kObrjhBg7hIQDlF7RctWoVVq1aVehlEBUl9rSkvLhztCicO82FbW3aN8+de1VMqs4vDj7YwJbsTMvM4UifwIxw5rheFj2cQTz57EzrmaA+DxCeEACgRVmrJkxGMFgxyDVHnqoKpDF4j0jzY5LTQNBnG8SjAilTumbAB3hc2d8GXOkeCCEQ6Ywg4JUcg5LmKe1Oma+jmXGXTAvLxOqxZt/OtF6j47t/p/1LoTnoHK4NQ5IkCPfAzoMchSSEJfMu6AO6+plpWQr0ncff/u4BbHnPh5lTA7jyW5/J2nlMD7y32HtaApn3h+Qwh6NR6eiN5Qpajv/3Szp4qbRAZ1TFbMG8CdJUVlbi9KVn4hMf+lyhl0JFQlVFwauriKh48BsD5cWVYz9jzlRTX8s9+f9sPljQ0p6plzBlEdp3hPVMn+Fk9eRTtqgHTiv8Wo82/fGn0sLYYR8ritA+vAdjzbTUDqcHybQU6QTefvVZ6/1EP4DY/SdIkoRwbdhSPm7mH2QQD5CdKTuSCp29VWqZloqaPRzDvC0tXrQYc+YdBbi15pVKshvHHvchS+Zd5cAwnoQM9igrAaFQCN/81rfQ0NiAz3/hS47ZLvp7TDyReQN4+IHfIRqNZoKW7GlZtnocg5ZqSfQApoO3t1vkVUFC5UP/vjrUd10qH4oojRZMRDQymGlJeXHlCG/PmuKCS9I+WIYTtBxsynTCPognZT7Pelk9iDSsoGUel9XLwyv9gCRJ8Hu1dSTlsQ1U6V/gFJE91dnMkmmp6Nc13Y6wXubFv21C+z8eRcXUHejr60X/vu1Q5RgCgQCOPfZYLPn4Yvg8uXpamgfxZJ9vH6Q0UtKKKPhOjn07G+874U6ZtOZgUyAYwDe/czl+/Lh2/NDpE3HnD1ZaAlkVA2XB+oT6XMFuGj/0HYVcWQ5pRRvS9Pa7ewHvVAhVwW03X4vnn3kU3oW/BVD4HxiocPocysN749qPlZOrC7AgKiq7u7UPzlKoVKCRoX9fVQWzaUijqtpwSCIigJ8NlKdcmZZ+r4RDJ2ln7u4Wg2ZQmqkC6E86n2fvjWcOYqbSwvJLrJ7xIysi719oh8q0TKUzDeL1UmQ9EJNMZ2emjSZz9fVg95u0lYdr1zVPEheWKXw9Xfsg1DQC8vuo9XVhYk0QPp8Px3zoGCxfvhwTqoM578tv6nX55pvbsH79eiTimSaliVEqE1ZF4YOE9kmG4/0LlVPQ2Z4hlxKZEeHzZk1DlS3zrtJUrj/YgC0aP4ygZY4fCdKKNqQphUoAgCR3oTY8AS0tLYj2RrTrqs7tI6j07euOO57e+n7/GK+Eik00kfmeON4rFWjkpE1BSyJA+w7B7YGIdAxaUl5yJPgBsJaI79qb/7fQ/hwBTnumpb3c2ClABzgHYJw4ZZeZmYfwVAzEa/TswqQ8toEze1/KXOR0diDXHHBQhTUYNWXSBAAqZFmGy+VCRbACFRUVWLhwIQLBgDEV3nFNqcwO6bvvtaG5qRkrV640ApdpdXg9RvOVVoB0kU0PL3QQ9WDZg7BA9uvPnDVVFZSy3gsqApkTnHrZ0fjTnxB44z0l549QaQXYt78nM8AruR9+vx9CCKhyzLjcaA7louL1542bM0fSmUDlH55pKcBqqJjs6818aI73z08aOfr31fH+QzCNHJXl4URkwqAl5SVXpiUAzJnmNg7/a98wgpY5srKSg2RaAtbgmzlQmauHmr3P3lDl4eZBQHoWmU/PtJSB9Bh+01bzzLS0D+IBrFmagDXD9Myln8KRR8xFX18fOjs70dfXh3nz5mHxosUAYJksbven9U8ahwPBEKqqqoyJ47rR6GupisJPErQHY8f7FyqnEl7768g8Cbg6KGW9F1T6nS9L49e1zXHcvT6F//2T8wtZVgT81dMzJ6T2I5lMQpIkVAQyXWfy6R9MpWdfV+aHLVeizTjc1lWI1VAxSZk+Xxi0JJ3eimS8f6eikaOUYXl4Q0MDGhoaCr0MoqLEnpaUF/cgUcsJgcyO7Ztvf4DTj5mOQDAw5G1GE86n23siJlLWfo7ajrB22JwppgWUste5u0ugLpw5fagdaXMw1SgP90gARFbG4mhT8sy0ND+m1EBgz17aaQ5Q1VRV4PrrfoCnntmISGcE4dowFi9abPzdBsu07Ny32zgsXB74vF5j4rguLgtUj3CvqrRS2J0cVc0ewjTed7qcso6zMi1N2ZNVTkFLU6alUy87Gn/efE/bsHfucf6VIK0Ac+afhOf2aMfjXe8i1dmJ+vp6TD10Gt7bpp3OYTzlyR+aBAx8vkuJdiB0BABA9U8t4KqoGMh5fqeh8sLycLJTVVF220M4HC70EoiKFjMtKS+5ysMT8QR+d99dxvF3P4hYSoUHk6v00L6jK2ynmUsOzUEXp6yxvrhAR6/1m/FQmZb9TuXhXufzR1s+5eFpRViyKo3ycHumpelx+zxAVWUQS5cuRUNjA5YuXWoJNAd8uQOOddNqM0ckD2RZNiaO60Yn03LsJ7ebOW1fuQaVjBdOZfz2119fwppp6bZ9aoRMPS37hn7Z0zig96LMFXRMq0CfnHlTPGH+dNxwww1Ys2aNJdPSHgCn8nD4vGOMw/373jYOe6sPK8RyqIiY+9yO849PGkH699tyy6yj3Mqxp2UkEkEkEhn6gkRliEFLyos9UKHbuGkjdr79BqBqe7fuwMSsUuFcErJwHNrilAlpDlSazzeXhzsFYPb2qOgzzQQQQlj6PzqJO5SH+72FGTZiLQ93vl97MM0oD1cEtr6vYONbachpYXnevO7cE+GBwSdAn3fuF0z3LbJKy4HRGcaTVrTHVChOPVPH+xcq50E81gfVa3r91FQAHrc1oF3hZ6bleBONRnHPPffghhtuwD333INoNGo5X98ukmkg7fBLQVoBIn2Zv/WXPnMaLrroIoRCIQRNP3iwp2V5SqYzHyCnfPgIuCVtg4omOSm63JlbvIz3H/1o5OjbBYczkU4RgCiz7aG5uRnNzc2FXgZRUWJ5OOUlV3V4pDOilW6n+yB8YcBbnVUqPJjumMA0W1afUyZkPCUwoTIzDEeXtpSHZ1+vo0ebmp2UBfxeCam0lrk5GEt5+MCkbL/plRIby0xL08/OubIMk7YgrB5w6OhVcdf6lPHLdcqSaSlBiz05PxuD9bSsnRAC0KcdnjQVH29ssJSWA9kl/iNBUQtbTuYUFB/vX7Cdy8Otx82ByElV2ZHukKkTRK7saSoe0WgU559/PlpaWoy2G08++STWrFmD0MBkeHMwO5oEJti+KaQVgUg087eeWpM5L+DLHGamZWkxt2kZTNT0Gfq5s5ei9Ykk+pNsF0D2oGXh1kHFRWF5ONkoinUfiIjKGzMtKS+59lPCtWFIkgQh92oneCohubyWUuHBdPcfQKZl2jlQaQ929sSEsdOsDwjJZ6fJXP6t9+szZx7GUmP3Tdv8pT5XgMwerE0rAmlFYG+3MAKW7RHVmmnpAdxuOJIweE9LnymAEZ40Jau0HBid8nBFzR24HQtOQfHx/gXbuTzcnmmpHfd7tJ6WdlWmnpZRZloWvaamJrS0tKC2thYzZsxAbW0tWlpa0NTUBEDLfjK/7zhlz8oK0BnNXGhydeY8c6alfagajW9OmdlOYqYfLwJeCd6Bzwyn91AqLzKDluRAz7q1D86k8sXp4URkxkxLykuu8vDFixbj5ZaX8a94JzyVswAAhx9xPE5eeDLWr1/vOOTFzB60VFThmGlpLjdO5Jgebi/77ujJfCOOJgQmV2dnJTqJOQ3iMWUexsawb18+PS1TtkCsKrSdw7jpOYvbggc+jwS3y/m58HmkQbNpXC4JXrf23OfaiU3IIu+snHwpavZwobHU09uP9eutg4uUmorCLWgEOP39zK8vIQS6BzLqaiok+DzZf89Q0Nw6YcSXSCOsvb0dQgj4/VrDXr/fDyEE2tvbAVgzoQDngWnm8vCaCmt2ZdB0eDQyrqlwUmnrj1a5mKsVAj7A69YG2Tn9SELlxdLTkkFLGmCUh/MtggaU4/RwIsqNQUvKS67y8EAwgOXLl+MXj3+AXQNt0c459+tYteoXaG1tNQJXL7e8jOXLl2cFLmMpbUdGD4bs6xVDlqym0gKqKiBJucvDhRDo6MmcF41nXyYXc/m3Xh5u3lHrHcMSWHOgMp2jn2PMoX9kQhaW5yyWFPCaMit9ntyBaE+ODEwzr2fwoKUeOB2sN+ZwKQ7Tu8dKNBrFlcuvxlvv9Fq26ZtvWA6gqjCLGgHOPS0zh/uTmQzm2irJMWBRaXpJ97M8vOjV1dVBkiQkk0n4/X4kk0lIkoS6ujoA2dtEr0OmZTypGr1OwyHrRPkKc09LZlqWFO2HwaF/iNJ/vJAkLUNbz7QcaggelTZZEZaGNIWsnKDiwvJwslPKcHo4EeXG8nDKy2AZc4FgAMcdNdM4vuXNHWhtbUVVVRVqa2tRVVU16HCeLlO2pb2MWWcf7JJMazvX5lPNWRwdvcKSValPQHa6bTtzlkimPNyUTZZwHiA0Gswf2Lk+vHtjTs+X9TnTgsOZ873u3EFLbx5BSz3InB5kJ9Se3XmwtJ6WhfkG09TUhDffejtrm3563TMFWc9IGao8vNM0bKW2KlPmaRYyl4eP4ZAqOjCNjY2or69HZ2cn2tra0NnZifr6ejQ2NgLI3ibsfUpVVWCfabsIh1wwz2ayZFqO0fskjY1UvuXhA+8DAa/23cE38Jkip1n+Wc7sWdwqIxI0IM2gJdmwPJyIzJhpSXnJFeDS1VRm9lo7e9MQQsDr1dLsvF7voMN5uvsFptZoO8fdMeGYDRm3lUAnZWQFUNJq5pe51t3Wn/ATspbROVSmZSKewJ59vdCy5wREOgH4gpZBPMk00BMXCPhGrvQ5F/MHtv0LP6A9XqfhJwlZWJ6zWBJGYMElaROgPTnSZ73uoR+X/nzIikAsKfDyTgVzp7lQF85sKFo/0ZEtD1eFtqPjypX6O0ra29sByWNs0263G4lEAs888yzqKjrR2NhoDDEZT5yyms2ZluZhK+Eq5/LwalPQMsagZdELhUJYs2YNmpqa0N7ejrq6Osv2a98m7O8vadW6XdRWSXCZPh/MmZYMWpYOIYTjZ5AT/X1A72/q9Wjl4YD2/mJuJ0Dlw7795CgeoTKkqAKRqIq0wlwa0ihq+bWQWLZsWaGXQFS0GLSkvAzVmrCmInMBd2ACJEmCLMvwer2QZRmSJOUczqP3tWyPqFCFcO6zZ8+0zBEQS6WBdztUxwyyaEIMmmmZiCewcuVKRKq/BlewCkLux5133IPly5fDb4qQJmVtOIV5Yu5oUVSBzj4VFX7J8RfHvrjz/O+EbMu0TApjuI6eSek6iPJwvexbVoDfb5bx120KqoPAjxoCRtBzpAMWevlQWgV8Y/y9tq6uDpLbD1mW4Xa7sXvPbiQTSbzdugM33PCHrOnL44VzT0tzpmXmG2NtaKA83HYd9rQcf0KhEC666CLH8+zbRL+tp2VasQWzQxJcpg+IoN8cxD74tVJxUEX+g1NiA1n29s8cQPtsYtCyPNlb3JRbQIJye/IVGb/dJOOEWWk88O+VhV4OFQGVPS2JyIQ/aVFehkpsMwctayYdhnnz5qGvrw+dnZ3o6+vDvHnzsHjRYsfr9ie1YOLeHms/S3N2Z8KWaZmQtSw/uz3dKnZ3O38T7osP3tNy46aNaG1thcurfWGS1LhR1u43ZZilZOFYkj0aXtmZxvUPJPGfDySMEnczp35zwECmpak8O57KBCP0oKTnIIKWesadrAA79qgDawHe25957ke6n52+g1OIiaONjY048qhjEfMejt6a05FGCP6AHzMOOTRr+nKxMwf0HcvDTa8Rc3l4rkzLSlOQKs5My3HPvk302/6mslPQ0vReEjD1sWWmZelQRe6+ymaykqloyGRaZs7Pp0ULlSbZ9tnNnpaka9mhfUF97V3FsXqIyo9ShuXhO3bswI4dOwq9DKKixExLysuQ5eEV5r52LixfvhwbN20ccnq47u121bKzAwDVQcnod+mUaelUxvxuR+5vwX0J58nkukhnRPtVz62tU1ITRln74aYd8WRay7Qc6enYTlp2al/k+pPac1Q/x3p+T47gaSIFxBKZlClFBaIDAU59B/JgMi31gSxpBdjfm1nD05u24dufnYNAMDDik4PTA99eChG0DIVCuOzKFfjBgwoU4YbLX4PQvkcgiVTW9OViF09l/n5O5eFpRcvwdbskdJrLgEMu+DxAwpY9pw91UlSgn4NXxj37NmEv+deyv+2ZlpnzA5by8FFZIhWAEM4tSuzMP+jpGZU+02e1/QdIKh/ZPS0Lsw4qPuaBkqk8B35RaVPV8gtarl+/HgAwd+7cAq+EqPgwaEl5GSo2F/RpJWCyAvT0CwSCAZz28U+jMypwSFgaMri3b6AM1ZzlU10BdPVrh+07OknZeTL1YJ9vfTExMP3UWbg2DMlbAUhaNE/I/UZZu3kQT1IWUIQWSAwNxGHNE9BHkjlbMZrI/oafK+Ozpy+Oba3vAv5Zxml9cQFIkhHszZVpmVdPS9Nzbw4ivrEripUrV2L58uVIeoND3s5wGOXhefZVG0myIpAQfihCi9h5az+E9J5H4HO7s6YvF7tEShg/MjhlWgLa66vCD0QGglMel/Z69HkAW7UwJElCwKu9Hsox0zKR0qbamwcSjWf2YSv2oKWczu51av5RK8ieliVJVQd+/IpGc/ZDBYAu07YR8DLTkjLSafsPIAVaCBUd83fdoXrPU3lQVMFhXURkYNCS8mLeKXVJgNslWcqzJUnChEoJ+3oFeuJaufd/PZpAdz/wpXogvXdTXlmX5i8rWmBFuw/7zm8y7dz7cjBDZf4tXrQYL27Zgb0Dx9OJXqOsvduUXaYPKumNCSNQ8d5+FeGQhHBoZDsu9CcyT8hfX3wV5596rLGDmEhZJ6SbPb9xI2LJQ+H2m04cCBxnelrmGsQz9Lr8OQK07uq5aH3xf7Fx00acecbSoW9oGBSjPHzsf4WX09Z+jZLbj5g0FX2dW6HYpi+PhbQi4MkjuOwklc4MMzK/hiQp0z8oIQsEfTAyLcMhCV6XlPM+Az4J/UmBWGpsMpCLiawAu7tUHFGXxwtnHLC33bBnz5oH8VT6tcBU7vLw0VoljTVVAL3RGM5f9lW0tLQYr3N7P9+u/kwkSp8kb/5MSTLTsmzZy8EZkCCduZe2UwUIlZ9de1W89YGCk+Z6LG2IiKg8MWhJebGX/3ldQE/cepmaCi1oGUsCb32gonsgS/IPf4+g98VmYyfn5ZaXsXz5csfApb08XGfvj5iQRz7jLhAM4Ctf/Sb+e512fN7sGbjsHG2dPlNKgB4o7I0L1EHbyW+PqEjILoRHcA5LNBrFlte2AsFjAAAv/O0VnP/yrcYOYk+OfpaAVuoueeY5nucdCDjmKvnPpzw85+R0TwUQnIZIZwSqGNkMVPMgnrGWTAvEbH2WPvSRL6FOzMRJMz855tPDU+n8/k5OZEUgrUrwuaxDV0IBre8roAUW+pOZAEOufpa64ECgKpHSepvWVBzY2sajVFrrxzt32thPtR8N9h1Ge/ZsIqUaw9PCVQPvJaaHzUzL0qQK4Ol1z6ClpQW1tbXw+/1IJpNGP199sJNeHQEAVUGtdYDX9N6RYKZl2XKaHl5uP3JRNlUVlmqqXBUgVD6SMnDzYwn0J7XPkSvPyt1ejIjKAwfxUF4kSTJy2yp8kmPQytzXcsuuzLfTtCeM0JT5qK2tRVVVlTHcxok5czDok4wMDXuWZCo9+CTwwbRHVPyhRcburuzo196+TJrQ8UfPREWF9kEZsJSHa//rQ3DaOlWkVWB/r/PU8gPV1NSEXlN6XyA00TLwZbBhQOHaMOB2Ls/2DjmIJ4/y8EF+7vDUzDUmxY9kmY+RaVmA8vC+ePZAkoT3MHyloREXXnjhmE8NH26WsZmiZq5v3l6rAubAQqY0HABqqySjD6YT/f1AVoCOngL8gQpIVrRAcEdvaexo2V+z9gnge7uF0WcqHNI+F8zBWvNkaHsvYhq/hAA69kUghIDfr6XwO/Xz1QPagNYyocJvfe9gT8vy5fSDI0vEKZm29rNneTjt68tUN7Xu5psEETFoScOg75cGfZmyLzNzdtWru6x7ulLtiQAAr9drDLdxYs7y8XkyO8BOk6ij9uZ6efrtphTWv5bGbzdm3+g/P8gEXObPcBvl3z5LTy7t//6EQCot8EFE20lTBbCne+R20tvb2yG5M78uuryVlh3EvkEyLRcvWgy31zndbSQG8Tj1E9XVzPiwMSl+JIMWSgEH8fT0i6zgzQcRgZ6YKMh6DiY4nlYyrzPz660qaA7MC9sQniEyLU3vB+2R8gpU6TtY7Q4/goxH9vJwe7bku3szKfZ9ne8hlbS+EVsyLRmgKhmqAMK1kyFJEpJJ7c3QqZ+veThcpV8LWprLwxOp0nid0PA5TZ9nhTj12aq2DuZHWSoN5tYy5dQHORwOIxwOF3oZREWJQUvKm55NU+GXLJmHugpv5puGaus8oFQdCwEJsiwbw22cpOxBy4H7cSopy9XPcSh7BwKL73cKqCJzG4oqsL1d26EKBYBDaiUj+8zjlozMRP1+BYC3d1uzK0cycFFXVwdzU0pV8hk7iKoqjExPJz6/H8LlEFkG4Bsi0zKvnpa2v79HSsMF7Y8XmHSUUfo/2LT24RAik91lD6qMhZ64yMq0BLQgdyF2ug4mEyGtZnYezUNXzEHLhAx09mW25aEyLStM/Yb2941sxnGx04d7dfeLrKE141FWebjpvTcajeLuX/7WOL7tzS247ae3IhqNGqeZf9Aop52NUqcKgY8v+STq6+vR2dmJtrY2dDr08zV/LoUCQIXPWh7OQHb5cgpGMdOS7N9ly+n7Azmztgso3DrGWkNDAxoaGgq9DKKixJ6WlDc907LCB8c5KO3vbQMw3/nKvgnoTk+E6NthDLdxYt7J9XklS6blSPQ+MvfOUVSgNwZMqNSOv7tPNc6bP8MNlyTB7wV8HknrzegF0kkgZfow7eixfuOOJQW6+lVMrDz43wMaGxvx89f2Qv+8lhU3TqpfiMbGRvTExKDBssFK8PTp4C6XVtppv5lcwUwze6blIZO98HskbG9X0dUPRKIqwiFXVtAimhCo9GPYf0dzWdlY7+TEU1pGrVNA6p8fqFALsNMlH1SmpdbT0n47WZmWpvLwcEiyTAC2q7D0MdQmCE+dUB59ylJpIBFPYOOmjXjywR048tCKMe9xOpLsgQVzlntTUxPe3x1BoFY77vcIbN++DY8++ij+/d//HYD2/uJ2aa9TlgKXDiEAry+ANWvWDDo93FwBUBWUUOmXjB/KACBuy1in8qEogP03UQYtqS9mD1oWaCFUNOKpzPdHbg9EBDBoScOgD24J5hjCIvfvzzpN6d4G9wQtkHn4iV/EyXXv5z09PJNpqQXoZAWDZnvlw74T3RlVMaFS+xr9zw8y356PmqE9WI9bQtCr9dvxeyTEkrknduv2dAlMrDy4dQJAKBRCzUQVnQNJTIfOmovfXPNbhEIhtO4evH5msGEH5ufQ47ZOgXdLuaeKm9kzbadUuzCpWjIyVXfuURGe68qaFBtNCAghocq53WZOagGDlnq5Y7/DzvY/2xTIikBgjKeZH8yXOEXNDEQwB6hqTH+TpAxreXiVa9Dy8ArTlPrEQGn51AkHvsbxpKcvhpUrV6K1tRVK37sQ0XeyJiqPJ/ZMZvN7SXt7O+CpNo67JQVCVbBnzx7LdfweIJZiT8tSoqra4JTKykpj6I6TPkumpYTKgPUHDw5nKl9pRcBe/8GgJfXZhhwezI+yVBrMnxPllHm7efNmAMDChQsLvBKi4sPycMqbJGnZln6v9s8e25o80Z91ndQ7v4db0iIjfe7D8clPfTpnwBKwfjj5PZKlV17coa/lcNl3mMzDRra1Zb49z5+hBTK97syQET270B6Is9vfJ6COUM2wJagUnopghRYEMQeUAC3Ta/369Whuasb69evR05c7ncVnypK0/w3zGcIDZAeup9ZImDM183aya6/2XNoDvLGksPQ8y5c503Ksp4f39OtBy8y6D5+iPf5oAni7fez3ulIH0fMprWQCU+bgp3mQVlIWxmvD7QKqKwb/wcBcHq6VlpfPl8yn1j2L1tZWVFVVYcrU6aitrbUMzBpv7AFxc6ZlXV0dJG8mEKum+iFBxbRp0yzX8RltPUZtmTTG9I+0wd5/+/r68M+3/2Uc90oJBH2Z7H6A20S5EkLAKfYwUt+VaPzKLg8v0EKoaJiTBMppe2hpaUFLS0uhl0FUlBi0pLy5JC1YJUnaP3u23acW1VuOC7kPc+r8OO4wLQDYnwRee3fwaIv5w8nrsWb0jUTWTswW+NSDf7GkwLsd2t7YtAkSJob0XpaZISPmoKUQudciKyIrqHigzD0hE7JAWtXWai5VTsQTWLlyJZofeBTr/rQRzU3N+PX/Nee8TfMOpNtWqzVYCbCZvTx86gQXDqnNvJ109IiBNVsvF0vhgIKWlkzLMe5p2RPXt5HMaUfUZZ64QkyNPqhBPKp5EE/mdqorrIGFyMA2HA5JcEmDD+KpNP0OkZS118Bg0+1LScd+baKy1+sFJJfjROXxxN7T0pxp2djYiElTDzOOx6JdmD//CHzpS1+yXCfzXlke20A50D/y0jk+wqPRKBq/9g28vfN947Sfr/wx4rF+hPwj+zlO40+u4SrMtCT7UM2D+VGWSoM5SaWcMi2JKLeDDlpu3LgRLpcL9957r+P5O3fuxAUXXIDp06fD7/dj5syZuPjii/H+++87Xn6o+/r0pz+NKVOmoKqqCh/5yEfw0EMP5bz83r17cemll2Lu3LkIBoOYPXs2VqxYgb6+vmHfN2kZV+bedQFbnc+E6iD8pqDX9Ook/mP5ciz6UOaCz2/NP2hpnh4OjEyGRq5My7fbVSOT5KhDMi8LjzsTnNWDNgJDTzfc23PwH7JCCMvzkUxpX/AjtoDoxk0b0fruXlScfCsqT/kpQpPm4oPdnTlv15wxZ0+szGdyuP02AGBKjYRQAAgMBCv2DQTyUragxYFnWpoHJg376gcsrQj0J6yZlgGvdVp2IYav2ANLw6H1tNQOm7evCRWZ7b4rmun9Wlulb/+5b7PSkmmpPR+RaHnsjU4IT4EkaUPGILkcJyqPJ/bycD0IDWgtKz580keM807/5Gm46YYfZpXBBwa2lZEaxDVe9cZEyQRu9bfgXO+/TU1NeOXVN+AJZLaFbf/YjKamJks7EGZalqdcwe4CzNWjItPP8nCyiZl6Wh7M910iKh0HFbTcvn07Ghoacmadtba2or6+Hv/3f/+HCRMm4KyzzkIoFMLq1atx7LHH4h//+Efe97VmzRosWbIEzz//PE488UQsWrQIW7ZswXnnnYcf/vCHWZffvXs3Tj75ZKxatQoVFRU466yzoCgKfvrTn+KjH/0oent7D/hxlytJAoKmCnCn3pY1lZnTPnrcNASCARwx3YW6idrpu/aq+Ne+3MGM7PLwkc3Q6O2zplru79W+Sf+zLfONWi8NB/Ty8IH1WKbiDn4/nb2qMaE5H06voVQ6k90CaFNXVVVgf5/1+Yt0RuCeeAzgDgIuL1zhYwFXdqm+zpwxl5VpmUc/SyB7evjkai37dnK1dnpnVBjBMfPzEE8KJOTh78j39fXjl4++ilvufw0P/f4py7Ti0dQbF8agIj04WeGX4Dc9h7ECZA4daLmMqmr9YfUdSHP5fo2pD2tbl2lyeGjooKU1k0r73x5cL0WKKrBo0ccxb9489PX1Yd++TseJyuOJfQchmQY6urXtIa0IyGrmTWPxx05BKFSRdRv6+8NQWemlTFYEtr6vlEwmmVEeniP41N7eDrh8cHkyEUo11YP29nZLFne8RIK4NDzmH3oTKYHb/pDAfz2awL6eEnmB0AGL2oKW5VQOTM7MmZZDJYkQUXk44KDlhg0bsGjRIuzevTvnZb761a+iq6sLN954I9566y089thj2Lp1K2688Ub09PTgwgsvzOu+9u7diwsvvBCVlZV48cUXsW7dOjz55JN47bXXMHXqVPzoRz/Cli1bLNdZtmwZ/vWvf+EHP/gB/vGPf+CRRx5Ba2sr/u3f/g1vvPEGrr/++gN96GVLLw/XOQYtTTsnc6ZrO7eSJOHjH8pEPJ57M/c3kuxBPJnj8YPM0EjEE3jsiXWW03a814lEPIEdu7Uvzi4JmDfdnGmZKVE3l6oPFXRThNbb0k5VhaU3os5pyIs9gy8pa5mX3f3W08O1YUj+iZn7dlVC8uSedOMdgUxLc0bthErJCFJMrtaeOyEyQSs92yopCyOrYjjZltFoFN++7Aa8GpmP9+Uj8eBze3D++eePSeBSf66FEOgfKGGq9FsDeCPRa3W4FFVAyaMXmL2sRs+w1APJ5gDVRNMPDnu6TJPDqyS4pcH7nYYC2ZmWvXExrMD9eCSngUAwgOXLl6OhsQFf+OKXcMMNN4zbITxA9g6jEMC/9utBS2u5eMBnzTo2Tje9b5frDui2D1TEZYFSadmXKQ93fkB1dXWQ3AGo0sAGIVQgHUVdXZ219QTLw8uSebt5eaeCdzoE2iMCm/7JiES5s38nLtfPDMpgeTgR2Q07aNnR0YFLLrkEp59+OiKRCA477DDHy7W2tmLz5s2YNWsWrr/+ekhS5kvrddddh1AohM2bNyMSiQx5n6tWrUI8Hsell16KE0880Th9/vz5uOWWWyCEwM9+9jPj9B07duDxxx/HoYceihtvvNE43efz4Z577kF1dTV++ctfjlm2VqlwSRIqTDuoAW/2ZY4ZKK2ePkHCobWZv/nCuW5UDiT/vbJLyRm0yi4PH7mdnY2bNqJjvzXDVpaq8OfnX8CegXLuoNSNjRueQSKuRai8bu1xSrAGqvIpe9zbnZ1B0J/MDngKIbICkdpl7VN8gY5eNWsnePGixZgweWbmMSGIydMOzbkuc8DRbQtE5R20NAVwp9ZkDk82HdZLxPXHaw7CDido2dTUhG3vZf5ugZpDR2XQiVMgWm8Qn5QzmUYVfskatBzj8nAhtEBIPhlc9gwGPUsq5TA9fIIpaGk+ffqEwSeHA0DINnkc0J6vLoftupTo71eBYABLly7FN7/1bVx00UXjNmAJZJeHA9pruS+uZU6bs8wD3uzexoD1/aEQQf1C29utYt9ARrxaIolkQ5WHNzY24tgTTjKCliIdx4ITT0BjY6NtyNdor5SKkTlDV/8RBACi8dL+jKCh9dt6Wjp9BlF5MSeplFOm5Zw5czBnzpxCL4OoKA07aHnzzTfj7rvvxty5c7FhwwYsWbLE8XLz5s1DR0cHnnnmGUvAEgBSqRRSKW1Pxm2vT3Xw5JNPAgDOOeecrPPOOeccSJKEP/7xj8ZpTz/9NIQQOOuss+DxWGsaa2pqsGTJEsTjcWzYsGHI+6YMl8uaXWmeGKw7/XgPvv9FP773eT/cplJjn0fCR+drfwtFBf66zTnqZ/5FzeeRLIHRg+2FFemMAB7r5HLJ7cPOPZKRRdLdvhXNTc1YuXIl5GQCkiTB5dIyCc3l4ak81hKJZmfD9SdF1k5bKp3daxPIHhokBLA7kr3HGAgGcNjc44zjU+rm4OSPLM65LnMAymN7B/DmHbTMHJ5iDlpWZw7rw3j0x2vOJh3OkJb29nat9H2A5K0clUEnThmw+vAd83mVfsmYjgyMfVBmqOCBWV/celzfcTSCl6bXmznTUnfEdBeOnekatDQcAKocMi2B0i8Rt+9clUKAymkHISkLtEVUyIqwbO9+r/OPV9b37dLeBpyYW0aUSqalPuU51/TwUCiE/7r5pwhWaln/Qb+E/7v/VwiFQpb3Fva0LE/m95X3TEHLcnx/ICtmWpJdwpJpWbh1jLUzzjgDZ5xxRqGXQVSUhh20nD17Nu666y68+eabOO200wa97OTJkzFv3jzLabFYDMuWLUMqlcI555yDmpqaQW9DCIG33noLAPChD30o6/yJEydi2rRp6OrqQltbGwBg69atOS8PAEcffTQA4I033hj0vsnK47ZmPjrtrEqShENrXZbL6RYdnYmIbWtz3vMxfzhV+KyB0XaHgN1whGvDkDzZ/dfaYmHjcFBEUFVVhdbWVmzamAlqB3yw9DFM5lGuIJDJ1NP1J7L7OSZl6we0LpbIvo/+HAGy3nhmbZJ/AtIi81yHrHFaSwDK3sJysBJgs/l1bui/RZh7gE6pNmdaan8v/fGaA7N9CWHsBA+lrq4OLm+m4aIKHyTJNaKDThRVWKaDA9p7TyZLNHN6hd+areoUcB5NerAyr6ClbRvSg+h6qZ7+evMM9G41bw/zZ7hwyRk+uF2DTw4HgFAwu6cloA30KWVdPf1Yv349mpuasX79ekRj8aGvVOScmt4n00BHj4qknHk9+zyA2yU5vtebTxvr10cxML82S6Wn53Nb03j2H/Kgg8fc3qCRaTmhKoAJNVrGcVXQnGlZGs8HDY/+A4+saGXhOgaxyf4DPcuByZxpqajIe3+BiErXEPkz2S6//PIDuqM//OEPuPvuu/HSSy+hu7sbZ599Nu6///4hr9fV1YVEIoGqqipUVlY6Xmb69OnYvXs39u7dixkzZhgZWNOnT895eUDrlUn5q7RlVnrcErxuKe9SjnDIhaBPy0xzKocGrKVjoaCEudNc8Hu10zfvUPD5eoHKQH6BNbvFixbjT9vfQMx2etw32zjsSrTD5fVCCIGuzn3G6UFbpmW+X7T74sBE02YbTYiscspkWjgOJ7B/kQP0Hb7sx28uw+2NWbOhwiHJUiZsDlray8HzzbSsC7twc0MAbV0qjpqh/faRiCew9dW/AzgFALC3SxlY88DjMe3sqgLojQMTnF/SFo2NjfjNXx6EPg9dgQcn1Z88ooNOFDX7i3IqDWMIjyXTMmAN4o31IB7V6Es59GXjSa2vpB6M1rNdtPJyYRz3ugGPy4XjZ7nw6jsqjpvpwrc+4YPXM/QQHgCoDjpnWsZSAomUcAxsjXfRaBSXXXEVtr4bgxACkiThlc1/xYJfXTuuy8OddhiTslYa3tapGu99+o9WQR+Qsr2pWjIty7A8XDG9NkthX2vXXgU/eVz75ebQWhfmTXf+oOhPZN5TAl5hVNmY2wUwSFWe9M+r3RFhCeoziE32H0I4LZoSsq1CM50ZilrK1q3T5i4w25Io27CDlgfq2Wefxfr1643j/f39aG1txUknnTTo9fr7+wEAFRXZGXK6YFArHdV7VA51HfvlnSSTSSST1tQrv98Pvz/3VOZSVxnIPi3oA+Q8k4sS8QQ8oh9AJSJ9CuKxOIIV1oEx8sAOs8etZVkmZODUI9x4fquCVBr46/Y0Pn28Q4pnHgLBAOYc8SG88b719DQG/qZChZTYA1mWIUkSpkzODLcJ+Kxl1fn+EpyVaZkEbN0SBjItnUqTs09zCgAkZJE1ac+c4RYOSXhvv7XsXpedaen0KJzNnuoypsUn4gmsXLkSra2tCJ76YUhuP1rfiyARr0WySnsd2suoI1EVEyqHvsNQKITPfv5c/GbTwOOZNB333fGbEQ0MaQNGrKeZ12vOtLQP4hnroIz+G4GWNTl4MDCZ1r7s6X/X3r5+rF+/EZHOCLYdlkIy9W8AtKClywV865M+RPoEJlVbk/B9Q7zkzOXh9vYHkahAXbj0gpZNTU14481tqJl+DLxeL2RZxvbtb6OpqQkXXXRRoZc3LHrQFchVHq793xMXRlA64NV+tHLb30Rgy7Qsw6CEuYS6FKaHm8t5zZ8lduYfI4Om9wxzEJtBqvLU2xfDhg0bsKt3BoATjNMZxCZ7X/ByKgcmZ/b9hXIJWu7cubPQSyAqWmMWtLz22mtx++23o62tDb/4xS9w5513YsmSJWhpacH8+fNzXk/veWnvi+lEHUhByvc66iANyG655RbLEB8AWLFiBa6++uoh1zFedXV1DXq+rAhEkrYswZiC3t6h98qS8QRWrboLXaHPwxP+EFS4cMtP7sBl3/0G/MFMNDSR8gNwwesWiPd1obdXxUmHSdi41Q8BCc+9mUL9YX1wH+Dc+1jSB8A5UKbE9mD/nncBScKc2XPwkVNOMgZFJfpViDQAaJ+aPb396O0ZOtVN7pdQV6m9zNKKQMf+NGJ+CZGqzEuvY5+CSJeKvfs88JrKszs6AftLtLO7FxN81ue7o1cCYI0o7+5SoAe0Qr6U5XbSqRgiES2w39erWh5HX48HkukXzsG2ib5eBb0DwyY2bNiAbf98E1WhEJDqBIJ1kF3VeOrpJ/H5z3wC+yvd2LsvDfNX021RCdVuN1wOQQ+7/jiMx+DxVyKWSOU1xCtfvTGBvfsUTKsw/V16Ms9NZ7cb+t/epcaQSqjQn/Pe/iQikYT9JkdNX1ygtyeNfZ1uSHLuF4IQAp2daeyt9KCmUkI0GsV3ll2Lbe/FASGwtvNV1H7+E4B3EtwugZ7uCKK9afgA9PZYbytZ6UYkot2X0zaRSglI8EBAQn8ijd6efuO8XaoLgRyvufFs586dUISABAVpWYEEQE2nsXPnzhHdNsdCUhZGNlw05oK9c0xXTx96q1QIASRSAQASvG4FqVgEkYgna5uQFAn6++y+zj5EasorUNUVUdAb1d4bI5VueJQD/MAqEl3dmb9nd18CkUj2L5WyIvDBXgX6+2LQk7S8DtwuNxRVQiyRHnevDzo40WgU//G9m7FtRzuCR38bPlNnl74x/vyk4tLV1YVovBbmz5y+/gQiEXtNFJWTWEKF/p0bADr2dyE9fgtY8qYnXfEzMttQMQoav8Lh8NAXwhgGLadNmwZA64l5xx13IBaLYfXq1bj11lsHLRPXs6ni8dzpfPp5+mWHuo798k6+//3v46qrrrKcVg6ZlvluOLrpsoKkNHTQcv2L6/Huex+g8ui4Ebh6vyOGLa+/gaVLlxqXk1Xtb+P3ujB1ShhdiRhat27ERPcRiCjT0BNz4Z2uGpw4+8CCILKaAOC8A33YZA9mf/bzCNeGsXjRYhxWV4FwWLsfl19gQnUSgPbzn+SpQHVNfhmfoWoPfB4J3f0C1TVp+DwSwuHMS29vTEF1WkVFyGPp/eX2JQFYs33dvipU11gfe3tUMdal03tcSgCmTgwAyPx0HZ5QiXBY+zKQgIp9iUzQcvJkT1YbgFzbxMR+BSmX9rePx2W43D4EKqqQkiNQg3WQXB70xDwIVIYRrHKjqib75/O0x41pE4beoRfuOAAtJSOluFBdU2OZdn2wJJ+KQL9q+bv0KSqqa7TnRpFk6M9h7YQQaie6AGg7Wip8CIfzqHMfIa6B7aimxo3wxNzPXVIWqKpJI1TjRrjahUceeQTbt+/EhLrj4PV6kXbvQUoBXF7t9TZ5UhjV+51THGZMs26b9m0irQj4vX1IyICsulFdkzlfsm3vpWLOnDnw+F6HgNvItHT7gpgzZ86w30cLrScmjAnPkisG8/sFALh9IVTXeJBKC6hC2+4rAh5MmVxlvEeaH/PEmgT096RHHluLf81KoLGxcVyXzQ9HZU8aKbf2OVMzYfDX6XjgD8oAtM9mVfIjHM6uYokmBNKuzGfklAk+yzbh9/YilgQU4UY4PGEMVk3F4pFHHsGOXe+jZsJkSBPnWb6BCWlsPz+p+KRUN8zfy10eP8LhYO4rUMlLq92W45VVE8b952g+9DZ44+075Fjh81LeCrYn+bWvfQ2rV6/Gli1bBr1cVVUVqqqq0NPTg3g8bpR2m+3evRtAplfljBkzAAB79uxxvE375Z2UQ4ByJITy7C8Z6YxACAGXEoUeIpN81dpEbxO9LMTnAZRUzCg5lmqOQPDYfwcAPPuPFE6cfWBfaPSSg6BPK3k09xtb+KE6nH58g3HcXCod9FknZttLYAfTFxeorZKMvoiptDaERs8w1If6xFPCEhhymkrtNGlzsGEnAR9QYfsbmftWug+wpyUAS7ZruDYMSZK00vpU5m/qCU2HrAhLT02zDzrVvIKW5vL5RCq/0ujhSKvZw5XMz3X/IOXhY13+qg4M9hiq7FQvu9NfU+3t7RCSFmADAF+gArJLC1573UA8FjVKx/XAfWAgCzo4RFmOx631fE04tDpI2rb3UtHY2IiHn23F9p3tRnn1vCOOHNFeq2MlIQvUDLyeUoOUh5tbIQS8ubcLSWQuuP7Z5/HkB+vw5JNPYs2aNWURuDS/Nkuhp6W5b3WuwUpyWqAnZmpLUmm9nN8jIZYUSLL0s+xonz0euH0BKIGplvPyGWpIpc3+nsLycEqks3taEpl79FP5GbWfLV544QVcdNFFuPfeex3P1wOCsjx49EeSJBxzzDEAgH/+859Z50ciEezZswcTJ040gpX61HB96ridPl382GOPzeOR0GAmVQ09WRjIBLXUZCa9W/JPRLjW+quJ/sHk9wB/fOL3aG19G1VVVZjg3g8ktCD0u/u0hv8HQv9yVOGXEA5Z133oJOvLwWM66vdK1r5cw/gA1ftamtds/gDWg0v23k5OA16c+id25RhqBGh95ypsQ1DMfy+P7R1gOEFLc/eFxYsWY968eejr60Os6z3j9LpZ2musO0dgtTcucg5lMjM/N7Li3AP0YKQVLbhg7lVqDkyb+4tW+CV4TUHL4QSwR4JiDOIZ/DnQe8fpj6muTst+1d9zU7IKuLQH4nYJfP1rX0VzUzPWrVuH5qZmrFy5Eol4Aj6PlNeXBL2PodPz4TRUarwLhUK45gf/iYbGBpxxxhloaGzA8uX/gYqK8Zc1lDL9zXIN4gGsgfyAT8oaKqb75xuZHyMnTKpDbW0tWlpa0NTUNEIrLm7mnpaDdKEZN8yDMXIFLVMK0G0OWtpi0/oPPexpWZoGm+6rffZ4oXgmA5I1V2KsPz+puKQVkfW9ltPDyf6+wG2CAOee61Q+Ri1o2dnZiV/+8pe4/fbbHXtHPv300wCABQsWDHlbZ555JgDg8ccfzzrv8ccfhxACn/nMZ4zTzjjjDEiShD/+8Y9QFOsW3tPTg+eeew4VFRVYvHjxcB4SOXC5JNRNHDqgoQe1Er2Zie2Tps/B4kWZv4GiZqZK+jwS9u7dDaHI8Hq9kAC4Y/8yLru3Z/h7gkIIY6BKhQ/ZQctaW9DSFqipCmbOH86OV99Al4KoKfBlDsKl5EympZlTgNLpfgcL+gV8QNCWMGzOEnSZHrLHlV/vWJ050zIQDGD58uVoaGzAScfOMk7vimtR0MECq22Rof+W9iBldIRbYOnbnfk5t2ZaWqeHuyTJeB5HOoA6FP3tdKhMSz0wrgccGhsbccyHjkdfXx86OzvR1dMHya2lykV7u9DS0oKqqkrU1taiqqoKra2t2LhpIyrybH5eYQpaCmF9TnIFOsY7jzeApUuXoqGxAUuXLkUgGMAQseSiZN7WnSa36j/SmN+3Bsu07O/bnzniCmg/Unpr0NbWPgKrLX6lNj3cvKOQa3BKWtF6A+uyMi0HfvRjxkxpig8SfGxsbMQR849Gwj0l6zxuD+Utlc5OAuA2QeW6TdTX16O+vr7QyyhaaQYty9qoBS3PPPNMzJw5E9u3b8c111xjCVyuXbsWP/7xj+F2uy19I2VZxrZt27Bt2zZLBuY3v/lNVFRU4I477sDf/vY34/Tt27fj2muvBQB873vfM06fOXMmzj77bLzzzju4+uqrjR3oVCqF73znO+jr68N3vvMd1NTUjNbDLyt1E11DFurqQa1Pf+Jk47RjTviIUX4KWD+UvB5g+rSpgEgb24Iaz5T77+0e/p5gKp3ZgQz6JISrTD36QhIqBymjBoBqU0X6cLIDMpmWpusP/GqYVoSRlWMPfjkFepx2GAcLCAZ92ZmW5sdlHvwz3JR7e7VvIKgFcM77wieN0/b1amtzyhrNXEbNCnLZ2R+3fSr7wdKzFpM5My0zh/UgXiZzaESXMiQlz6ClkWk58CEfCoVww40/MjIDL73s342sFzWdgBAC3oHUW6/XCyEEIp0RVPjz2y70TGSB7C+cYz1hfSwoqnAMUI7HzDp7JrNdpjzclGnplYzhPXaTJ2bS7IQ7iGQyCVfFVEyedtiIrLfYKaZIpTrEe9t4IJveF3P9SJO2ZVpOqrIHLQfaD5TJzme5GezHu4qKSixf/j3MPf7jWecx87a89ScA+1sks+rKmxCibDMtFy5ciIULFxZ6GUUrrQrL9ysqL6MWtPT7/WhubkZVVRVuu+02HHHEEfjiF7+IE044AZ/73OegKApWr16NE0880bhOW1sbjjrqKBx11FFoa2szTj/kkEPw85//HLFYDIsWLcLpp5+Oz372szjhhBOwZ88e3HLLLTj++OMt9/+LX/wCM2bMwB133IGjjz4a5557LubNm4cHH3wQCxYswE033TRaD73sBHwSJlUNvSkFggGc8cmPGMejSet1zDszfg/wb+d+CfOPnGtkhsW7MpmWuw8gaGnuERm0ZVoeWpu98+21dXw1B2/+P3tvHifHVV+Ln1vV1XvPPlpGki3Llne8ywZsJMwm22wBwwsaEQIP4pA4K4JAePkFkTxC8vLECwmE4ABh04jdgG2wbCCW2GxL3uRVHmuXZjRbz9J713J/f9y+VfdWVy8z07P11Pl89NF09VZddevWveee7znT8WEqGhRTWSr5gvFyTJHccSsVvAhKT0/LEmnpJZIMa0C0TGnpvDAedko8A9PMNqpkUdgWI/ZncdKyGixaW/Lvntg0nLS0+PeUHpvy+eJKy2AA0ErHjx9Hr3Myl+D365qkZaltiYM9LRSxlYFv/p132tvDoQDzJC0ydlbXdRBC0NHZgUiwPtJSfJ1YTg9UJ62XKiqRL0txPFWUlJbVysOdbeFgZaXla1/t9POprI6xsTFcfMWNuO0t7/R+Q5NBLA9fSpxlpYmhLvyeSkpL3ZQ9LTtdlQxcaWlata0tfCw9eHlwcxQMNv6jkXUAmBs1X/Tzy/2WN7zGcl5qfx/LBwUdoC4pjL/Y5QNgooClOMb20RjMaRTXK17xCjz55JN43/veh1wuh3vuuQeDg4N45zvfid/+9rd4//vfX/dnvf/978dPf/pTvOpVr8LDDz+MX/3qV7jmmmvw/e9/Hx/72MfKXr9u3To8+uij+MAHPoDJyUncc889CIfD+PjHP45f/OIXyyIMYD7R01EfsZGIOGSXqMoA5AlTMECQSMTx9590PONuvdkhuJ/pH0I+N70aYVG5GAkRdAlKS7efJQAEXKxcRCAti9NU1w1OyAwTJz1FsqBMaVko75m9FGu8PLwjThByEa1hD6Vl0PWa7pYZkpYVWEuFEHSXju3oFK3qdcVRS63oVu7N1NO0Eng5Z96DnAEcEk4krvmxnu/BlO1pWa/SUtg/8T3iJLNnVTc2bdqEqckJjI2NIZVKYePGjdiyeUsZ6V0JqwSbiBMjzV8eXmliVYtMXmywLCq1Ea/2fLj/OPbu3YtUxmk0sRCBWqEP6Gh1ZOlXXH0DPvo3/4APffhjQKA8dbrZ4CbkllJ7EBXlIsS2XtC9+3RWHs7+joeBoCY/HxIWyyoRnz6WLqr18XxsM5ZmbaAjQRALceVt890bfNSPKY8+xw/rWt7w6kuWC2m5Z88e7NmzZ6F3Y9HCokuzmslHYzDr9PCvfvWr+OpXv1rx+Q0bNuArX/lKXZ+1fv36qmWiW7duxdatW+vet56eHvznf/5n3a/3MXN0JhSEAlZNBaJCCFqjBOOZ8gAW8aYUDDDPxNZEFFu3bkU+l8f/3bULtGsTiBrCQNLArl27sGPHDqnEvBpEtVckCFy0Ukc8kIVuAsXBQ8jnXiF9lpvEiwvkzXQHVcOT8m8teCgtLcomhLyMrh6lZUF3fDrbYwQKkdWNPPVcIc7qlOYKTupuUXBqzII2zXRn8eUqgVQq291KMDjBSt8nsrTMP9Trd4nJ6W64lZaV0shnCrtE36MMllJqp4fHhDagCeXhPD16PsBLI2qVSPA2JhIOoh+MmHoeDan4j9278X/+80EMjqSk9PB6lZZXnKPiJ4+zLzs8YOLq85wLaK7Kw+fzuLtRabK91AZUhiWT2V7Kp8HhJPbs24OeK4pAy+sAAC1VrteYQO5fdc3L8cZXb8bJUavhXrSLEW6ScimpAnJFirZY+XmVqgQM1l6CrnW+gpAe3hYlZfYqUpCdThEPL8x162NuUE1pmdfZ/Sqvs3PeEoF9Ty0aC9uP+1hYpHLl590nspc3vPoSrwqQZkQymVzoXVjUsKyltRDso7GYNWnpwwdHJFgfmdcWA8YzLExFN6ntqyiqF4MaIy05cbhv/z681N+PaGIUiK6BEu5C/+PHsG//vqpENqUUusmUmyJxohET//5vn8Fwfz8opfghIXj2id9IJKibtNRUNhHTTZlEy+fy2Ld/H5JjSYnsEaGb5WQj4OVj6ZTReZUduxWJIvHbHiOgVCYtwxoBIQTRkBNe41ZatsVYAry7HL4WRNKyPa5gNOXcSUSSMpmiZUmyblRTWloWLWtX6QqKoJmCq6OKHkrLVDpvk32FzDjyuXaEI+ESuUyZh6POymXnA3Z5eJ0l9bpJ7UmhKbTDgtD2tABBPB7D77z1zZICmgB1B/Fcdo5qk+OHB+RRRa5I52RiOp/H3Y1KZY1LiaQCyst1+eRAJQZMyjqFYDiOUCKB4bEpBFvY61qilc+lSHTndIrRUp/U6MWGxQj3gHoplYdXUsvJSkvWXoKuxa/xtBOk1xojCLhITdH/1FdaNh+qKi11Ki1YxMLE7mf0EgnuJrl9LA94Vc34lgHLG55KS79N+AAbXy21MbaPxmFOy8N9LC+4FXyVICo5xLRRd3k4Ic5ANjmWBKUUil5KpSUqEO5CcqzyqpRlUXz55zr+8qt57HvOkHz2Bk4dQX9/PxKJRFlasv17XINoVXEIRTuYIpfHrl27sKdvD+6//37s6duDXbt21Sxd91JaAjJR6a20lB+LITxtMaZiFcE950QSwU1aAqxEfLrl4WJ6eGdC/t6WkDPC2Pfbp2oej2or64ZVXo6fyjb2rmW6lJY2qZzL41/+7S77dWfPHLPPr3gcc/Poa2mnh1f5St2UQ2J0k10P4jaxLfHforjuCGGNVLQBcCMRJji3m33A0ISspKaorsSZKRayjKyyp+XSGlGZFjs/nLjkE8YACqCUNTaqhKBpGqA6izFtdZKWqZyjcs/rtGwBp9mwlJWW2QrXqEgiUMihchyjKeeHtkTKrQPcSksfzYVqQTz5okxOJcLEHi8a1vJRUfkoRzrvoexeJqXAPrzhdR/y24QPgI2nfKXl8oVPWvpoGNx+ipUgEmsisVFwl4cTJ9G6o7MDhBDQ3JD9GjW6Gh2dHRW/Z99zJh4/ZoJSYN+zhkSaFLKTLC1ZYzMpMS0ZAAIKylRhquKEr3CSbd/+fTXJTy84npbydnGFUSQw+a64JwYTLtKyxWUZFy6RB6IvoeaREt7dUq6MqQXx8LRGif25+Vwev/zFvfZzvz34Qk0it7rSspzUHM/QsrCX2cAJ4mGKQE7o7du/D8dODduv00jRPr+iR1uuwcrPaqhHaVmevFjugelFWqquNh+p088SYNfHxWucRnR4QN7BuQgsWkjyoxL5ttQGVFxFzPebk7HBAACz1LCVIAtnCjikpXuBRISofp1yLTB4EV7NBMPFUi4l0rJQ9PardCufUh7qqLGUfC9S3eXhQV9p2azgC2KVFh+Z0tJ5LhYm8qLfHNmH+Fj8yHiMnfzy8OUNb09Lv034KCktl9KgykdD4ZOWPhoGLwWfF0SlpUi6iTelUIApvHjJ8pbNW7Bx40bkJ5wE8e51l2LL5i2e3zE6ZeFHB3ThMbUToAGgrSXE0pJ19hoxLRlwyFIRqkLKlJZcAVqJ/KwEqzTAd3uAiiXsIvHEvRTLlJZpoTw87qG0LO1vtIbSsj1GpKChesCVlryEmKs69+3fh8ETz9mvCyVW1iRyq3mhGla5qi2vU5ydaBw7xAlACvZdnAxLjiWl8BCFFuzzK0+65u8mWk8Qj3uAVzSo5GcJyIQfJ+PdSkt3iFM1qApwYY/zAff/6iXs3bvXJqsrhXzMBoa5cEnEFZWWS4y05N6onJjiZGxbSwwqYRtNGkAqlUJr+wr7fe1VfGqlJHnXtdHsJeLuxYSl1B68+lqgXAnndQ7Hppwf2urhaRkSlJZzsYDhY+HAu+BKfWK+CKk8PB6WK1maMajNR33IeKnq/FLgZQ2vENLlEsTjozpMiy45YYCPxsEnLX00DKJnVTVIpGXW2Z7KOIxc/+FnkU6n7YFtOBLGjh07sPXVToL4xpfd5BnCQylF36/0stTkgaRzI7zuqsuwceNGpFKpsrRkwNtfSSoPN1gZKFeAViI/q4GRY/K2ySzFQNLCeMayJ3bBgEMCuJVlo1POj3zh0MOIBuQ7O1e3bCyRSd0tBF0t5eeJCInf9YJX//ESYv5dybEkrLxD2pJQe00it5rSUjdo2SA2VwQGx2nV4K7pQCQA87pDDnd0dkDRYvZzVjFtn18xHbfRaebVYJeHV1ltdB9P3ShXAIrHlLd3rrbN5/LYu3cv7vrCZ3HXXXchnU7X3C9VIehpKQKUtcHBdFyyS5iLialFUUbGzhf49TmepkjlnN+21AZUvO3z48j/D2kKOtuZGW0wHMe23m244KLL7fe1ewS2cIgElbusaz6vlYVAeXn40vm9puVtueDuf92kpWVRjAuK2pYokexDAHaf4Cj4yrqmgmWrtMvbOqVscVZsM/Gw7KG9HAK6fHjDazHTJ6iWN7yU18tFaTndwOHlBotWt8by0dzwg3h8NAx1Ky09ysPzuTx+vP8oENoAAHh43z3Y/sTf44tf/iYARkyGI2G87dYbse+/8qAARlIEBZ3iVy+YOKeLYONqxrw89KyJF86UMwfHh52ZV2sihB07dlQM0PHyd1QVlEqCS6XdBlOAHjxwEP2lQB9CiER+VkOuSMtuxNkixQulslpechwMOH5guaKTtJnP5fH4M84xu//H38a6C48B7bfbn8fVj2+4IoDzVyp42bpAmdcYR73ehRy8fJ6XnvPAlo7ODkCfAqgJEBVWoKUmkVttkOpVBl7Q2URoNEXR7UHCTheiWi9fpDY5vGXzFux7Jo8J/lwmaZ/fnxxyvjczn0rLEgli0cqpq25yu2gAwYC8TSxj1QSlJfdp7e/vhzH2JFAcx3333Yfdu3cjHq+cqKQqwG9+vR/m5AqobRdDCXci3nmurbJd+45bZ/qTK8K0qitO5xKFInBs2MKuHxcQDAAffksIPR3KkioHBhxlICPBiU1QaSpX2lJQJYitW7fiX39SAMAOeLXycE1lpJVpAUXXCDPTQFuHxQh3e1xK7cG0rTjkc+smnt0kk2HBTg4HgPYoKatWiAiWAb7SsrnAiXqvxceiwa6BjIu0FIOcfKXl8kWm4OVp6beH5Qx3dQawfIjsCy64YKF3YVGDlYcv9F74WCj4pKWPhmEmSks+0fnpLx5GPriJTZWK44iZp3DgwAF8/3vfwSU3vcd+vRYg6EwQjKYohiYp/uu/izh0wgIB8Ls3auhuIfj+w87I2Rh9DIGuawEAY4JYLBpkJGilFS2v8nBFkRVERR1oiYarkp/VkMpRVBuacUIzGCB2UjVXlmkBVoadMzZACQGgBuJh4PSx5xARSEuublEURuq2VFFHTRdcSRMtlZXz7+JE7qnCBJRwJ2igtSaRq5sUpkU9CdW0B8nBlZCD4xTdLbP7HaYln4fJrPM4HAnjpi1vwL1PsMc3veIabLvlMpYeHnDaWXYelSLiDbtS6qpbLVU0KAxLPrZiGWuw9BmqIvu0htU2FHMKDhw4gL6+Ptxxxx0V9yugMpWtOZGE2nYxAEBpvRh09DiSY0nPgehsYVq0RKg0NpW8Frh66PnTJizK2uN3fqPjz98YXFIkFSDYDfCwptJjLQCEKet3eMK4SIbHI9U/NxRghvruyUbTl4e708OX0ADbtKgn8eT2b3WrZScm03jx2AiALgBAVCuXT0UkT8vmbgPLDbzP8/IY5qqplORp6ZeH+2DwUtX55eHLG14Lm8uFtPRRHZafHr6s4ZeH+2gYZuNp+cJIGwhhzTEwcRBBTQGlFEODp8vev7KNl0oDh06U0m0BfOvXOv7jgaLdoemn9yI48YjnPoRrePVVLA8X1AHch5GTn9t6t2Hr1q11EZYAMJWr3vOKgRhhSaXC/j82DCixtQAAUkwiqAVgFSakzxDfBzgl3Y0AF/hx0pKHtvBS/q5W1iCIFsef/PmHah6XSiXiXiE3PJBoLGWhoFOk02ncdddd2LlzZ93lzBxukmHSFRxStJyG/cobrrJ/h9je51M9Ju5vpTAed8CTbpaXUYuPeameQkSfVhWwCgiFQqCUYmBgoOp+5XMZDA4OIjd0yNm/yLm2yjZfbFw5v/351sJMcLh6SCwLf3HQwlMnrKpl+4sRXHFruI5lUCWuxGfHczcUkPtCL/Drw63SM6zqScNLHW6P1aVSykQptb2W3XBfY2J/l06n8T8/8Ic4M5QqfY6Fuz73T2V9sHgv2vOdH027n/axeOGUh5c/x4lMMYAr4Qfx+CghJ4xVuChAN9HwsYKPpQMvy4BKwYfNhs9//vP4/Oc/v9C7sWhhUV9puZzhk5Y+GoZggNSldwoGiF0qNpFhCrth8zy2gZpQJx6DXiyAEIK1a1aXEW2r2io3Wz656lAHUTz2Q2jmpOfrIkHPzTa8y8OJpLSc7UA7lav+vERaarJKxaIUJ62rnH0b+zXz0zSzIHB69EiQSGnhjSQtHaVl6buEfQxHwjh/rVMOnjNDSOcpvvZQET95XPcckFZKgq6mtKQABkfT2L59O3bu3IkvfvGL2LlzJ7Zv3173hLiWX5v4OCqEFQWF35ut0haMkoq0URDHbpVKo91KpmLJ01I3KB5+0cDJEUt6Ly/VUxXi+LQWMiAACgV2Lfb09FTcp3Q6jTv/+IN48qknkZ84AmqxxmsEV9gqW4tW9y6dCSxrYTwteftzX8N3P6KjuMRUZKLSUncR2aI6rmBQ+3eHg/C0JRDBlfdeJFgzqy3LPS0XZj+mi+olvu4Sf+fvvr4+PPHk01DDbQAAYmTw4vNP4/vf/770HsVy3rTvV49Mu59uFiy1RY16wC1LvPxQuYqy3NNSuH82uWWED2/opqzsbomwNkHpwtm++Fh4ePUHmWqDbB/LBpbVnPdQH/XBJy19NBTBGuobDu5rOZGleOq4hYLFWERj7Ckkh45jamIcmzZtQm9vb1mpNldacqyNDuEc1VF3tWhpXNf5AggB9Pw4YMq1uyENFX0dOTTP9HAgHnRm9T//5ePI5/LQTYoHntLxSP/06heqrRyaFrUnu8GArHjK68BjR0xMFFldtJU5g8kjD9hhQq1R57IOazJBW4NnmBb4IYyViLyQJpOiYrrweJriv58x8Ei/iXsfM/DS2fIRqdeEB5DVbByiUuu73/8xDhw4gM7OTqxZswadnZ12OXM9cKsV3d82NOFs6RR+U0hQilSbdB0bthpK1lmWo0LwWnG0LFqWMl80KHSTnYOv79Px/+4rSMeVK4sVhZX3b9y4EVPjIzhz5gzGxsbsa7ES+vr68MTjj6G1pRXr1vSA5oYAAGp0Ff78LxyVbbUyQErLPV5rwVygyQ0nhd1q6ZEpirsfbTAzO8cQPS1FLzEtAEQEkr6gO787UkeqPF/g8VJfNfP8Y6mWh5u2Ws5Daek6h9mCo27v6+tDNq8DGrsfESMFauk4e/as9J7HDvzG/ru1Y9W0++lmQTOqCvl9yMuLkN+L+OKjQtiih1jNkm/CY+KjNgwTKBjOvUSswnL3OT6WDybT5R3CPT95YNktcPkoh0npklkI9tF4+J6WPhqKkFaZfBKRiFAMTrBBy1cfHAUUNuF55UYgdO4tWLMihg9/YCvi8Tg01ZAmvqsF0lIx03jh5/+IzOQIAm2XItR5ITJjD+PQOSuxYcMGHD16FKHsENTEufZ7aqksAW+lZS6bxiO//AnQehsA4NcHnsfAs3vxijd/CD98lL2mNUpw8RqPN08T4u9l5eHOb07lKH74qPOCG9cnEVix1fbT/MLPCSayFsIaIy1DAYJ8Lod9+/ehOHEMl6wLobe3t2qoSj1QFEbucqKaEJYgzgk8ibTMUBwRiMoXzlh2cBJHJWJvykORKpJyg0OjoJQiFGKSz3rLmTmqrdpRSnEmyfa7I05kpaWoFKlAxqXzFKfHLHS3KLYidba47zEdP3lCx1uu03D1ehVuP8dkmpYR4lxpeXKspIjRYf8uwCkPDyhOef/jv/kpjPFN6OnpqdleBgYGQC0DmsaYqoAxCgtrAKJishgCf2euCLRX+Ay9pPSr12YCgO21ON/gbZX7tCmEkd2UAj972sDH3jbvuzRjcNJXN139jiory/O6c92F6/Av5j68RaM8MKqZk0CXahCPrbT0uH8XXQs7E+kitm//PRw4cACZTAbFyAUAYf051ScBamLVqlXSe6bGR+y/qRqedj/dLMgUKOLh+fXgnWtUU+nmXeXhsTCgEFd5+BJTp/toDAxTbjNiuFvRQMPGTD6WFp55/giA86VtwyPjNX3VfTQ/mNJyoffCx0LBJy19NBRBIV27EvK5PE4ffQaIXA4AMEqEpZUdwumhh/DhHTuwdkUU8TibBLn9Jc9bqeDK9Qr6T2WRPPQfUKw8qEWhjz8LPfksEokEnn8+iYsvvghXXHEFxlsJxoROrh6VkBdp+d3vfBuDx48jfCUjLUOJ1eh/4edQnz0LgE3QfvW82XDSUhPSwwHgl88ZGC95gV62TsHv3XI9gOvt599ynYmfPmHg+o0qggEFejFrp0Gb6ROgqaN1pUHXgkKcxHCOiOb40bQLq+bJNMXJUeck9A96KC0rlYd7lJKK5c+d3atBCEGhwPwX6ylnFlHNE3Es5agW13TI7UaadHl48ADAiwMmKLiqtjET1QcP6cgVgV88Y+BPbi0f1Q9NOsfGsigUhUA3KAyTICcoQnkbAhhBBTAiGmDEZe+7bseGlf+jrn3q6ekBoSZ0XYemaaDZAaD1SgDAQNLCmg72wdWOtWEyMisWqv84WRaFbsw/AcDbarqktOxMEFgWMJamnsrgxYxK5eFBTbZDSOUouKtDpI7JpNhnucnoZjbVN0skej6Xx779+zCRHMWz5xYbslA0l3DCVMqfcyvonn7uqK1uX7lyJU4X19vPGdkkLrv0Itx+++3Se1Z0tQIZ9jdVItPup5sFzVgKzduO12IEV1HyfjFe6lPE8vBK934fzQ3dpLbSUlPl8SRrS81F7vuoD5MZj5uQomFg4Pi874uPxQU/PXx5wy8P99FQ1KOS2rd/HyaGj0vb9KmTKBy+Cy/192Pf/n02eQKUl2orhOAPXx/CteH7YU6+BFCm5FFVFRa1kM6kkcvl8Nxzz+PQoUMoTp2R3l8PaekVxHN28Ays3Kj9mIS6QCnFRM750U+dMBtCWoiDeCc9nOHZU06PfdvVGtxY06pjo/IQjjz6Xdz/03vx4x/9wE6DXrmiu2FleaoiKw8B+dh2CErLw2csSR15fNgqm+BUUlqmvcrDhde+9vW3YtOmTRgbG6u7nFlEtVW7M0nnuznxxhEUDr2X0nJowsJEKdSnkaVOvLwwW6Dl/nkWxViKbXzimIkdX8/j6w8VYVLWpsTy7PG0XAoMOD6lQP1WDwDQ29uL6669GqlUCmNjY8glj9rPicewmiWCbk7/OJkWoC9EeXiRKXS5tx/zaGN/LzVCjgfxmJZMOgRVWekiBlRF6yoPr1zq18xKS5MywvL/7tqF7+5P4meH27Dzf//fRe/fyL1hdZPCcslD3b6xU1nDVrerqoqV51xuP3fphWvxf/7xH8oI2ltef7P9dyZnTLufbhZ4hUwsdfD7kEnL+/i8Tkv2JOxxvORbKPYhzVgy76M2dMNRdoeDrnuGnyC+bKGFEmXbiBpadgtcPsph0qUTbuij8fCVlj4ainpIy+RYEsbQI1DbL4FRzCF/8gFkBw+itbUVlFIkx5KSN6JW4TN5YAgIK002S+ZspmGCEIJ4Io5oJIrRMy8gdNEr7PfVVR7u4Xm5pmc1YLwIWEVACcLS2pnCDy32a0wLePQlE6992ewurbLycIEg4+WHARRw+In9WL1li+0ZmM/lbVUlpRTQJxENFGCRbla6a9CGluW5y9xE0lJUWh4dkpklw2Jejxf1OOxwoQKR4ZXMbZisLDigEihaBLt370ZfXx8GBgbQ09ODN71tW92qpuqkpfPk2k6ZtBTTk70mXWcnnPc2ynfRMCx7MJ/Xy8mE0RS1v2vfswYKOvBwv4nbX04RDRLJS3AiIxPjAFsQ4AiX8+EVEY/H8c1vfA2f+s99SI4lEW5djQdLzWtAOIbVSEnDpNOeqCxUeXhep1IITyLinOOlRlpyT0t2/J3zrwWIpHoVScvpKi2LBhCD/LhZYZpsYe7YWBihS38HABC6tBcHDty1qMvbLCEcrWjIad/u6zIQioOoYVvdbgUc04drXnYBEvEgADkEr70lCi61vOiSK/D2t+5c9OrTuUBTKi2F+1vRcBZ9iwbzH5NDeOT/Ad/TcrlCN4GCzu4xYU22I2nmhS0f1dHRvQZHXet77Z0r0Nv75oXZoXnEtm3bFnoXFjX88vDlDZ+09NFQhOrwOuvo7ACKEyg8/X8xNTllk44gAAFLLxbFlV6l2gALDDl44CAOv3gYRCEwDdOuJgmHw0gkElCIAis7JL2vPpVQ+bbt23vxnf8+jVO5USixHtBgO86/8HIMWjIL+psXDLzmcrVmum41iOROKOBNIOWHD+Fbv9qDxw4exI4dOxCOhLFv/z5bValpGvSMjrMnTiDQ3gZd1xGC1dCyvHhEfiwSwtEQI1yLhrdhwN0PHMLVq4exZTMjXSul1lZSYOZ1IK6yz4/H4xIZ8ORxA6s6WWk0RzJtoSNeLi6vRnqJpGXV8nCX0tKyqEQKegUUzAQZYXJnWuUp4WJpuFj+nSlQxItUmjCLpBufZIpKy3Ad14mIlpYEbr1lK6yS8vmXX8sjrwMD4yIZUvk4GOb0j5NJAbIAioyC4fhZAiz1lHm2UZtIdQeILVbY5eFWeXp4tAJpOV2lpbvUr5lDFkyLLcyp3Y5lhxpdsej9G8UFEDdp6b5uu1b04JpNr8DjjzwESikiq6LgL2+NEs8gO/GeetGlV+KO//mKstc0OygtD0lrBoi+0EXdsfjgi3lpIQuRP5eIOG3EfR/zsTzAysPZ3yFN9kpu5oUtH9VRtBQAMjN1xdWblsUCV0dHx0LvwqKGaQHUv10sW/jl4T4ainqUljyd2LIsEIXAMkv/WxY2btyILZu3VC0P5+CBIdu3b8fvvPV3sHnzZlx91VWIRCLo7OyEQhToug6aH5HfV0NpGXKVY3PE43F87KMfwepOJjMiioZb3nln2esGJyiODc9uKagopfgSTwIprJ9AIpFAf6mkHmCTZUqpHYgSDBBoAYK2tjakUimcPTvY0LK8MqWlQHIQQqQwHjeOjSrY07cHu3btQj6XR9Ggdio2R74oKzADivgcL70uf18qx5KcOdJ5iqdPmJ6kmZcKMp/LY+/evXj++BQARup1t7DfwiddYlt3WwKMZ6hUwtCoUqeRpLz8PDSWsf82TIpkqTScUpk0zRSYkkpUhIp7zNXM4nXnRdzXgqo4oUyr29mHJdNOWXq14+AOgqkHllWuNp1rWBYrd0wJJF4iQiRF+FIiJnj7556iHEGVSBYPpwRP2np8R8UFDHeQSzWbgKUOwwIS7d1QO65wttHAovdvFCvC3ap39zVmWAo++7kvYufOnfjDP/xDXHLVjfZzrTHiudAo3sOWq4dhQV8YZfhcQ2w7Yh/OyUhZacnaQUtEbA9zu38+FidyBQrTYu0gEpTDmfzy8OULL+W1hdlnBSwFJJNJJJPJhd6NRQvLolXDU300N3ylpY+GIlSHDx4nG/ft34fhoWGMjIygu7sbK1ausFV3qkRaVv+srVu32o/d5dGEEGzcuBETIdj+c24fRjfEwbQbsWgEl5y/AkPPshFV/5AKgP29ug0YnGCv+87PT+Iv3rbaLtueLqqVh3OomSMgmmaX1ANOyTwPRNELWajEwhvfeBu0YBDmxIu4cF28YWV5bkI54trP9hjB0IRzg4kFckilUlAiK6AmNiDe0m6Trlu3bkXRgCuxmErHIhEhtoKQE0MUkN6nmyw9+8y4hZVtrCEdG7ZgUuD0mIUNK+UGZboGx3YbOnISkVfcBEIA5M+iWGDnc323gmdPmxKxncqxGykn7ZJp+abaCGVZOp3Gn/z5R4G1/2Rv+9u/+z/40Zc/hng8jrGUQ5RmCvKgP1tgpb+Vyip4eTg/nQGl8mJBNQQU53vXdBAcG2Z/D4xbOH+l2vDy8KLhHPP5Qt6VHA6UPC2FZlXQl05CMA+OMUw51EgLAJef4/yok6OC0rKu8vDKaeFWyftuJm2sFsTrcCFgmhRt59wIctrZVihai96/USQm3SSS+7osGoCiRW11+3v+LYMjKfaieKhUHeG61t1J9MsReZ3dr3hAWrNAvK+I6fOcfMjUIC3zfinwssRQMmv/nZoYBmiX/dgvD1++4Ivcmurce5aL8nbPnj0AgDvvLBfE+GBjRz+IZ/nCJy19NBT1KC2BcrLRDbGyulJ5eKXP5YRociyJjs4ObNm8BZ97kNi+im5izY2WaOXJhKoAXS0KOFH53Gmn98yfuA80/joQNYTjoyp27dpll21PF5LiyUv5mTsLYkxB13UQwkrqAeDVW1jJPCdtrdQorr76Crzuda9HOBLGxT23oadj7gTWikIQChBbqdPhUlpG6BgmJo9DiawAlADUlg2gkwdt0rWgy5Pbgi4PVlqiwHhJXCiWlInv4xPuiQy1FR4jU+w8nUlSnNMll+4aVsnPjbL95yX28ZWXwSLsWOXGXsK+/S9i69ataIsRtEWJdI7yOjA8SbG6nX3uWMpFWjZAXdPX14dnXjiC+FpnW/+xAdsnb1T4TlFlCbDy8GoBEJxw40rLYB02D14QlZqsnbHrZCBJcf7KOoJ4pnGcDh4x8KGv5XBul4KbLo7Nyo5hOuAqsSlBXdsSkdNwF4KUmQkRkkql8JP7nb7ydTdvtp/TVKArQbCqjeDshHxepqu09CKrRe+7RiJXlP3y5hOUsoWDJ0/K5W1XX/dyfO0j717U5W2yWs696FIemiaqYUTbCaaYIjBd51wksZerso73HSZtrjInSaUr3Jc5+ZASysO5pUyr4Hld8D0tlx3S6TT+/tP/Aqz8CwDA6eP9uH/wt0Ab8y1cLiSVj3Lw8VM0BEyWeO3iMlXn+3BgFVOgQwdhkiFApcDatwOBxTum8tF4+KSlj4aiXtKyFkSlTKUgnkrwIkRXtBZxtGRtGamltIxW2y82kecQFXUDLx1A5NKrgUgPlFA7+vtfshWE04VYTumltNSTzyAzNmYrSbds3gKVAG2JiETaXtSj4Pd734qnB9gsXpmHmVIsDBRKVcxiGA8ArEwUcGayH1h1EwDACJ8LQh6zSdeCy/uOJ49yMHVGSWkpTHTE94n+kgNJS1J+6CbFmSTFud3Od2QLFj79gwImsxR/+aaQXWKvxNbatIOZOY3kGDuGmsoIufGMBb43RYNicNzC6nYFuSItSxN3l8fOBAMDA4Dqapxq1PbJEz0sx12k5WNPPo8VsQsqfjYnj3j5/XRCeEQEJNLSOcY8jMewKpNrxjTLw390QEe2ADx/xsLzZ0xcunZ+bme20lIK4iEIupSW842CUV/IGEc6ncb2d/8+njwdsVXpBw48Bqz8SwBssURVCC5YpeDshNyAExGvT5QhlgN7ndeiQesiP6eLfHHhVK6GBUxlKV44I0sB1m+4EPF4lRvLIoBYtuw+X26lZUGn0qKRuCASDrL+I5OX3yP2KZVC15odvO+wLKCZqh3dQTwAMDRhYXCcPSEpLUMECmELPRzLtT0sZ/T19eHIibOIrGSPNcXA4JmTCLWxx83se+yjOrj1U1gjyKp0RtZBPpoMRhrmU38NjJ8LSy0C6tPA8M+Baz7vE5fLCD5p6aOhUBRmwj9bZZmUHt6AEsJVrc5nxMMEDvUlg6B6ebiqAIlguSyAUgtmbhiKMQULPYASAAJxW0E4XRQFJUowUB6K8qqrVkK78BZbSRqOhBHSmCeQSNq+7BwVbTECDBj275trtEaJTea6PS1fvel8jPb/HDwayQifY5OuQLkCJ6/Lx0IkQcVJszigET9jcNyCuymeGrNwThexlXmPvZTDmdJp+ub9R3FJIs7S6IMr7PfQzBl0dG6CphIoCkF3CxAMKAgGGFlUNICJLAu6cZeGA40ZgPf09EDRXpS2ES2Bnp4epHIyuTsyLrfRx558HoNHDgAd3smEdnq4wq6N8AyVlqowEe9pdxjMJ14YQlv6OWzZvAW6GUfIgzxnQTz1f5fok+ZWls4lbJ82QWm50J6WlLLAqumQln19fTj42BNov2ArApoGQ9fx0tFTiPJJZID1dxtXK/jVCzJr9cSB3yD98uurKgejVYJcgLmblC5k6bFlAY8fM+G2XCosgQlXJbUc4EFaGs6ikWE6nrWhABBQ2H0oI78FwQDs++5yVVryvqPZLLnEe2zBoBiatPDcadMeY7k9LSNBgkjQuQn4hMTyw8DAAKjm3D9UWgC1nNWPRoUX+lh64PfwsEagBXzS0geA0z+AlRsBcE7pfmMBuUHg9A+A9e9Z4J3zMV9opgoVH4sEYnnvTAnHap6WPCRlT98e7N27F/mcS9LhgZdfGMDaToLzVyq4bJ2CroR304+FSVU/NFUheO7JX5Vtt3Ij0At50IJDUirhTltBOF2Ik/ywRiSVSkAB3vWmTdjWuw1bt261y89DWnlZb0CV1W/zobRsFcrrxfJwAuD8njA++pd/iJDCZryxzvVSCb07hbvg8rQUSVCRnPAqSQNQRlgC7NjyNNN0Oo2v991tP/fSQBGPP/4ENmzYAENzSMvzVoWxZfMWIWWbYGUrQbB0XjixOjhueZKWhlkeFjRd9Pb24vwLXyZtW7v+QvT29pZ956EXTkiPQ9E2nB6sTKCLhJtCZhbCA8gK6QAtQDGZ5HayELFDl8Yn057v1S06rcUOUWk7la38ukaDEy5TbtJS6KfmOw2XJ5ZPBwMDA6BEBXpuQ+HCj0PpvBZQHbNKrdR3XLCqXBJ274++je3btyOd9j6XgLzQUqk8fC6wkEnEhgU8caxcVr0UJlyit6+bZHUH8XClJaUUhgnk+CQzyBZAvKwaCHGI/eWqrON9RyVv4aUKUWmZzlE8d8qUFoVF0jIWZr7iYZcNjI/lhZ6eHiiBhP3Y1DOA5TSEpdBn+mg8TMvxNg8HnflfsQkDzHxMA/mzMClrDBblE1kC5M8u3D75mHf4pKWPhoOXiEeDBDddrOLlGwM4b8X0mpqstHT+5iEpe/r24P7775fSp6vuEyng2tg+9KR/gP/++QOIqFlPNVlrFZUlwEi/qfFRWMVJaTvND6OtrQ2F1JC9rWf9pbaCcLpwh88EVEaSAcDl5yi2Mk5ESCMIubTTmsomkJw8ng+lZUuE2N8jKiNXtbEU9HAkjJ4uRlLmzRAUzSFK3Iq5vC5PcDuF0vyCQE6KpEg9SqvJUvJzX18fBkdS9nY1vg5Hj53A1VdfjVDbegBARM3jIx/6E4QjYcn+YFWbgmDpuPJ9PDtBMZ4un5FSzD4NMx6P4/13yObct775dsTjcSRd3znpIvGIFgfUyjW94jWmqmTGSkuRIN+3fx/0qZOl748h3r4G/f39+NZ3fuD5Xt1gCqR6yTeRqJ7KebMA4mS5UeCkGA/iUQjzXhI9Lefbo82iTgp4vejp6QFRNJidNwGBGPT2l4OojhlkUCVQFLZQEA3kpPcmwgQHDhxAX19fxc+PSEE85c/PVdBCbgH98UzTsQwRF96WQlq2eNmJ58u0aJkysGg4ikndFMr5gqTqgge/Py1XQsJWWjYZaSmmuRpWeRVLWvS0DBPEQq40+WVKYi9n9Pb2YvU5jmVNPjWMc9auth/76eHLE+L9O6w5VUC+XcAyR3gVLMragmmTlhQIr1q4ffIx7/DLw300HOwmQ3HeCgWEEERDwLldCk6MWHWXRSmSOpBAJWxSxUNSEokES8fWdSl92gteieLP/Po7+Kddn0delxMbqoXwAIyU6ejsAB0cA4Kt9naaG8Eb33gbhvQ1eGyMbbvpNW9uSHp4NMiIkTtvCeL5MxauPs/bDCusoUwlKnoV6ub8KC0DKkE8TJDKU3S1ELTHWOL3Feud/e5qcZKlx1JOgE06T+1UYctiJc/isRAVsjlRaVl6TTqdxne//yAGR1JS6bwbqZJKbmBgAEQ713lCCYBEezAyZcGgrHs8f00U4QgjVkViKhyEo7Q0+H5UD5qZreerSUIAhBIqS4NpUZuE5bACLVJ6r0VCULTKnnoiCa6SmXtaiu0rOZaEmclDbb+UfW5iHWjyNAbOjni+1xBSIusJ3xLJ6amc92vOjFm4aE1jzeMcT0t2zBMRQCGyp6VbMTzXMMxyNVwt9Pb24u77H8YxhTVKSwnjnPUXgJ8dLeCQ0AkMIwvnOtGUIiiltp+qFyJCwrhnefgcTUrz83zsRZgWtQnKeFgIEVgCpIyotNQNat8rvSaLBZ1ZEuR0CtOk9jURCQIhjwU1jqBGgDxdEsdjLmArLans3bzUUYuE5YtHmkoR0giiIZncXq4k9nJGKBLDq1/7Rtx9kD1+7eZN2PyKq/EPP2JtZbn2Ecsd4tgpHCTQ1OXVHvzU8ApY+3ZYz/0CKBJYUAAoQGQ1C+PxsWzgKy19NByhAEuXXSH4SCoKQWIa4Qiqq2Xy9GwekqJpbMSraRoopVW9I0Wis7OzE4lEAk8+fhAP3f/dstfWIi0VAmzZvAVxTY5i7kpQvO51r8erX3mlvW2qMHOyRLxBR4NMKdnVouBVlwQqhkywlHF5m01alv6fp4Blu0RcVQg+/JYQPviGIG67xmHsxDCj0Snnt1IAkyW1JSci+bEgkMvNRXKioFMWLLJ9O/7r67VVuJMlwqmnpwckEJOeUxPnoRA+z358XrfTGEXSUVVk5VCt8u9GrBS7VWTZAsV4ulwJRYLt0mOThNC1ci0qQSoPV1CeVl8nRKVlR2cHaOa0/djQVoIQgu4Vqz3e6Sgs6yWzxDLgVM772I9nqJRs3AgUS2WxPIiH92viMXQHMc01LDr9hPp4PI5P/9O/2I8jsXbc/rvb7cfBAPNvVQjQ0yJfQ3p2HIQQ9PT0VPz8aKiW0nJau1s3jGmm0Df0uy3HtkDsp5cCKWNSuR/myl2v65Erx/NFecEgotWntNTr6C+bDYbp2F8020+vdbnxIJ5oiP0fKY1p+BrrciEkfDjQDaBoOTfN1998AxJxZ6VrKfSZPhqPcqUl+9tX3i5zBOKwLv0k0H0z0HYlzHXv9kN4liF80tJHwxEMEFtlKaIWIShCcb2XB0x0dHYw9YfOJAu6roMQUtU70ovotMwiRodOSkSqppKaabaqwoJubtp0ibR929tei3AkLHkujnt4G9YLccAWDpK6EtRDmotUI7BTmjlp6T6ucwXR17I9TnDFuarkb9rdIpCWKfk48eRrTkpxv8hggJXKc4g+WEWDlXofOPg4Wlo7bHKaq3DdyBbYBPJ337UNrZ0yiRbq3IhTE47X0uXnOuSzVEatEEmhWGtQ1Qgixa3gyxbLg38opWXl4a0dq3HNppsqfq78u2bjaen8vWXzFqzrdDbo2gps3LgRt77xLWXvsyxqT3zrNeAXlZaVysALOsVYqnETYt2kMCw2sOa+dPEIJy29/VbnAzNRWgIAFdLoTRIEiJOeI/q33rb5Iul9E6OnsGnTJvT29lb87JiktCx/fq6CFky6cBPeXJHaZF8kSGwSv7gEPPvcajnenir1WwWd/d4JQeUdDlZf8BCV6W47g2YnMcU+oZk9Ld2g1PGQjpW6l2ipb+D3Gd1g9wAfzYNaNi+GCWSEBcW2qCKNEf0gnuWJnNAmIkGy7MrDX3rpJbz00ksLvRuLEhaJAStvBta+HeY5v+cTlssQPmnpo+HoSBCsaC1vWtVSud1wKy25amfL5i3YuHEjUqkUxsbGkEqlpPRpz/3xIjqpiZ6eHqxqc74oUdnyr2y/VnbIrM7aFWwU3hp11APJWSQaiwRYNATohVzN8KGwJpNomsffVTKGGorWGgS1pLRMyTMe7mvJSUlOQIQ0Wb2Uk0hLijNnBkCVUN0q3FSOIhyJ46JLrpS2m5H1GDc7AQCqmUJXxFHVur1EQ9MIE2iM0pK6HqOMtGQ+oPL7iBaFblVW/oq/K6RVD6OqhoAw6QhHwvirP3svVMIac8uqS7Bjxw5owfIydZHAqHdFXfQJ9CItiwYjQt3tazbIlpqCqOzk/ZpUHt5gdWctWKVAlHogKk/FSaNhyseRL5SoCtDTFUabfdos/NWH7sTu3burpodHxCAejwnsXBGLlC6ccislqA5DmrNYtBRCBNwkok1aVjhPBZ0l1k9l5UlmdaVlaQJqyqSEZVFkF9CLdD4g9lfN52lZ+blc0UlLj4WY9Qsnp0Tlrc9ZNhdq3ccNi9r3UwBoixFp0b3oK+uWJcQqlWjICTjUzeZf2AKAvXv3Yu/evQu9G4sS4jCq2e6hPuqD72npo+GopFacjtLSLQjkpGU4EsaOHTuwb/8+JMeSVX0LObZs3oKDBw5KnpbXXXsVent7EQwTJNMEnQlFUv9VAidzRNJNU9mAiz/fFiNIpumslJZScIOZw9/93d/i+aMj9v4fPHBQSt0G2CRZPG6iL6BWIlvnqzw8HGRhLpWSfLtaHLJYLA8HHF9LXv7NfSKDGnEpLeVyxpWr10IJRKDruu13Wk2FO5Vl/loFUwHg3AGVcJf9d374Cez/JWy/VLcnZVgKG6nuU9aI8hY3MZovlpcie7W7bKF6ybKotBQVctOFm+uMxiJY153H8WGKrBEBVUOekxHx2NRLZonHIuORw8Wfn8xSGCaVCNWZgocspVzJ4YBcHj7fYTAsPby+146lqN2fZlzkqqiac5SWLPn5DVdp+P7DOm68WMP7f/+9FW0qOKKOaBOD4xRf+lkRrTHg9pdrUAiZs3Iv06JlbahR578W0jmZwAuoAPSlUeq4/zkDe5/UcevVGq46Ty0ppSqfp4IB5HQqXQuRYHkYnAiR0MwVHcVd0WBEf61Kh6WMZlZanklaODJk4urz1LKFPXEhJBqi0v0lqDH/86JZbnHiY2nDtNhihFJhAVQ35AW09hiRFmWXQp/po/EQx06RIO8jGBrhC+9j6UK8R/j3i+UJ//L3MW/gPkb1lMmWKy2dv8ORcMXQHS+4ic7Ozg78zR++2lYJXbm+/suA71eXQHB2txKp7Lq9RFqm84zI8kr6rgVxwLbvF/fj2WeeQuvKiyqGDynEUcsphHXoIhHllIdPe1dmjNYoQX7S+1y3RNk+GWY5aUnB1JacdOJKn3AAaBXUsHkXMfT2d/wu7n3wt3jmeMomd6upcKdyQEe8emiKOXYIyTHHC9J9LsMCMVNrkD0X5eFepLA7gR1gE4jxKspfkXCLzoI4UD3EnOs6FRwfZszHqTELq9vLVdiimqve4ySq6dIeykY7qZcyNarosTtT8GMo+vglSusGYmlbJbJ+rmBa9R03SimSaQvrutg5yMrWvFLb4W2dlzi/+rIAbrpYRUAlkndpJcTCzosODzgszeXrVFyyVoVu0qqT2pmAUkZ+uMuxUzkqWXfMFVJ553cypWWJlFkCE/DvP6xjPEPx44M6rjpPtcnKSu2qqLM+WAwBC2vVy8PFRZ50gaKz5MKR1+ffUmG+ISktm2jCVTQo/vlHeaTywNAExVs2yVJbcUEpFpLvL6LSstmI3OUOy2Lq7WCFe4VuAhlJaQmMTPnl4csdGeEeGg261LeGT1ouZ4j3iGa6h/qoH/7l72Ne0RolZR6GXnCTlrNVYIhEp6YSxOMza/p8v9piBKvbCAYnKC5fJzM17XECDLG/x9MUK9tmR1qOjwyAmsWqZc9iYmsoQJDTqVQezlVG801aDk16P6cQgs4EwdAExWjKSarlmMhQ5EuBJ055OEFIY+UiuslUPiK0UAz/+M//gt1316fCncxSGBatrIozi7AmD6Oj8wrnO6ooLd0l2W7MRRCP10S/EjnJyWFC5CAIhcip89HgzBuJ5kVadikAGANyepTi6vXlrxH9r+o9TuJvP35qCOn0CqlcWVRiMtKyvs/lmMhQtEZht0sxpT0lqIfa4woUIrcNN6E+12CkZe3XFQ0gJZAIbvWtSFraSktp8aPUj9RBWlYqEz6dtHDJWvahugmEGmhSwwe1bqJtMkvRPg/2R2nh2IY1obRtCUzAuWKSK7XtY1mpPNxgSeliSXzN8nDhuaxwDRUMWnXxqBnQrErL44Npu0957IVhvOHyTumem5xymKmhM0dg6RcDYGx1SHNI/WY6Jj5YsJdlVR5LFA1q33/CGhDWFAQDpvD8nO+ij0WItEBkR0NwkZbVq5l8NDeoxRb/Aqp/v1iu8D0tfcwrWurwjQTKy5iDASIpmWYDL2KlXvDJukIIdrwlhL94UxBvvk5mssSE62rqNhFurxY+ydVUYPWqboAaVcOHxMkg/1sTrm5tntPDAdQsH+1OOP5monINYMctr5d8bErbwhojUPjvK7iIoYIOKFoUW7duxbbebdi6dWtV2wDdZGpYPll2Hxtj4nlsvOA8SalZVh4uKi1rqOsaUx4+PaVlq2AfyQmVaFBWLruJ2OgsysPboqRMjSqG8ZwaszyPw3Q9LVOplDSpOXN2HNu3b0c6nba3icdmJmE86bwc4jOZdUoYxZLYjjizQhA9LRdCaVlPeXhBZwN/rlJ1k+Ciao4veqgenUY9SstIBfL7sWdO296845Npz9fMFPz8uCe8qVz5a+cCYilsWPS0XOQTcB4wBbDFl7xOhSAe7/fkddY3j2ecizceru6HK6owRQ9LrtpsZoh9gtkkMpF0Oo0//cuP2o+HRtPYtWuX7bmdz+Xx/R85/mzPPvkIPvyhP7f7aX4vZ+ro5jgmPhgoLffJFVEUysOjIQJVdRNUc7yDPhYlRMuASEgeT/ptYnnj6DDwsd15fOLbBaRzPmu5HOGTlj7mFfX6Wnrxk6JH2mwQmAVpKSpAoyGCC1erZZM0sQxRDEmxLIr/fsbAPQd1m5S0KMW//aSAj+3O49iw0wnzm3MwALz9bW/BlS+7rGr4kDgZ5GSDSEYFFoC0rFXGIZbYj07JN6BMnnlaioOUkEYQUIhNhuSKVAonGp/K2D6Y9SKZtmziZlWrrBK6bmNE8g1VCMqIc1GVWLM83KW2mkm5uFtZ6TXRF4nyNR3lXXw0JHuDukn82Xj/KQpBT7v8/p52J5zq1JjlqTozJE/L2sflG7u/LT1WgzEcOHAAfX199jbxWBUMOu3jXdApzgr2BqJXqEhatscJwsGFTQ83LVozrRVw/GEzJXLNHRgk/i4vpSXAdA71lHTHwwTXblBBCHDjRc6HHBvM4v7778eevj14/x/8sUQ0zxbcnF0k9ws6LVNlzxUkpSX3tERjFizmEu4gq1TOuV7E60Y87fw6nRLaTLzyGhEARuRyiJPTgkHnneifb4j3h2bJk+jr68Ozzx+1HyvBuG1dAwD79u/DcDJrPx9WDTz15GN2Py2T2E1yUHwA4J6WlZ8v6NS2DoiF2EKYeA9thJ2Oj8UFq47FGtGyJhaSy8OXQ4J4R0cHOjq8ffiXOx45qqCgs3n1Y0cX+aDKx5zALw/3Ma9I1JEgXmlSHA0RTOZmP5CZHWlZe/87Yt6k5Q8PGPjZIXbXbY0SbL40gJOjFM+fYSO7h54xcN5rGDPL02a1AEEiHsO/f/6z+EJf5bJnSWlZuqpFgk1bAE/LmqRlwikbHk1RnL/KeY6CJcWJ5EM4yEhj/luzBRN7+vbYpeVP/fan+PD/+kcoWo2Zs4CxKcueTEZDBK0xghfOWCAE+N1br0BYIvfKD15kOqSli5gbnqRY2zm9E+JWWrofA7LSsqdDwXOn5ZlDJMiUy0Pgat7GNoq1nQpOjlq26k0LEKxqJxhIUpwdZyo/3aTS9+rTLA8/PTAib1CjoJRiYGDA3lQouo/V9FTWBYOR6bqpQFOJdC2L5F5nnCAilAIDmDZ5PluYFrteanlE8pL5dB5oj5cTBWLpOL9+3VYd7sfV8Ke3BjGWpogECR5/aRw5Mww1thodnZ0wdB1PHnoEfX19uOOOO+r/0CrgzUi8FrPF+pPVZwuRiAtrQEBh5a+mxYjleu4fC4GU6746laOe6eHxsKOK521JVOu21liUFO00xBCowjLwtDSFNtgspW0DAwNAQLjfqjHJuiY5lgQJORNw1UqBmkW7nw5L4WU+SdVMsChXFHv3CVM5p3IhGmILPL7SsrlRzeOUQ7yHRkPy3KaeBe2ljm3bti30LixaiGONjH+/WJbwSUsf8wpNJYiGiHRjcqPSpHg2ASEiZqMkq2fCLiotHzt0BNroCUTWbMbPDjmvGRxnsxZRvXVyzFtpGVCBtpZ41fAhydOyNDEMeHnRzeOcOaASOxTIC7LSskLYgzBw5Wm84ZIPFoiKeEs7ggECXdfx7AtH8eAv9k0rpEn0z4kEgTdfp+FHB3RcuV4tm4B7kbARQf1bqDGgEknLySzFRJZibWfdu1r6DvlxrogyP1CutAyo8AyfiQSJXB4+CxLfC8EAQXeLgqFJpz2v61QwkDRhUWBgnEI35O+dbhBP18o1gOiXqoZACEFPT4+9yU2CFHRa07LA/XqLAiOTFF0tshpNLDfuTBCEXYbx86+0ZP/XmhRwNRsnjNz7KZIpmiuIh2M6xFtAJfY1EsYkcggDagQIJKAhBRBNIppnC67sEfuNXGH+PCUzLtJSbONFQ+4vFhPEsBQAmMo6pZ1iv5WIEFtZyct5RYK+LVa9bYgTUFElzpXQ7sWMZoJYEt4k1eHo6ekBUV90NqghEEWzrWs6OjugnHJISzMzDFDD7qdFpWVFb2kfSw7pdBrf/ObdGB4axPlr29Db2yv5TQPy2DcWYvcVsRTYa0HWx9KGUUf6t3gPjYeINLfxiezljVxRqGYqVHmhj6aFT1r6mHckwtVJS1KhhnmmXnthjUilZ9osTBHqma/HAk5vOjCSxZ7HHkb0Za8EFOdy48SS6CE3PMFKosNBIpGWCiE11aGenpZe6eHzrPTRVFKRzOtKOPsyUgdpyZRLcolhILYSKAyzkCItIYUT1QNR3REJEazrUvAnt3o3NK/Bltgm60kP5wTjZJZ6pnzXQsFFMlmUDQRFKwD+ue0x4hlgFQ3JiufAHNwF1naWk5aP9DsJ4kVDPnai95Vh1VYM3vbmt+GuXc7xo0oI1256OXp7e9ljSstW5d3Hrhb4689OWlBVBeKncWVaJMhKmNzl4fM94TIFgqnapID/Jrs8vApRYJeHz0JpKZ7CjqiO8RT72wqtgJVLgqghiWieLbyCeHJFWpcKtREQS9se/vU+TJCLATDSZjGTlu7y8KkctZWBIuHL+g32mC+gTEdpKSrTxb6Xp71PVw29lCAuCDSL0rK3txd7/nsEg8K2DRdeblvXbNm8BfcdHYUBgJpFpCcGce3VV9j9tOgJ/YW7voqrz7U8CS4fSwfpdBrbt2/HwWfPgFo6kB/Bfffdh927d0vnVRz7xsPspiItZPrVn02Hes6peA+Nhok0rloOSstHH30UAHD99dcv8J4sPoiL7Fl/kWtZwve09DHvqJYuCjReaekuSZ9NeXg9733kt/tBDSbFUmOrELrwfRJhCQDjKTZrGZ10emEK4OhgHpbllOYFA+VlM14QFQv8taKiNKDOr8rSvS9eEJWWlYJSxEFKJEigKAQbVjoNpLjiFlCUwokEhUe9ECfctTxTtYAXASjsSx1EFR+0TWYYqVaJvK9UXuzl+ybeyPNFJw29LUY8if5okCAhKA6Dc0AStEbl71jb5fx9atQqU1O6y3drDW5VLSo9JkTB5774VXtSVNAB95Garmceb3sTGWorozl4engiTJia0FUeXitJvtGwlZY1jlvBrbSsUmLjkJZyu68nOdzrtddettb+O21EkUqlcNkVV9sERiNgUe4V7Jw/PrhtxCS4VgnrVMa5GA8++msMnDlpP17MyqGMh7epbnFPS2e7eC/l/Y54XYlVBl4QF5zEvpd/RrOWCJsWlfqjal5/SwnxeBx/8ME/lba9+71/aFvXhMIhKCFWThANFHD7O27H7m84/bQKp6O85ycPYufOnWWBaj6WFvr6+nDgwAG0t3di1ao16OzsLPObLhpUWijhXrjieHEx95c+ZoZ6fLfFe0AivPwsAw4cOIADBw4s9G4sSuSF8+/3D8sTPmnpY94R8iB/RFSaFEe0Su441dFSRlrOImikjreOJ5OghZLiL9gONdINADAnXwQtsprWgdEMJsYn8NuDz0nv/cb3/htTQpoDU1rWTusVJ4NBjyAeTa2e6jpXMPWcFJbDU0UBtp88TX405T2Lk8vD2f+bLzIQVkqfk7gIE+Qcz3AiL+RzeWl/JlPOsm64Qtqxs7/l2+TycPk5dyI8wAgA06I26VVJbTmRpZ7vL3qoBUXSYEJQL7RVUFpG3EE8Na7HmUJcZBATxAdL5eEi3IPZWoNTr/JrA7Gqz09HaWkIacqA7E1b0B1iOBFhZCVTWgr7N++eluz7ak0K+DEwLDY5qEbk8n7EvYhUT3I4h5g8vm6Fc7Gce9EN2Na7DZ/8u081VFUlkkFFWwlYTr7NFFPZ6sf3yAmn1L01HpJU/Yt5wpVxKy2z3unh4r3UK4G+o0Z5uJfSsmg43nbNmiDuVlaazZLEA6ZyF6HDuc7TeaBYaj/nrWnFG177GrS0JOznTx3vt//uXLHWk+DysbQwMDAASimCoRBAVIRCoTK/6aIBZARFHR+PiNYQi7m/9DEz1OMtLSroIkFl2ZGWPiqjUCwfP/hYXvDLw33MO2qpBitxa0opObpaymRAkUtNFQIkIq7XNCg9vBI6OjtAT40DsTX2NmqZmDj0ZSRedgeCwVYYJIL/7xP/C8V1vw9V2L+xXAQP7jsI4DoATJUaUNlvd/82DgL5mHqVh3s9nmuk02l84m//F54/OmaXRR88cFBK5O5qUTCVszCZZZPXoItAc3taptNp/MuuT2My2Y7QpR8EAITPfxe2brkQt7zhNVI4kRv5XB67du1Cf3+/vT9rL58E2t4MoLbS0r1vAPPc8drXfc8ZuO8xHVuv0vDalzknRzfkSfpElqLHQxyaLVAUDVKmSvYqtRcn+sNC2nV7BaVlJCgrpmpdjzOF+LmRIEFYY2RiKk/LCKRimdKysoE/4L3KKoZ0eT1fy3NUfm3l50RVcFcLV0IThANsj2mN988FrHqVlsIxSOdpVSKXk7Du9PCZKi1XtzkPEt3nY+vWS6FqjSXMRc6WlTUTOyG9VpuqBxNZipVtlZ/P5IUO2iqCEMtW2C3m0raMyx9KDuJxl4cz8LbDCXpVAVpqlIfHBPU1V1pJ/qNNOhFxX5fN4mkJlBPNoj/qmLDY0xEnZX6l+axgTKyEPAkuH0sLPT09IISgWDQQDAZRKBTK/KaLBpXG8XwxREqK9svDmw71lYc77SIaksfdi/ke6mPuISotmz24z4c3fKWlj3lHsFZ5eJV5T60S8fa43KSDASKVTgOzIy0JIVX3D2A+TomwfHfOnfwZilOnYOWTpc9RMDpeALRW6XVKfB2OjjmlrxtXqTaJW0khGgwQyQdUU9k+uknKuSKnKqGvrw/PHnoCiUQCnZ2dSCQS6O/vx779++zXiL6WXiXiIvkUDbLPPPz8c4joR6GkDgMASLAVJwvnVSUsAWDf/n3o7++X9mdwZMJ+PjIDpaXsaens608f15HOAz95XJcUk7pJMSmoKyspLbNF75uy10qzqJZ7ccBpd+d2K57XSzxEkBAO1VwFX7hJXk5YZPLlfpNl5eEzUFomhfbj9fx0BjnVSk9GRdIyQWxyLxxU7L/nu3TFKzQFkNW+IlkOMDVdNXKV9x+zCeIRF3miIUdZzcvtG62ccCstCzq1icx6UulrwZ2yLSJXpFACzgoUsfKgpsPoLGaViLs8fCpHJZ9UjlZhga1gl3Szx2ENZfdaN1qE93OiVLxWmnUi4lZaNkt5OFBuuyGW/Yp9cmeClPkntwk3IqqGPQkuH0sLvb292LRpEyYmpzA0PIKxsTFs2rRJsgEpGkBWaCd8sUOsVjh6/DTuuusu3yqgiVBXebhwL4qFiK+09GFDXGRv1gVOH9Xhk5Y+5h0zLQ8HqofxBBSmLhMR1srJptkqDmuFOYQjYbz6lVc6G4wMckd/CEopzPy48znhdiihNum9JLoaY3S9/fiq8xR74l9pv8MeJHAkRMoCjYINVjXVwsDAAKhVZCE5ADRNA6VUCssRfS29wnjEQUo0REqfqSOoaQgM/cR+bihdnbAEgORYEpRSaX+gOu+rFZLhRVqKyiFeup0tUEyV0qVzRdh/A4w4EQ3o8zr1vPnmPUp3WbhM+T7ki07Z+8PPlkhxABf1KAgFytXBrTF3ebjHj20A3MeL+1ZlCuUT3emWh3v5h54cdZgAr7LnejxHOaopEMWk+64WRz0UCTq+lvM9uHaUlvJvHBdIcfdvmsyWk8ccBE67cZOUMw3iAYDV7ezN6TwjN+pJip8OTBdpKap5GqHcKeiVB8sTGYpootN+nBwZRLHgXPyLecLl9tadyjptSSR7W2Oi6oX9z49HJEg81egixPJyTm4th4mI4ZJWNksQD+ChtBTa0lja+aGdCVI2hrnumsvtvydTeU+Cy8fSQjwex+7du/HHd/4J3vm727Bz586yEJ5K5eG5bAagrKMeGZ3wPU6bDF6VYm7w8nBCKCJB2XPdV98ub8j+/Qu3Hz4WDn55uI95Ry3FXzUlj5dHH0c0RMqIp5BGbOUhnx/PVlmmKrVvnuetCgJgveo7XhnDk9kr8Mgjj0BD1tm31nUgAbl2nRAFaYMpLdevIOhOKDb5WIlc8iIj4+HybXMRuFINPT09gKVD13VomsbCcgiRwnJWtDr7OTRhAefKOykF8YRKn0lN6LqOAMYAywCUAEylsi9ePpfHvv378NKRl1AsFlEsFhEMBqHrOpS4o2qtpbT0ajficealt4MT8shsaMJCa1S1f49IYgKM7HB/d65YTjLlKoT2pDJF7Nr1GfQfP4voDZsBAAH9LBRrNQgJIxaSidP2KEFr1LEbqOVDN1OUk5aseJpSSGpT06Jl5ZK1yCwvRdZEhmIsZaEzoXiSjobFPree6398MoO9e/chOZZER2cHtmzeYit5Rf9VcSLOFkgIMoXqZddzAS+lpWFSDE9SdJQuDTeRO5Wjnh6pAOtreL8zm/Rw92tXtREcLlV+nh23EA+rnrYQM4XYjooGRa7gfK7egNIy06LI5MuvV4C1P91kbVyBhVu2vh6DygV4KeXsz2JF1lUenspRWJSRykXhWhTLvws6W0jh12K1BUWO1qjTILiPpmhZMN9esPMFMYn9qRMmNq5qnoh0d7+SqaC07IiTsmqR1lgIAGt8m17+Krxh+3o/PbwJEI/Hcfvt74RFgUvWlrf1gi6Xh7eVhmF9fX2g5ltBAhEEQhHJ4/SOO+6Yr933MUeoy9OyNM4NlcYgy608/Pzzz1/oXVi0KBpiEGDztwUf5fBJSx/zjoBa2Z8RqK607EwQ2zfOjViIlIWpcBViSHO8MKcz6fZCPe+/ZK2C92zRQAhw/QUqXnnhHyOXy+HohKO07Fx3FTL2Iwtu4fPV61WoUgK49y/3Ulp6kbvzXR7e29uLH/30l3j6ZMr2kHSH5Yg+d4PjtZSWCm7t7cX3H3zG9smMFCeghLugE+9JjuhjaVkWinoRg0MjiMUTUKiB7o2r7XMQqWE94Km09PC0HJqQf8fZCYoLS9VuE9lyddlEhmJ1u/NYN9lr3BP4dAXS8unn+tHf34/YulfZrSN79kns2/8itm7dimiIYEooa22PM2/Y//FKDU+dMPH+19aQmM4QZeXhwrFKCqSl10C2dhCPhzpVpzg1xkjLSgOagl5baZ1Op/GhD38Mzx2b8PRilZSWCcW2m4gEnVLx+R5cW3YQj7MtV5TtB9xEqkUhEVIixGM0G9LS3ZevalcAsJ0cnKC4YDXbr0b1TZbAWrI2JLSzBqjbDIsphLo8npvIUOQ4gRdWsK13G+45qOOlJ1hjnm8iezpwKy11ky0MmKbcpmJB595d0GkpWKz0XI3+EwDiwhpdrsjOl3hcTOrtbbzUwY/R3qcM/ORxA/Gwjtuu0WqW0y8FuNt1WvS0lMrDlbK+VyT/X3nTzbjjzbUrJnwsDVi0cp9bNOQ+hyu4BwYGAEsHEAGI5nucNhnqUUpyu5EI9+ZfZuXht9xyy0LvwqKFaGfUrFYyPqrDJy19LAiCAQKjgqqiWvV1MEDQHidSmi9HNETshHH+LJ8UBANO2cFsy2HrmbQrhODlFzpfFI6EsWPHDnzvgafwm2G2rX3tVciUKqUvWBXAS2flEd6pp3+C+4c0XLX2tYjH4xXJFi/VT8xD9TLfE8F4PI4v3fXv+JeveyvWAGBlm0NCuxWKgDxIiYXYZ/7jpz+Fb/2IfeZzhoaxAlcm0rJJoOhjqWka4tY5sC76c6hqAK9dewgT4Wtw8Ch7ba0gHq/jL4Y88X096yIthyad3+XlYTnp8snL2V5v8utSglqSEIDbFU6lC6CUQmm5EHxMaEw8j+TYeQC4D6ysllIVgpsuCeCmSwK4eM3cqH4qlYcDwIRw/XoNZKuFbQHeBFCuyFK+qykdCzr1VCGL6OvrwzPP96O1e4OtEOZerFu3brU9LTUVaIs5yvCwUMo034Nr0y4Pd7Zli0zNwq8LL5/NSkpL0ffX7QE8vfJw+VivbnMeny1d77kilewKZgPTopjMUrRGCYoGhWGJSsvZfTYvl3b7PwKlRHmd2sc4XOqHRD/QxawS8breprK0pE52tgU1gqAGGAU2gRCTw2M1risASAivyevUJj9F5Ivzv8A21+DX54sD7I90nt0nzu1e+qRlmdJSKg9nfwdUdq909yVh4Z7rT0KbCxZl/bEXigaVApvaSwrunp4e0EGdxaUpmu9x2mQwLWovBFcCtwgJa+z/5aa09OEN0+KVLAzz7RvvY3GgyYaGPpYKgppDIrpRq3pzZauCZLqc6YiFmN9kMEDskjOevsxKqEsD6FkqLaeTnisiHAnj9Vs24TffZazUgCO6xOXrFBwZsmwiypg6jod++X0QI4un930Fu3fvhqZGPD7Vm6D0LA9fgKu9vTWOrVu3Vnw+GCDoaiEYmaI4O85KEkWiQ7wxcSVPSzxqf+ZXflHE2BHWFiYyFCvbCJ45aeLQCRPXbFDLfCzVlS8HtDgogJZ1r8TwWQtM5Yoyla4ITSWeXqaRoEMgcmXk2XF3ebjzG7zG8NkClcqW+aDNPRkU/QkTYafkW4skQAiBGSuVlVg66NRRdHReC6C8bLM9RjA+lcPeBxnx++K5Bbx7+7aGl+S5SV6xTSbTzMszEiSeSstcoXybiEpKSwA4MWJVLC+vR+02MDAAKEFPL1aLUls91JkgCAmdiai01E2mIqvlf9sImBa1KWldaGDcToBfF/UGOwGyFUK50nJmQTwAsMpDWV3tnNSa4LjxDz8o4NeHTbzlugDe+YogqKi0nKUfFieexPJXDr4YkRdCaQCZpJnvRPnpIOdxL57KsX5JJHvDAeZJnS0wglZUg9ejtBTJ6VyRnRP3ccnpFC2zTHlfbOBtZ2Squu/uUoT7+uXXB6XUXlzuiBMopLw8XFxwnY7nsI/FD8uqHDglKi1VhdoLHr29vfjXp0ZgADCpgknf47SpwNW3lQQYluXYjfAFDVFk0ogwvcWO+++/H4CvuHTDPUZZzJUrPuYOfhCPjwVBNdVfLVKwu4V4qjF5UnJEIGm44iVUuvFpanlAzXQRmAURIQYZiARWa8SAWhy1H2cHHoZe1JGIR21Pn0pefF4KFy8CbiFK7mqpWvO5PFSd/e6CAZwdzUvPS0rLklJPJELE4KXxDJtkf+UXRfzqBRP/+pMiXjC3QImvg66zO5wZdfxiRlNUuhFWU1pW+h0BVUFnnO3D4AT7/qHJ8vLwWpgSwnk4aemezI8LoQZtwu9e2XMuzrt4ExBkNebm5EvYeMF5dhm+m0zQkMWn/vcnsadvD+6//3783SfnxuxeUYjUZsV2mspTW1nsRTAWDFpRpQF4B3bwQcxZD8UuRz1EQU9PD4gattuM6MU6lXWUZ10JIpFSYU0m++aLpBJDPURijg/+OaFW8FApVFIuiAscqkIkCmk2QTyJiBN4xYO3qp0TL2VyJVgWxW8OswNw4IiJoiH7wM429IeXOmYLVEpl5/upm9R+De9/RZJmMU+4vK6nqSyFYcrHLag5C4FFA3Y5PCArqSshpBF70TBXZAnl7slHMxrsGxYjeScdS+uKHsVLDe5+hQesZArOue2Ie/tyiwtqOZ+0bCpYlFYvD+dlwEGnn4zH41i5goWZhSJxzxAfH0sYtDKRDchCFl4evtzSw48cOYIjR44s9G4sOrgtbHyl5fKET1r6WBCEqpBZtSbFAZWgq0V+kUKcyXBEKBG2lZaB8nK9mWKmSkuAkaheHpRHDz+B3OhzAABKLejDB1EoFJDNpm1PHy/iTFNrJ7ZyLITSUlVIxWPO/SZPvXjA3vaF/7ob+ZxDXMrl4eVEgEjeTWQoRqeopCgbybcjeuVHkDaiGJvIgkbPsZ8bnaL2xDGgAFqV4xjxOGccG1ezH2iYwJGzll0+zJFMV05p5hA9JzmRWjSo5NE3IUx4W4VADMNS8arb/qf9+PL1Ydt/EZCVuAEFuP++u/H8s08jkUigs7MTnZ0dNjHeaIhtVlRaZvIUI1MWxjNWRQWcl/qLw4vYePrZF3HvPffg3nvuwZ6+Pdi7d6/UloD6Br3btm3DhRdfhlQqhbGxMaRSKWzcuBE3XH8DfvKzh+3XtUUtSTEQUInd3wDzp6QyLaa2LBq0rDwcAMZLhLhXKXil4+Eu4wxUUV5Wg/u1hBCbwJjIMGV1tbLQ8Qyte3BaMBwThKksZcS38NbibJWWpfebtLxtjmdlSwLeDsT2sZhL27yVlqxtieXhIc25jxR0mXirpzwccBYVuaWHm0xuxjAe00LZfeG7d9/bFKnI5Z6W7HcmJT/Lkk2Pqz8IS0rLudk/HwsDswJBZVnsmuc2AtEgle43IY01EjUQxh133OETlk0EZhlQ+XnRWiIS9MvDfThIuypcFnPlio+5g18e7mNB4Cba2qIEE6XJdT1CyJWtBMOTzuNoyFFQcvJSJc738Emk2gD7vtkSn+0xgkGX+q4wNQz9xM+hEIrU2WdgZodAKUUhn7M9fby+16s0vBIWyidMUwkMD9Uc95uMrVttkw2DE9T2DgTkQQonvSSlZVxQWqapPdABGJFtUYAqQVy65Q8Q1k/j+YLz5tEpRwkQqXEcO+LeJz2dTqM49iyASwAAv3y2COoxrhqapFjXWblhS0pLgaTJ644aRUzcFn93Xqc4MqKAl7l3h8dx9913o6OzA6/esgXRkNPoIyFgZGgQ1NJL5c/WnJrdBwNOMnFMUGKlS9v6By2pZFhEtlDZf9KL2Dh5ZgRP3vtV9l2xGBSiSAE6gDeRmMpRnBi1cPk6dpyC4Th27Pgw9u13vFhvuP4GfP7zn8fRyQ6ELrwKAPDLn/0QK9MqLvzjt9oTq7CwYDJf5SuTWYpPfLuAbIHio78TwisvYhc6L7HPFhihmdcpxtMUp5MWLl2rQCGVB35uAj8aBCZLdgQzCeLJ5/L28TTMVwHohGkB6RzQFq08ESkajLhc1Vb7piCe20yBeVCKZOtsy8PFPixToLayv2hQZAtyqXTUVlo671/M5Uxe19NUjmJyKoMnnnwBwEUAAEvPl3yDWeG9uNiSqDNDJRokSOXY8fKyiGnGyYhpAgOjsufFN/q+h2f2/deSV5K523WuyBZRxgTfYl6NEHD1K1J/2YTnfTmDWt5BPEWDtQ/ebiIalca2QcFixUdzwaLeFkkcovUKn8ctN6WlD2+4vcT9trA84SstfSwIQi7l2kqBuKjHM60z7io9FUpguU+SGMoStMvDZ7K3MmZrUyeWiAMsOGhlVxQwUggM3gM69hhM0wSlFMVCzvb08VIC1uMjZu/3PPjreaESWWr7TZpJexuJrEJyzHks3pi4KqNaefiw4Bn2zldo9vk+W1yDlnNeKX3/WJraJQeRGimuXS3lz6fTaWzfvh2//MlX7G1PHHd2WCx9G6pSsgw4/pSA7OfIVWaWRZESBnQiiVrQgaND7POJlcf93/8S7r//fuzp24PP/POnoCnCPgUJelavAKGGXf48l2b34uJEXGirfHCazlOcGbOQz+XxrR//Gl/8xl5bIVlNaZnzIIAULQrLtGCZFjRNQyKRsAN0ONyqvlOjFh47amB40rIHRQWd+c9u3boV23q3YevWrXjk0UfQ39+PUMI5RsmzL+GLX/icVFovBUvMk2LswEsmkmmmWHzsKOs3TItKZZvDk4y4/OcfF/CFvUXsfdKAYcGTYAfK+8mIcO6mq7TkimpuR3D62LP28+OZ6krLgk49Q9e8Xys/nnIFXOmzVGmIpKc4ueJes+LvaCnZDweWiNLS6xyMpwz8xV9+GI8ceNze9om//Thawk5fdmrU+TsRqa9hcLI3VwTSOa/wtcV7nGYKwwIefeqotC3e2j1nCvf5hJftRLYAJFPOueVKS3e/Ilad+OV+zQWmtPSyJHEWMgFGToljfj5eNC3v9/tYurBo9cXDjNAuoh6kpU9kL1+UKS39+8WyhE9a+lgQiOXhKmH+cBz1cGuKQtDT7rxQJIjCNmnpbLPLw2ul/NSB6UzavdDmIi0TEeDmLZuxceNGZDIZaJqGcDiM1atX484//iNbieFFuNZbkreQqFS+3tHZAUIIjPQAW5YHoMR60NHZAQA4k7RwfJhtDyhOibZIBLjLw4cFP8lzVyi4ZgN7ca4IPNovj3hMy5msF/OTZaXEHNEQ8Uxo7+vrw4EDBxAPpAA9xTYSp2Fz5R5Q29eSl0tZLrKJ71+2KE8OOwWl5ciU45VmTJ1AIhFHZ2cnEokEnn/6MZw8+rz92kgI6H3XO/Gyyy5m5c+jIxibQ7P7oHCuRNWkOAAZn8rhHz/7NewfugZP5Tfj2/cewK5duzA2kan4uZ6EoOp0AqZhSgE6HGLYQypH0X/WtFf+ObHsNQnnBLsZaLW3WdlhBFQiEQ9RgdybL2VdpuCQAyzUqbzcd2iC2RZwj8ijQ1bVckz3QkNEIGPdieDVoBBiK6q5HUEQjs/BeIaW2SCIKBpMQV0P3L6M4kIAwErHZ+NrKZa18clV0aA4k2RPiErP1hjzXRbvN4tZGeDlaXnizBiefvZ5hCKOCvDZQ08iP+6Qb9yXFmD3sXrA79UWBZIenqWLWZE6U1gWMJaWr5tAKDFnCvf5hFe7TudlpSW3hChLD/eVlk2LSqXABYNKqil3ebi2RPpMHzMAZV6nlSAuYjlKS7883AeQdk3P/L5hecIvD/exIBBVg7EwQUhjykndpHWTguu6FJwes2BSt9KS/R/28LZshNJy1qRlVJ68tMYIwhHmQyiWpG7ZvAUXrosiHmc77R7wA9MrD18oVAqx2bJ5Cw4eOIj+/n6E82NQIt0IxHqw+VXrkc5T/McDRXsic+PFqq0UFYOQEhF2PkyLkZbixGdFC8GNF6t4pERWVhvujA2dxq5dX5JKiTm6E94kzcDAACilCAVUFLLHYLVeIT1/5XoVj77EvnuojjCeVI6CUvm7OBGSKVDpJp2IsLasm06gCQBY6VNy6rWeRj49bj8fDRK0tsTw2X/ZhS9/Zx8mkiO49tzXobe3d05KFEXySyoPFwYg+/bvw+nxAILMfx+R7svQ3/8j/Pi+B3DNX9zu+bleyjAScFgTNaBKATochuWUDZ8ek2dUw5MUG1Z6kyacYKd8JwFY+VGEgyoKGYd4CC+Ap6WoWsnrTMngJqEmc1SyF3C3JzfcoV8iGevVD1WCqgiK6lK7DFhp8EPMCUnRBkFEwWATlXS+slWA/Vq30jJbfvwNc+b3AJm0pBiasPDioJNUL35/IqwgHibSd3mR4YsFfN/jYdaeLApkigooFCiBMPhyD7WKUPIDADYAAE6PCeXhkfrIbLEtJVMUcdf7igaddmr8YodhUZiBNkC45gwamDOF+3zCi0jI5CnGJE9LBSopr6IRlem+cqa5QC025jItKp33ouH2LpTvKW5lXZ2uEz6WAGp5WooLjdEQ97R0ti0HomrTpk0LvQuLEu4gHt1kSuyFqiD0sTDwSUsfCwJRBckno/EwMJ6pv/w6GCDo6VBwasySJkLBAAt/Eb9DU7nyZfb77lW+rhKgXhGPW2nJSUxekipC/CpNZaXk4tdEp1EevlCoVB4uErW/PGMgaTL/ybQRQt8vi/ak55wugndvdmY3oi+pQgjaogRjaYrxjFNCHQ+zY3P+SgUr24hEGnbESVnJqUoMu5TYfQ46E94sdU9PDwgh0Is5KNnjEmmpENi+gRYFhiarl4cDzJvQ7VvKyYRMXiaZYmGCSBDQXWoyK3MKus78KnVdB8wsOtoiwBB7PhIkCKiMuNy6dSvCGrE9EOcCfJWcexoG8HIYCEor6smxJBCI2Y+VUDsopTg7Mln2eRyehKASglJaUdB1HSmDBejwFHXnvUCQ0rJzki1SpHJyaBLf7+GhYbS0tqAYZqSlmZ9AMABEQ0EUJxziQVwoqVb23EiIYSi5Ii2RluWvmxTKpbMFmWxw9ytlKb+C0ng6Y0RCHMKXt0szP2Y/75RW07K+jFJql3SPp2uTlu424UVa6oasGp0ODKGDT+cpnj0tK7dFojgWZiTekvG0LB27sEagEIqpHGCQKIgahEWFH2EZWNdF8PTp0kPhELfUSVqKC4ypfDlpyRSxC+fBPBcwLcBU26Vt2QKdM4V7I6GbtGwRQ4Sn0rIAW2mpKkBL1DvoTrTwWQ6ExHIC7y5NS17oLxoUWWHRMhKk0uKOOG5n96jFP8b1UR9qkZZiBU6sdJ+WlbfNv7Bx/fXXL/QuLEpkPArhckW6JKoNfTQOTTQs9LGUwElEizqkZSxEMJ6h00rnPrdbweC4ZfufcISDRCIQACAUqJxkPR147V9bjCCVr++mWkZaxip3umJ5ISGMcOLKHk0l0qB/saLahIcTtblHdTzwFJu1fGOfbpcdtkSAP3x9SCJN3OewPc5IS9EPZ0UrexEhBDdepOIHjzgzopsuUfHjA/IMSaWFslJivu+tUe997+3txX333YfHXhyDojyF6Oq32M91t7Bz09VCMDxJMTTBkpKrldZOZan0OwFRaekKJQqVzr3Lt29dl4rjz6dspdLVV16K226+Gg9/lxEB67qU0u9ir5/rRcpgwPE07O/vR/jai6FEVmB0Mo98jimMOzo7QDTnfJhqAoQQtHasKFNpcIgEUCTISLpQpAXve+97AQCpVNpWK7uVs0WDpcx7VSSfnbBs3yRxvymloEoA8RAjHmh+FFpQw+S4XFov9jleJbdzAZGgzBeZqitXKP/uakrLRERWOQTdnpZC/zpdpaWoqKaUQokEwDWxvFzdKw1eNx0idTxDsa6r+ne5P8PtaQnwMJ2ZNfpqky3ArbQkaCkjLRfvhIvvey4zgYCqAohDpyFcctnLcNSg4DzCVVdehrduvRH//Y1yYrw1Wi9p6fztdd4BRmA1E2mZ1ynGs/K2zTe/Af/+lzsWdQgPpSwwpZo62YtszOSpnR7eESdQCPH8jLBEUM1yZ30sKlCBtBRR1NkCIUciBElV7ZeHNy9oDdJS9G3nlReS0nIRL/z5mFuINkjONipVcPlofjTRsNDHUkMoQJDTqT2J8UqHroVggGDDSrVMIh4JkrKwn5DWGE9LL+IzESEgBBhNzYC0rDLZcx8LXhIMLI3ScEAedLRGCLLFcm+51YI/KScsFQJ84HVBtMeJRBS7j4kX6bui1dl2w8YAfnTAgGkxBebV68tJS0vPlpUSAyxAoFKZYjwex+7du/GvX7kXRweLeCSnQ6es0a0spR2vamOkpW4ytVhnhVJzgK0yu82muVovU6DSgC0eIVJpHcDIpI/86Xvwq1+tty0GPvI/t+Dpk8ANXU9gcBzA4ATS6ddAU5myca4rMIMB2dPQonlQAFQJ47/37cOtt2zFls1b8MDh58A5M0tN2ArJXJGdMzdEAqglQhhBqIbxpje/ueY+5YrAmaT3dTo8Se3BsrjfmqahqLSDD5vW9yRw3ppbcfnarXj/e97hpIcL5ySTny/SUiQjLfzX13bj2GAWsfY1Emk7KSbUF2RlYluMSCSf24c2oBIEAwRFGQwUzQABAABJREFUg06L6FYVlFlftLR34b6SUs8pD6/ubTiRsUCpUrVk2P0ZkxWUljOFVxKu9P3CeYiXlJbiBHyxevZNTqXse8rUxChg5KF2XAbTAv7yI5/AZ+8exImSvexd//F5DKUj6GnP48iQfEDqVVqKitlKxH6jFVYLXW4+NEHLQq8uuewqxON1GoEuEAyr9rnwCrg6NmzZ9y5+L/ayiVEVYtu76Cb1y/2aCGZpVbCMtDTkwJV4WG4/4rjdD15pLrCQwMrPi7ZBcY/y8MVssdIo7NmzBwCwbdu2Bd6TxQXRBokj47HNR3PDJy19LBhCGksB5vJu/v90gh4AYG1nOYsYCaJMhagFvFf7pwsvpSUjLSlGU7Xf7+VpWQlugk5U7iyF0nBAnqysbFOQLVCcdpFGq9vKD+pt1wRwwSr2g0W1XUAlUjmrJ2nZQuzS3uRYEpe0vAxn9PPxpus0RkRCLoct5CZx2fpoWSlxNZIRYMTlu373nTg+YmFybwFPn2QjslWl37OyVQFKVNfQBEVnovJnUZST3kWdTeRyBblsOR7mSmJnW087QSwmWwwE1Tz+4VOfxOEjg6CU4pCRwvO//DK+/F/fBBCeB9JS9jQsmll7j4fHSonbkTDO2XAJDg+y7eGWldjxQeYtmit6lwXzCTEBOxZDJWKY+1VWw+kxq+Lgt2BQe6Lk9mJUoyts0vJlF63Bm6/bhk3nB6TyVlE5lPJQ+s0FcgJZN3B2DJ/52mdBYudACURw8MBB26c1mXJmgBTA6IQjc+tMEJwcdT7Hi2CIhtiEczqLSvyydVtfPPT1HDIFYHA0iz19P8C5K8P4i/ffJqnOxPZuWGxCUy3sxV2O71kePotJcLXUU/f3JyIKYiEgvMB+XOl0Gn19fRgYGEBPT4+nd+3Xv/ldAO8AAGiKBYqcfY1mjTBW9azFiZIvcGtLHKNZEz0dBEeG5O+qVjEgQiwPr0RaNrKU3rJYAn1Xy8LdL89OlM/Uc0tANaQbta8Zr3Z96ITzpgtWsQ6jUr8cCrCguaLBSor9dNDmAK9kKCctqeRP5+7T/eCV5gUrD698TjN5p7Hw+Y3cHuZu3xYLkslk7RctQ2Q8qofcPpc+mh8+aeljwaAFCEIB56bElYOzDboBmNIy7FZaBhrlaVm+rSVC6lYgxYXwGGD6SkuO2BIhLcVBR1eCwIoTnE7KI9lV7TKReP5KBVuvcrqncvLWKZNv95gst0cM7Nr1GbsklZC92LhxI65+2w4E1ADaXb6W1151Kb6y462Y0mM4PGBCU0t+mK21GyOfjF24WrFJyzUdjtKS4+wkxaXrqn+WW4FqWMyLkEIesCXCKGvfvPSbQ1MJvvWtPXj+2UNo7d7AfC6zFg4c2I/vffdbuPDG985LebjoaQjTqZGMtq6w/87pztkvWkGoGusMvLwZAYfU0AKyujGvA/Ea17jX4EcEH1O7vRgNtcV+DSdA3IsgYsp8yjuMvuEQj1HRVNHeuRKBlh7ouuPTumXzFjzXfxoIrrVf+6Of7gMSjKTvjDvkOuBdmhsNEqSy01OsVerLW6NslTyjB3D/Q3uB4jgO/GI3du/ebZNqbuIqr9OqYS/uRPlJD9J4NpNgq5bSUlJCs5JHcUGqME92ARzpdBrbt2/HgQMHbKXhfffdJx1jADg5KEySLB2KlbGDd5IZKqlTgwFAUZiXNCCzWS11igbFci43cccXmu6beAkXrYs2JCDMsIChSYqultqvnSuc9Qhic7fXxQjDrK1OLpaaQUBx1MiiYuqC1bIliRvBEmmpG6VrrAFjNB8LC0sgpkyXJUfRkKsQWiLydSC2k+VAUi0nUFr9Pir2GzGv8nC/PSxbeCktvbb5aG74i5o+FgyhgDyBCagEEY00RP0VD5Oylf1goDHl4W6PvWCA+QvWm56qECL5f1VTqJSRllLqel1ft+Dgg9BEmCAcJIiGCDri5ceQE31hDXjvzZp0nN3HQXzsRVqe6H/MLu3t7OxEIpGwCRygXEG56epLkUgksKZDweXrVFx/gVqamNcG35cbLw7gynMVXLtBxVXnsR/dKah7kqnaYTxe4N5g7iCesMv/0q041gKlhHOzaKsFgwHCQm4Gz4Bg7j0tFYXgtTdvwcaNG5FKpVBIOyEsl15+nf132kXwTZRUcl7ejIBTJqSpMlHYSCJgy2Znv8fGxlAkrfZzXaX2414EEc/JfJWHi76ARA1Di7QDICw9vuTTum//PuRN2U9gbMohnbpc18OR/sNIp9PStkiQTLt0kxDvxRwzx9oBUQLoWHEOWjq6ceDAAfT19dmvcROMtSYsbqXl0FgOe/r2YO/evcjnWAOrpZasBr2KQoR9v/N8a+leIPZN810e3tfXhwMHDqCzsxNr1qxBZ2dn2TEGgNYuYSXFKsAqTNoPx9OWtJASUFh/t6ZdPqlhDdDqNIxOVCgP5x6ye/r24Fvf+QF27tyJ7du3l7XD6cIwgdEpq6rCZy5hWhQjU+V9/3wFdc0GhkXLFtKk501qkxBe4xhNBc7tqkFalipidLN66aiPpQOxybjPacGgyAr3rNZI+ViQw8t6wMfSBKVs8b2azYo4ZuK2ATKJ7beH5Qqvqgxfabn84CstfSwYuGpDRCxMGqK09FJ9hDTSEKVlLAQ7RAhgqjeADbYiGpHKNSuhPeYo/aqRlu5S+Q0r2ME5O2EtIaUl+79bIPDWdihIpmUG4fe2BPGbwwauPdfEwd88aPsybtm8BUqHnIYjkZbx8uNQnBqQSntFAofvS/+g8/qEQACvqENdKYLP1cNBgj98g2w02ins21h6ZjdY/j5x8hgNEmiKfPxWxnWIXXowUEo4p4aTKF7MgxCWdq0FGrNAUAst8YjtafjUYCeOlybrReqQaG6CbzxD0d0iG/aL4ARWMCArqkUi4JF+A785bOKWqwK4ZO30L3y3F+Oz+jVIliZbazpYMrx7EUQMrKml6GwURLKMBMIo0DCCYAnq3Kd1bDQJEpSlZiTUZv/dEpEZlMcP/Bbbt++UVHnRkLefby2oCoHlIj6IMQWAJbFTrRVaaAqUUgwMDNivcZN8tSYsboI7q6u4/6H7QQixy+R10+lHMgU6rT7UrFUeLkzE+QJWe9w5YPM94RoYYH1gKMT6pFAoVHaMAeDGm2/FN4+yvwu5FKz0GHgvls47SjtVYfdrlaBsQScSrP84iottzz5/BGefPICRkRGkUlN44YXD6OzsRCixFkZw3CZZ77jjjmn9dhGGyUiU0Slqew3PJ0wLGJmq7tm6WKHXUFqKE8m2GCmzN1m/QrH7SLdPLgcfH+gGPMPRlgMW2nO10aAVSEvdZAF4ItngVlr6yrrmBL+2qyktZa9T9r+iEFvF7beH5QuvqqtshUosH80LX2npY8EQ0kiZX1083JjycK8BYEirnoJZLwIqQWfC2UlRYZmoM0H1pktUaCrw8o1qVW9KLyXXpWtVXH9BYEkkhwPsXGgqQVeLc8w6E6QsKXtdl4K3XmPh21/9DPb07cH999+PPX17sGvXLhQLcvSqSJ54pbF3d7U6JcmQCRygXFlWr0rWC9Xaa1vMIQaTMyQteTiPOGAz9QwOPf6w/ZhSC9/9+r/aijKATRJ7e3vxsssvsdWCE8lRO+06oM690pLth+NpeOPLr7K3Z+zfRcsIKp4qXak8vKg7Ju1hD6WlblDs+ZWO/kEL3/q1DupOwagTfL9/913vQtpiSsvOBFMLe6m2RfJmvkzC3YqtdA4YGxtDKpWyA41aOrpBVFmaTUpJ6ADw0rMHQS3ng8IhtUyVx5SW098/r7l4e8z5m2qt0HUTRA2ip6fH3u6eoNRKDp1MywecKBo6utdIKmtO/FsWxfHh6cm6aqnARPK4pXQfEBct5nvC1dPTA0IICgV2XAqFgr1gIUK3nBW+9etWY/ONm+zH+SK1y3/5vVNRmN+Y2O9GpxEKF484jeiZ54/ghz/6Ifbv348nnnwSuVwOY2NjsBCoSLJOF0ZptuzlKzkfMExv0rKSn+digmHKnpbpdBp33XUXdu7cibvuuguj444KNqyVW5ZsXO2c60pjr3BpHFM0lq+ybikQ2NOB2FeKfxeEYEGOVpfAQPNJy6aE7XFa5RKXvE6F4QpvE357WL7wUlUuhXuoj8bCV1r6WDCEtPLV91ho7tRfwQCZkVLICytbCUam2N8i4dUSIRierPAmATdsDODaDWrNcvVKhJhXOMliRmtUJqgJIVjRSnB8RL7puBObdV1Hf38/7rv3Hlz+p73261RhApRweYSuaCHYsnkLDh44KHhaEpvAAVAWytAanXnDUKsQ4apC0BYlGM9Qu8x7phAnNj/8/reRHFERLNm9kcIojvQ/j33799mBJ0GVBQV97l8/g7u+xdSCF6wO4M73vQXxeBxB1Zh26NVMwCwN2G+PCW2Ak7EZD+/HiRLBW9C9E2U5yRlyTZQ5gXdkyLIHuCNTFGcnqJRQP10MTVL7887p8i4NB2SlpVslSinFRJaiPdbYtUJ3Sfxbb/9d5CcHbZVyOBLGFVffiJ+clt+nRbttF8vs1BBg5IBgKXRIoWWEUTQ4Q6Vl6bC3RQkms6xE7KrLzsPzv2Hb08UAzFQK12x6JXp7nWvcTWBUm7Ck02l85/v3AG1yerylxqBpeVtlzcvDz05Qu/3Vi9rp4c7fvK9riYqk5fwOsHt7e3HfffdJnpZ8wULEZM75YVdecQku6lHwyL3sx+R05zzw9s7vSWs6iL24MB2lpVgermhRUItCURVYJQlOPp/HVEpBvOhNsk4X/Jwn0yzMrJLib66gmxRjpb6/M0Hsv5dCqSMjLdl+enmk/uD+A8C6XQAYsRALEYm85yE8/Hl4LEJx7zoKtpDVPjsL0yWJgiF7My91iIpZkbTkbZ570UWCQMjVd0jl4dUYLh9LCnzd2KxyTuWAJqcdaCqQw9LoM2cLMbBwOaCe8ExAXhTmqGQf5aN54ZOWPhYMIY0g4lqZj4UJGmA76YlIsLwcfaboSjglCy2i0rLOMAKgPn/NRqhOFwN6PAgjt7oxn8vj0UcfRS6XgxpQkQgk7LLukWE5qpYdO3bDUggjBnkZ9YpWgnAkJJX2igQOAEn1CdQfIuGFgEebEpPLiXEzgFZkCoyEm6lClg/YCAGGBk+D6l32c0phUCp/B5wyq7aWmD0QunC1injcSXMl89C+xHKvuLB6zkmjtMfAY7xEhlAAz5yy0BZj57glyjw5izZpSWRPy9LA5vnTMsP09AkTq9tn/mNPjjqfxwOPgp6kpaz6NC1qe7NmCsB4mkoqw0bArVK9/hWbscZVvpuzgnAzBlR12IGVnQnQsTwIWAm5qefLCCNFIRLpXC+4OnNdlwJj2EI6T7Gi3dmfDRdfg+tuXo0/2rZFCl1xq48qJb4DzL9xcESHUPEOgAX9RASVNS91PTVmTVu1UW2yxfa3RO4pzsRbVJfNt5oqHo9j9+7dNdPDRT/ZkIdy2VFasu28u+tpV/DsKXZdxKahtBQVNFDDoJQiEAjAsiyoARWmYSKVLaAwOeZJsk4XnLSkAIYnKdZ2zi9peSbpeDWu6XBIyyXhaWlS+/iJHqmhUAiFQgHPPN+PeMkSVVMJYmGKsZL4UiHAeStEpSWB108WK00mcxRrPV7T7GB9x9JaiK4GsbDBEPpN3gdycioaImUKXL88vDlhk5bVlJalIUpAke+dfOFbn4Un9VLBBRdcsNC7MK8wzPoCcj3Lw888BBwfAta+HQgsw9WuZQiftPSxYPDyE4s2kFh0wx2gMxsoCit3TqZlEioRllOwZ4tmIS07EwTpdFqaQL/9ndsAsBksD2F47tnnUCgUUCwWkc1k0d7RjmKxiGeefgp33XWXPel2K77aYg5pubK1FOhTKu31grs8vJqvaC24zxH/LVzlGbpoNQIrbgDA1D61FH8i4SmSrZxwCQWANWt6QHUn1IZmTkvl74BInMgr1uLf8+EhJpOWotKS/e8VWMMVXAAwmrIwmmJ/X9SjSucuqMqTXq52e+GMPLp9+qSFN1w1vf2eyFDEw4zcPSWQlud0OqSvG2KZrG6ygRYnatN5iimPROvZouBagfYqmZnKlm8Tt9z2hs34ydGzNqmQTU96EkYzUXirCisB7UoQjE4RpPNUCqhZfc7F2PqaK6C6WODpBPEMDAwAajndUTCDMIQyed2kSKYtuzzRS8XrBUpp1ckW4CRhi2pbZo3B2sJCTMDj8XhNP8iU0CaDroXEXBEwSvut2UpLdrx6OpzjNh0yW1zkI4EICCEwTRMKURCPxVEsFnHpZZfif7zmTfi97dtmnR4uKr3m4vqrhSGhLL1TWOxcCiXBYnq4t0eqs9oXDPD+gR3jc7uJNDYKqPAkLcW2k5qn8LLFhmYj584kLXzq+3kkIgSf3u6sUhR0CotS2zol5uGT7KeH139fWkqwy8OrEI+czHarjvnQYLm2h2aGbvJZYHV4jWvzE6eAE98Bhn8OXPN5n7hcBvBJSx+LCkvpRr2ylZSVMAZUgkiINCTVjKCxROtCIpPJlJWW3XffffjTnd+EooXtsvCOjg4kk0nk83lks1nk8jmoqoqnnnoChx59APfddx92794NVZGlke1xApTEmN11BOnEQqysOK+z4zwdhawb7lVCd4l7gaZtgoiRlpU/y014iiEiRaEkure3F9/5+T/gZOYMQBTkT+2Tyt/56wDZI0rc14A6uzTleiGWe4mkFyeOvMp0xzPe18/hARNTbc75DWkEcYEozOkUqRzFqTH5/UdLCr96SbdH+g18/SEdqzsIPvrWEE6OOp+3rkoarujTWjSYUo1/ZypHkcrV9fXTgpv8yHusSE94kJYiutqjOHftKrxUuobe8LpX4zN/tqOMMJoJaakoLLiIEIJEhGBwQl4kGE/zkkFnH3WznCSsVhrW09MD8mT5Rdxz7sV4zeWX2sS/YQEnRhwSqWDIJGMl1CoNBxzyOOwqd+Sk5WItdRSvv1BAVgvnik56NO9H+CLNud2CR7FHGFolhINCkJ0aAVEITMOEGlBBQXH55Zdjx44d2HJFvCFhc+Jxd1spzAdEojQSJNACgFEsX2xYjNAt5/iJHqlcaakEnGCrgCovRF+wSu4geYmnG6JKNz0H/eNiB6scWPxtYTp44CkdZ5IUAMWTR01csoZ1HgWd/eOqOy9vaL88nN0vQk0iWOCoT2lZuoe6qpECSmmsmMnjrrv2eFYMNAs+//nPAwDuvPPOBd6T+QFTYtdTHl6+LWcGAVhAbhA4/QNg/Xsavn8+Fhd80tKHjxmiPUaQLZSPLFojjSEtm0VlCXiXlh04cAC/3rcXr3rdW5EcS9oqjpUrVyKVTmF8fByEEKxetRrhYg562rLDQV731vdLn79hpYKDR0xoKrC+u/aBI4Tg3G4FhwcsdLcQhLRZeFq63sp/C08uV80U+AJxrTCeSp6e+/bvQ9HcDKBE1MXj+NQn/xrfv3cfxpNJdLzzTVL5O+BdohpwqS6rJTk2CqLSMhqErUS2y8O9PC0rkJYA8MKAw7SGNFmpky/KKktOGFEKPHPSxMsvrO+W98QxExTAQJLi8WMmTo+xA9UeI7atgVdJi0hY6QaVBlrpPCOApkOeiqikvnCTH17eP1PZsk0S4mGCWFgBSi6XN934csTj5TW/8XqWxF3QFGKri7nKLhggiIVKJfMZinwuj2888AsEpp5AT08Pfud2R4XNYVE2idU8FK69vb2469FHkXZtv/aGV2PrTTKZKRLiBb0+0rJWcjjgkMVR1+dxLz93Gf98QrQpcEMiLTUiqVzyurOwwfsO3t+talPwO9cHcGLEwls2uXxeqkBVCCJBdu4Tbd246a2/g5GREXR3d2PFyhV2P1bUp5fuXgniwsxClGSnRdJSY/1hrrhUlJYUhsX6Hi+P1Is3XwHuehsMEChCErToZ6mQynY4ceEcL0elpW7WDvlaahDV2xMCEV0w5MWpaIhIi6qAXx4OsEWyQJU+eykilU7jh/f9FoXJ0zi03vAkHvk9VLwnp9NpDJ09DQTXQjeBnTt32uKFZiUulxOKtcZWRho4/QPkcrcCkMekOYM/JkD+7Fzsno9FhoaQlvv27cPNN9+Mu+66Cx/4wAfKnn/xxRfx6U9/Gj//+c9x9uxZxONxbNq0CR/60IfqMp09fvw4zjvvvLr25dixY1i/fj0A4IknnsA111xT8bU33HADHn744YrP+/BRDYpCsKajfHtngqmJGvH5zQLv0jKKybEzAICOzg477VvTNEQjUaRTaRBCEAwGQQqWlCbrJi5edYmKlghBdwuRwi+q4V03avjNYRPXblBnlSqvKrIlgPu3WPlR+7W1SEs34ck9PZNjSXsAz4NnEvEobrmlcv/JB/8BldjKpoCLwJwPhYeonFAUgmiJrOJkpZfSMpWrbNAtqpsZaek8l9cpXjjjPP+GKwO473F24J6eBmkpllPfe9CwyY51Xd6l9uL+2PtpyiUt/PdOZWdGWqbzQEu0fLt7Yufl/TNZQ2kZCRKpzD5YgWCox4fXjVXtxG4D8bCjsmuPE2QKFBMZiv+76//hpf4XoQ8/DGLm8OOf7sMff/w/JBIeYESP13GPx+O4/Mrr8PBL8vaMXv3CZj6ZtX9TLaWlXiJ3gPJQGq3kv6sv4AR8IEmltstRdBHroQDrz4IB1q5yRed3ienhHG+4kjX4nmn4xaoKO0aZAgVRI/i99/ye5+saRViI566gU5twmy+IizLhILHbw1JQ13HC17C8PVITF70Ln/wBe01YI7j8fBVPHrewqo3g0nVOm6gWfhQT7ALSC1C+v9DQzflZPJxPiIS8aP9S0J3ScKBUHu7qosX+fSH7zIWEYQJWAJjFsHRRIZ1O46077sdE61boAw/hnq9+sIx41E3HszKbHsc//dPncf755yOfzyOX3gS1Yy2IoqGjs9MWL9SyPvGx+FG12stIA4/fCeQGkTfeWPZ03uSkJQXCq+Zk/3wsLsyatDx8+DC2bdsGSr0HG7/+9a+xdetWZDIZbNy4EW984xtx5swZPPDAA3jggQfwz//8z/jwhz9c9Tvi8Ti2b99e8flDhw7h6aefxjnnnIOuLiec4vHHHwcAXHfddbjooovK3rfcDG99NB5exGJXgk1MZlva0qik88UAr9IyQgjWrW4DAM+075WrVmJyYhK6rkOlpv2enp6eMnWjqhBcs2F6Q7yVbQredkOp1HeWPWFAON/u36JEg+BaL3eCuNu/MpGIS4SnXgoRae/oQPEUew/3CavFH4mKhWCApbrK5uaAMg9qn5YIIzUGxkuhHWFGWGTs9HDnmLTHWNI6BSPaOhNeRIvzd0gjSISdxpAvAs+fYd+jqcDrrgjgoWcNZAosnKfepMIpQR0yJhDN53Q53+X1OWJZEysP5/vllNlO5Shmkoc8laNlhLwYSsThVQIrkpaqUq7siYZk0nK214OIbiH0SlEIYiGCVJ6iLUZweozCosCRE0NIJBIIRS6DPnkMTzz1LPbt31e2qKlXIRkNSwUgj4C9vDxFFOts/7VsFMRJejTkJi3Z/24rkfmCYVKcHrNsWwMRvFyTg6uWI0FOWjq/PegqDxcxnaoAQoitpMkVUZFEbJQyVVTJUrDfO59JzWLQWFhzjuNCKm/rBT/3RaPkWenySP3SzwoAGAsVDgJrOxX8/btCZeczXEWIKyktlyFpaZjVS2aXIiTSUmj/nkrLKuXhS4HYnwuYFoVlkaZhLfv6+pAMbIYCQFtxPaIu4tGyKI4PO4OSoYHj+OqDX4WqqkgkEqDXfZE9QRSEwlFQOsp8rH0seVQdW53+AZAbxJQegm6xm0hIKaBgMbKSkZYKEFnNwnh8ND1mNTX5xS9+gW3btmF4eNjzecMw8Hu/93vIZDL4x3/8R/zVX/2VPZh58MEH8aY3vQkf/ehHccstt+Dyyy+v+D1dXV345je/6fnc2bNnceWVVyIUCuFHP/qRJBd/4oknADA5+RvfWM7S+/AxF1AUgpWtBKeTsxtwKU1EWnqVlm3atAnv2fZ2PHWGhea4075vuP4GfP7zn0d/fz+Sw6dBzKwdDpJpsBejV8npdKAqsFeJ3b+lpb0L951mz4lKSy//yg0bNmDDhg04evSovW3jxo248abN+PGe0ueXJoBqlQGtphJp4qiprDRSJMI11UkCnksQQnDxGhXBAHB8xEI8TDA8SUulp1RSIq3tJHb57kSGojNR/nligmQoQKSS5WdOmbZv5AWrFIQ0gsvWqXj0JRN5HXjprIWL11SfCVBaOTBHJH5CHhPxkHBH1U2KnF7u2ykSiEWDkbft8doX+2SWYm2nvC1XoGWhX55Kywz3imIqN7dnaFiTveWqKaNmi0SEkZZiGA+0NmhaCpS0MUV1jiA5lix7bzWiRyyL536ttYJX6p0UmzUSq0SiOOqqqucE8EKVOmYLzOt1KltOeud1WfHHvSnDGsEkKHIFagcocEWU4kEwTkeBqyrcRoGWSv6BgEphWfLnNIqw0F3nLj/fpKVw7YeDRGoP8636nC54X6t7+I5ZFgu14uBEtNfvCWqVf6PsczzzfV2q0A06L4F484mCcO3yRUlKKXSDIiuc44RHxYFfHl5SWjZRmxgYGACU0oBJ0RAUqqbGUhaeO23h7IQzsAsQA6tXr4Zpmjh16hTa9bxNVhR0aosXfCxtWBatPrbKnwVAMJJzShpbghmM5NkgKxdYA5z7bj89fBlhRqTl8PAwdu7ciS9+8YtQFAXnnHMOTp48Wfa6hx56CMeOHcOmTZvw0Y9+VHru9a9/Pe644w587nOfw7e//e2qpGU1/P7v/z6Gh4fxmc98BldddZX0HFdaXnvttTP6bB8+ZopVbQpOJ2dX89NMSkuv0jLuaRMaMlAwqGfaNyf/tKlLcM6aFfZ7jCqehzPBbMrDgXKlkfu37PtGDum8rNrz8q88evQo3vGO23HDy2+Q0sNNEgLA2D2u5qtGOAZdPbsWYK8XlcGM2Jy/kfGGlSpyRSCiOdfFfXsfwpT5cvvx2k4FT59kz1cK43GXh7dFia0eFINuLl7LTspFaxQ8+hIbEA+OU1y8Rv48t9r1+pdvrrj6Kyot3WbxADu+dvCK4ZBZoldbpkBtb8aXzlqIhQjaa4y3dJNKihX7szwISi9Py8kSedcaZfs3nnGeC6iMLGoVCK2ZeFfWi0SEAOOQSEsl3AG9mISmJZAvGlDUBDo6y703qk1ieZmzqrA2MZqiNZWW9ardapWHiyXWbh9Guzx8HkKvvMDDDYanLLRE5Y7OrbTsSBAUhXAi8fhwywAvL1evbZWgKLJn2ViK4gt7i8jrFB9+SwgrSkFqjfJ8dF/LzAN2/ohCUUnOlJaOmch8qz6nC6MkAfQq053IUql9RIOVj2moykxDDMHzsgppdjDP0IXei8ZCVlqy/4sGa/XifeyZJ36Dr509hD/4gz+wBSeaGMSzbJWWzUVa9vT0AGcd0jJftGzicSrHxkNim1EpG+uGQiGEQiFpEXV8Im2LF3wsbVDU8PMNrwJAMZx30kvbgmmM5NnYsBA6H1h/xZzuo4/FhRmRlv/wD/+AL3zhC7jwwgvxpS99CV/+8pfxta99rex1qVQKmzZtwq233ur5ORdeeCEAzFjm/b3vfQ8PPPAArrvuOvz5n/+59JxlWTh06BB6enqwapXvdeBjftESZWWQXkRDvWgmpSVQXlrG0RIhGEl5HydO/t108RulgUtrFA0pweeYLWlZ6f2cEEPxZQDaMZmldihGJf/K4UmKZMur0bOO4A03aSCE2AnLgDPJraZuKiMtVVJGLATU+Zy6M1Aji2P9zwARtkj1w3seRMvLNgDaCqgKsKrN2aNKYTwi+RPRgHiE4O03aHjgKR2TpcCZUAC4ej37wSI55vZ29FK7/vbxI0DX+8q+tzUKidjzUloCjCDmpKVhMcWYO2wolaNQFeDshIXVbV5lu9S2AWCPvQm7jMckP1eUidhEezcK+o2l38A/UyB+S23lposDeKTfRCJCcOPFc5fRx8N42oXE6e61F+HsU0+AUgorZeHy667Gls1byt5bLXGZP6eprP8dTTH/tLGUhf5BC5etU+0QpXo+T0StIB6RKHYrLfm1aNHKPq1zCd5GRiYpLnANhQo6lVRRXQmCwXFqKyFFcIWg12LJdMrDFSL7fv72RROjpf7/4BETt13DPqxRSkv3ucvNc4K4qB4MB4nUN8+36nM6MExHxe1FuI9OyUS82xZBhNcCD0dMUlo2EVNTJ3SjtpJ7qUG8dnOlc8oXdibTDjv1xKP78fALX8RDDz1k+xtqrutjOcJoMp/T3t5e/L+nM3Z/Mj6Vt4nHM5Nsm3gPtXS2qlooFKCqKs47dw2Ol8Z2Oz7yMdzx+29v2hCebdu2LfQuzBssWoO0XPt2YPjnSBYE0jJcAKbY38u1f1jOmNHMZMOGDfj3f/93fOADH4Cmafjyl7/s+bq3ve1teNvb3lbxcx599FEAwNq1a6e9D4VCAR/5yEcAAJ/97GehuBiew4cPI5PJ4Nprr8WnPvUpfOtb38KRI0fQ3t6ON77xjfjEJz6BNWvWeH20Dx8Nwao2BUeGZi6vaabkwGpIRICRVPXXuA8FIYRNsCdmP9gPKLMPPfIimEVCLHhxHIGudlAKfOUbP4AevQiT1nUg2q/K/CvHAxfj6JCFo0PADRtVnL9KlSYBfMJfXWnp9onyNryf77Cnn9z3Y0yOBaGtZaRlrGUFClYACpi6TyQY6yEtQ0GCgEpw8+UB3Hx5AONpitNJCytaCLpKXoptwme61ZteateTA+MIl6yRN6xUcHSIjapElaVCKpdQhwIE2QK1UxHzxfKAiYmMU1rpRaIMT8rBKbkiK61zl5N6lVNm8iZ27fqM46kaWYnIdQ5p6Sb6OaHQFiP4izeF0BEnc1oeHgux49fd4nzH5dduwc2XMSJ//arfwWtedytSegjPnTJxTrdil5BWU1pypUYwwAhmjr//XgFFA7iox8Sfv1FmFOstP6yV7iuqROJhuTMQJ+EFY3qqxEaAl2N6lYjnXUrLRFjBRMjyTFTnCzNeBOW0lJZEVloeHnAu6DFh8apRno9ulex8p3ZnXZ6W4gJXfp5Vn9OBqFD1Urwl01TaXlVpWcXTUiwRzuQrv64ZkU6nsXvPPTg1lMKV58AzUXkpQrzGskW+jbWVF4+cArAOANAaU6C3t0v+hnJ5eHORufXCsCgsujj7hZkgHo8joFn22O0vPvxx/PF7fwfxeByFMbZRJKAKmXEkBwehqio2bdqEdRvOwfFn2HP/413vRrwOO52lio4Oj3TXJgWlNapYAnFYV30OY6eftDe1r9wIlBwJvaqKfDQ3ZkRa/tmf/dmsv/jpp5/Gnj17QAjB298+fQPV//qv/8Lx48exdetWvPKVryx7npeG79+/H4888gi2bNmCtWvX4uDBg/jP//xP/PjHP8YvfvELXHrppbP+LT58eGFVG8Gx4fIyj3INizemo15ZynCrn7zgdSy6WhqT0j5bP0uAqx4rE2IUWfB7828Om4icdz4AoP3KP8T4Y5+R/CtbO9cBJRL3+LDFSEthAukoLSvvj3uSqKmA5jqGAZVAVeb3pj86PAir6KyaKpEukJIXTSxMqhKMHDKBK7eN9jhBe1w+MG3RykSol9oVxDHSvOJcBRtWKnjhjIlbrnYOajXlEJ908Ql9Ou94W3KcHrPswZqbeNRNRmiK/pn5ImtduimraL2UlmeHJ3BSIGKLwdV222uNEeRc3xeyPVJZG/byLGwkFIUgHiZSQM94RsG7b2N2CqEAgaICP/5tET87ZGBVG8HfvCMEhVRPu+eKQS1AbDUn4BCTx4atMtK3XtLSsCjuf0LHgSMm3vkKrcwXVSSeY2VKS9mn0V0+PtcQ1WsjrhLxgi4HOcUjQCxIPNs3L9v0WqAJTGPxQ1FkpeXpUWf/RNKyUcnBhoukn++Jjnj8I0F5QcArNGs+4FZye0GcTLonlpkCRbYoKy1jVSwlqn1Xi1gevoyUlul0Gtu3b8djhyeAYCvuHj1Ylqi8VFDerwpKy1Ib5/1MKuc0GmJMMg/jkr8hII/HlqunpWkxv7/FuqAxXVBKpT7kLW/bhnhpnFbQKfK5PB458CwANh/fdM3lWH/Ze3H++eejt7cXf3e3AoA1hmYnspNJ5uW9HMhLSmsrijNmHPnEJgBs9SMR06AQHRatP0jRR/NgQWiR4eFh3H777TBNE+9973tx5ZVXTuv9lFJ89rOfBQB8/OMf93wND+HZtGkTjh49ir179+KnP/0pjh8/jm3btmFoaKhq6nmhUMDU1JT0r1BYhi7hPmaMkEZw+TpVUsRduFpFxGPS6uVf6ZOWDArxNvfviJOaCdr1oBHKJ6/zJxJixfRZe3v4nDfYfxciG/G6t38Qt9xyC7b1bsOOHTuQN5wPO1Ga0BeFiXakLk9Ll69eoLw8nL2u6s9qOHpWdYNmztiPzci5ICpjYeMhgtbpKi0DpKb3azhI7PCiSddndnR22GntAKDrOpRgq/18a5SVnn/87WGct6J6CA9HUJP3c9TD+kAcwOumHIaSztMyItM98ePIepAeeZ1KRKwadkji1kh5GWe4RCDx4zgfuSCJMAtQ4sdxVFLYUWQLFC+WFHhnJyjSOf5c5c+0lZYqJNKSo2iUlxPpprcR/OiUhZEp5yRlCxT3PmZgcJxiz690WK5xw/Ck81hsw4ArWGKeB9mWRSVCVdxPgCstnbL6YIAgGkJ1peUsy8NVIpdEi3skev5WOjfTgVjizDHfJWViMFZIw6Iof33hTO0KEFGR7SaQk6ny/ihapcy9anq4cK3mlhFp2dfXhwMHDqCtvQtd3SvQKSQqLzW427HYLnJFdj/i/YyiOYQs0dm8SgxWEf1P51sVvVjQbEE8psUIKg4pmDCVw65du/Cr3xywt50+fhh33nkn7rjjDuZ7Lyx6NGoxa7Fiz5492LNnz0LvxrzAomxBuBqmcrJ3cixE7HGjr7RcfpjnKSvzr3z961+P/v5+XHfddfjc5z437c948MEH8cILL+CGG27A5s2bPV/z6U9/Gh/84AfR2dmJ9nZn0haLxfClL30J+/fvx6FDh7B//35s2VLunfXpT38an/zkJ6VtH/3oR/FXf/VX097fpYLx8fGF3oWmgwJgVczC4TMmNqxQESUKcmkDI2M5/Pq3v8F4chztHe24/bZXYiovSxVaVAXJ8DzXErowX20ilzLKUl45NIUgmfTuqlTDxHi6fKlOJUAlu0uVsPVrzhOpRuXPrxfZlImRs1npnGoqhWUWkc+moFuDdmdLVFmKdaz4MvzprRdCUYBiMYtUNgS+nnRsSMfUZBoTkwoA9j5i5ZFM5pBKW5ia9J58ZmIqkoIkKj1lIZOiSCbl9pTJUqjG9Fiq2bSJra9/Nb73sy9jhFogRIEZ32ivnIXUInLpDOKhMNIFgmTKxNRkeYJ0KqUCYDNkU88gNVn5OABAIZeHagUAJDA6pWN4MIlwlF1r11z5Mvz2V7/EkaNH2KiaEKxcvwYl+ySoVhpTk+XtK0KVsmPJwcSrARQNirt/8D1svvGV0MLVk23ODAZssutM0sLwqInhjoDtfzg0YmIqbeHskIqOhHNeh0YpAJkRsBC0211A02BQh00IkixUSqT3BIiBZDKJ1KSJqUkL4Sq/rRKm2yaKWQupKRMdsRAGJxSMpSyMjycl8ms8HQZXmgwMTwDtFDmFINlZfq1SChQMFQCBQkyc054DQQiEAIkwxWSOffDA0AS6W+SO4exwQFL+DU1aeHHARE+7AnUVOw4nB0xYlF1/I1MUj784iQtXOe3iiaPONbuxM4VkUkg6sggA9jnDYxPQ5tGrLJ2nmJx0RvxTAI5GVbTFFFBKMTJqIF9k+x4MUExNJlHIUhBLbiMAQM0ikklWv5ua1CUycGoygLxroaRam1BMC/walt6TltuB+9xMFwWdYmpSnuVmCZDsrMKiNRjpnAJAgaZSZFLjgKmBD72HRiexIlL17Q3H8KSFYwMmVscCVf1VR6ecflUzFSQFhe6RUwamshSZrPNbjEIa2QxjmQq5vH0v7Ohox/nveSUSiYRnmyhmKAgJgFKCqaxhK42aHUeOHIFpmiCKAkMvIqCqME0TR44cWXLHYCJjoS3mdN65AmvzALMrGRlNYmjEwtSUhVC0FSgtQg2deh7m5BiuvfZa3HLLLUgmk8hnAd6mpjIFJJM5LDeMJU0oOgHRm0O5wEht5749NDKJZDsBpRQ/vvcBvPD8M0hsuM1+/tSxF/D1r5/BH//xHwMAqOG0p5HkJLrnuc+cT2QybOyw1PqAmSBboEiOmUi2Vp5/nRowMTEJ8PkPzCw0VUUOBPmiuSyO03JAvcrieSUtn3nmGbzpTW/CiRMnsGnTJuzduxfRaLT2G134zne+A4Alh1eCpmm44IILPJ+LRqN4zWteg2984xs4ePCgJ2n513/91/jQhz4kbeNJZs2M5SBJn290dABrV1Hbly0+ksL//qd/l8I/jj12N/7of30Z4YhDbnR2KujoWFjSEpifNrFyhYGpnDfLGNYIOjq8u6rziYWih2qkO6FgJOXNDrRFCSwK+/u6Wmd/nJPZFO760uelc7phwwZsvPASHD16FLo5Cvc4i1IThKgYnFDw3HALXnkR+415Iw+uPxpLK1BD7QiELPDyiI7WCDo6wlBCFC1T3svOK7tVdAi+P0qIAkGr7HdG49RW2k0HM20T0Xg7Pv7XH8HffiuDtBGVVI1tiRBaWhNoi+WQLgCTOYJf//Ygbt6yWbouVE0HLxXqao+jq0tDS8r7OORzefzbv30BU8FbobZfAgsBfPErffjIh/6EfWYr8LGP/42UHj6o3YhHj7D3r+pqQUtr+cRhZbd3m0mn0zg78BIQ2gCA4Ec/vh/PPXcYO3bskH6DG6GYio6O0qA8Z6Kl1UIoFrCDc4JjBlpUiliLio52Z3/UUBE8VZ5DCcZw8SWXs7aYK0LrjNn00Mmjz6PnPLmyIR7V0NERw4RuImVZaGub2fUwnTZhqBZGCiZWtBUwOGHBogSm2ob2Usm4ZVGkC87vMtUWtLSyfWprC5R5sRYNCkqZp0IkFMBl58Xwyd+1oAUIfnbIwM+fNso+hyOWCNi2BMm0hcGMiUQroEaEfieow55pA3jsRATXXRSy33NmnElj13URXLahA+3CtReLZMHb61e++k1csi48b951+qSFlla5f8wTdn7zRYpEqwHdZL8rpCno7uxAOAa0teQByDKnRCyEjg7Wi7W365JaeEVX+TkBKreJrvY8eH8mwqIEVGtDS+n4iedmJkjnKVpay/uGRGugIbYg9aBgpABQRIIELa0diEWd/isQTqCjY/4IVNOieH7YREsrRaK1OiFcIBZa0qztRIVrgVIKMmigRQOIWgTAXrOiqwVTOWr3ufxeCKuA4we/hd27d6O9vb2sTWgRirCWQq4IFE0VHR1tc/LbFxvOP/98qKoKi4SgBaMwTROqquL8889fcuNwXbHQ3krsihiTsjYPAEVTQWtbKyJpEy2EokjZ+EaBhe2/+3as6VSk9PCiYgFIsw9WgujomP4ccakjPmWgtU1Bh0dI31LEVJbC9jwCoIYT6OgIIl+kyOV0KGoQgXAL7J7azCKVcu4fLQnnfhGJtlScDzQDYrEYgOUxFw/mKeJps+r5VJIGlKAOPh7pbo8hHCyWUueVZXO/8MEwbz3igw8+iBtvvBEnTpzA1q1b8Ytf/EJSQNYLy7Jwzz33IBAI4B3veMeM94cnimezWc/nQ6EQWlpapH/NTlj6mDvEBbP5B/f+xPY67OzsRCIexmOP/goP/+pn0nuWSxAPgKrEWbUU9c4E8XT96UxU/rxEhEi+crNNDgeA+378ffmcJhI4eqQf11xzNbb1bsNrX3Wt9HpKTXRM7bUf//iAbpdCZl0lcidHLc8gnmpl7eXl4d6/c77Lw7UAS4S/dH2i7Ll4mCCfy2Ns4HBpC8F37n4Qu3btQj7nEFhyerh32TsH9xUNUEf5dvR0kiW6l8AT6rf1bsPWrVuRLToNTkwLF1HJ07Kvrw+ZKWflN97Sjv7+fun7vCCWefPSKbEd8NK6svJwD0/LvA7s2LED23q34eWveSeCq19lP/ez+76Hn++9R3p9pMSZcNXVfHQ7vA2LvpZiiXgqL5eTianvXiXieYH/4u28q0VBa5SgRZjzei2MiOnZYvm0GJ40lZUXQA6dtDBeKmU+dMJ57spz1TLyTiXODn/nez/Ezp07sX37dqTT6fIf0mB4eZ6OTFowTGqXdPLjGdaY32g05F3OK/Yf4m9UyPQDvcT7oRtuq4DZwKggwG5U2all0bJgKze4byXvM8Q+NzvPjkMnRiz7mNbyC5SCeIS/MwWnikFOD2f/i17OnZ2daImHq5Y9ix6nYp+X9KigaCb09vZi06ZNmEplMDY2jrGxMTtRealBN+W2ILatfJGVB/N+hrf5RFTBRz7yl/j93/99aQFHLg9fnuWfzVYe7u4jeRsoGI5Fj0mFE69n7Dk6IPeZjQpo87HwoBQwqziVmBZFJk8lW52YYCu0XD1vlzPmhbTs6+vDbbfdhqmpKbz//e/HvffeO2OVwWOPPYbh4WG86lWvQnd3d8XXfehDH8Lb3/52nDhxwvP5o0ePAphZcrkPH7PB2MigHP6hMvVCNnlcet1y8bQEqvthVfMsDAZIGbEUUFBVnZOIEMnXrxGelqNDJ8sCXazCOFKpNLZu3Yr3ved/SAOv1dFx/H93vhEXr2E/birHQigsSssmsidGLNk7rDQ5rMQTaCop86TTVHiWAs53eriqMB/Sc7vLT2o8TLBv/z5MjRyzt8U6zikj/STSMlS9fXBfUcV0CCKitSI55hCLukHxzEkTqRJJxYktQspDVTjCFdrrwMAAqOkQrFooCkqp9H1eEMNxeHAH/79oOCb2bhP6nAf5YpiAGgzhoqtfj+eNLYDK1HFK6jDiwQIGTh2RXu9Oo58PT0t+/LqExYWRKcHnyuU9OiWQll4DVdHbyE3Ei/3DpMcapTggTgqeioblnIO0K9WYUuDXh9mOPHXcaZBXrlfL+u1TJ5zjvWLlmnn1rsuWixlhUkbOFkpp9Pz3c98wTSWePsOasBAidiXVSowrIVHFLSEpkJaz9QB1h/Bw5BoUgKObwOhU5c+ilNrXKG/zkqflPAfxiOncXongIiRPS6HPFa9FibQs9SPucLOgaklBK26IafJie3Uv1jUb4vE4du/ejXe/53245dbbsHPnziUZwgMwUrISaambrJ1zX25OTEdDBEGvhVRhQXC5ElSGxY5ps0B3EVO8DRR0ii2bt2Djxo0oms6Nc+P5Pbj99tvtxyHh3rNciexmhEWrk/PpPNNri+c8GiQIl9pDQUfFXBIfzYk5p0XuuecevOc974FhGNi5cye+9KUvIRCYubznkUceAQC84hWvqPm6u+++G3fffXfZc2fPnsUDDzwAVVXxute9bsb74sPHTLB6ZZcc/lHIsHLiNQlJNVgrYKSZUC2NuZrSEmCp09JnBRlpV4mPY0pL57FblTgTrFuzCqCGFOhCC6zcGGBBQp1x53vOiw/i7rvvRnHylL0tlad2SrSIctKyutKyp52UqXQ1dfG0p2CAYL0nackmvFZuxN6mRFaUkX7isYgEq5POfBXfKjjvJ+E2+7wAwPce1vHve4v4f/cWYFFqk5aJcGVSt1Iabk9PD2A6DKRusnMvfp8XMiXyIld0CEqukhNVhO5JnJeSDmCk37/vLcAoqRdI5ii003ugaRqoLiv83Gn088FjayqBphJ0t3iTlhNZ+XdNSqQl+5tSam8XlXOa63pujYikZfnx4p+XKdAyY/e0i8gW8eDjKfyfz3wRhwfYSemMs2vPfZ0Vck5ZHBStLC13LlGpfZydsFDQWfvkrxDVle0eiz6y0tL5eyb9SrXwNTFBfLZG+5Um/o1SWhoW84ishHSe2ophfo9rEe5X8620NKl4HVV/rai0NExqTw7Fa0EkPvliWlm4WWl8w4NW3FCIc2wMk12PpkUxkaG2mrlZEY/H8Yatt2Jbby8+8IE/WJKEJcAUUWJAiptsnsiy+xoLnWPbokFS1lcDvtISKA+uWepw9zU8nKxosEqXHTt2YMPGS+zn/+7/+yvpWggK96blSmQ3IyitvLAIOIt64jmPh50gHgpfbbncMKfT2KGhIbz3ve+FaZr4m7/5G3ziE5+o+Z5sNosXXngBL7zwgufzBw8eBABcc801VT/ngx/8IABg586ddpI4AKRSKbzvfe+z/1+3bl29P8eHj4bgne94KzZu3IhUKoWxsTFMjo9i06ZNeM+732WHcRDMf+nuQiJSxXmhlpAn5kpDjgSZt5I7JRlgE+xoUE5QbkR5eG9vLy7eeK59TlOpKVxz+XnYstnxy33ZueyLtOIgfn73F3D//ffjhWcfs59P58pLwwHgxKiFY8POxJhPer3IJQJgbWd5t04IqXqM5xNaAFjbScr2PxZm5B4tjNrbzEBbGeknKoBYebi3RQAAexU/PzVkb+tafYF9XgyT4sBLTkr16BRFqmRd2FKhNByonIbb29uLjtaY/TiTK2Ljxo1SO/BCrkCZj6NAMvEE8ZwuEg1y+0gJrxcne8+dtmx1oDn5EsjRL4NQnREJpiw35NcCVwjOl/g2pEEiLUeFtG43ufjisbO2RQAnnAbHKYYm2HtE5Zz7ehbP45QHackHxEmPlHdO0IjnhdASGYMIjobeDj6MKgwdRCFfgOr+/pgjK7SUcFla7lwgnU7ji1+8C1/+6m7s3btXslcAGImQTFsSeSdadHiSlkL7EonKmVQEJFzXltgOxATx2U5Q3QofjkaljhomMJ6pXCKeEgg+vjjQIaiLc/NMyoiqlkrHhkP0LKXC6yXSUioPV6AQp8/l98LxsbNVy55ZebjzOJ1nZKVF2bFtZuhCuv1SLgfWTZl8cLctrkYWSfpY2HvBI6ASu09ZrunhpgXbLqgZ4FZ1i0pLgBGXHd2r7ee722PS60WlZbHJiew777wTd95550LvxryAUtb/WRXaum1hI5zzeJhIY5WcRzWJj+bFnNIin/nMZ5BMJhEIBHDkyBG8+93v9nzdjTfeiD/6oz8CADz66KO4+eabAXjLfo8dY6WDK1eurPrd7373u/Gzn/0MX//613H99dfjxhtvREdHB/bv34+xsTHcdNNN+Jd/+ZdZ/DofPmaG9pY4duzYYYd/nLcqiD/7n29CPB7HqqKFaJDinC7Fk3RrVkSqKC3VGqylu4SXl5rHQkQiGwB2wyOEIBKkUAibKGgN6AXj8Tg+/fd/i+/ey87pyu4W/NX7X4uDJx3C4q2bAtCHHsZ9P/g6Eok4NE1DQXFSeNN5ikyh/LdOZICDR0qBCCFg0wVsh73Igu5WpaIKMDqLFN5GQlOZGm5NJ8GpUXkwsmXzFjz82GFwilFXWstIP1HREbFVp8STOOCr+D/62WP/P3tvHifHVV+Ln1tL790z0z0zGo1WSxpZ3jdkjAHJGDuywQGMzSIpwYEkygPn5b2H2PL7vfCc3+/38pIQJS8vgQAPEgho9JKwg40HHEBmMfbIm7x7rF0aSbP3vtRyf3/crqp7q6uXWTSb6nw+tqa7q6truXWr7rnnew4OnmPvXXHtzXYozmvnTPvBCACGzpq2OitRRw2myrVKVnsfYjFsf/NN+EF1nmzHnXfhXbff0DCEB2CkQFETlXEljSmO+IGbu1yWJ0USEWKrFY9zJHcKRzCcnrADojZuvhxnuXXYHqnV9jQf5eEAu+Y7YsS+Dvky2/G0uKOnz2ewb9/XsHfvXlT0CEyT4tiIac+488eocXm4B2lZfSD28tDL2qQlt/zJh6Gu3gEiByAFHDXI8Es/xmf+6mc4detq7N7tBO1cfeUWPPUoW2YiXYBxgb3rcrkcdu/ejcGnnofc9XoQQnBo8FBNGNREjgqDgTBHxHcmajsXPrhGUFrOYNKnzaWOv2a9jEcOswt7XCgPrz1fpUrr4WH1VBylOSJDDJORa6Npit5k7TbxdgQhlbX1zjifsjw329EqKNfEm5Veu/1ANQOQJSr0UXxfHFSZ/YfV51rPN5etAvbc927EYjHPpFeJiIR5ruRMQix3pSV//JYyR2WYDslNqai6BIDxnImgSoRJ2UiQ1H32CiiMjLhYVVS6QWHSxfG8Nhdwk9hFDwUdfw9PRAjA9VVBX2m5LGH1eboJBDzGM95KS1E0wCYgl8+14qMxLihp+cMf/hAAoOs6Dhw40HBZi7RshtFRVjrY3t7ecDlCCL761a/i1ltvxRe+8AUcOnQIpmli8+bN+OM//mP80R/9ke2548PHfCKgOOEfALBxhYxYNS21NynhwulvFi+CKmzywo1m5Yde5eGAU67GwypLtJSYuRKdsxTZZHvEPqddCQmJhIyAotuDQ0II9OxpUKMCVWVBNApKdkZvtkRtDz22fG2J0N03qrZyjBDmD8mPy9d4qCwt1CMz5xusJIxidQfFqTHufZQRCofx8f/0+/hEvwkTEjp6NmHv+64VyBb+AdgqK5Ql8f1SsSQkgr/ljdtw8Jvss1zZOUaHT4hP0y+edl7XIy2DTW4bsYgKK+nwdVtvQijcGqtTKFPkXCRGviSqCHmiQTeoMAhs40jLY6POE/9vv3cHTl4Vto/FtjdvwycOOKWz1nViEbHzRVqGAuw3U3G23aNZahOrrxw9C8BRXsjBdgwdZt6mG95/J05PME9GzWCz9Lxyzl1yGA4wolwz6istzWo5qhusvFe8Lo2pV2CMPQVl031Q29YDAMxyGrlzz+G5CQWv/PxLeOihB21/ung0ACv59C1vfTvetOHmC5oe3t/fj8HBQSR7NkFJpaBpmu0La/VP/L5b4ImjVG1OlqBgZW2FHRNlBtLcSEjs7zevlPDYK0xdzCtevZRWk3mKldMgLX/xkg6TAm++TLbTjefKS9Lqc0YyJnqTrF8ZSZugFFjRLglK6FCADcZ5wnS+PS2npbR0Eb6azv7j37UmiiTCSG2l2g/zzzc3bFAQa6Ba5z0tARZ6ZandixpFsUIbppwvZfDE8FL2MDQMh4DlLScsTOYoejqI0I/GgvUn/yzScrZBXEsRelV9u5zKw90ktqWO4yfN+Ht4LAhUis7yfBXJcieyX3vtNQDApk2bFnhLLjysNl6v77PaBP8cEA8RwU7MK4zSx/LFnJCWX/nKV/CVr3yl5v3Dhw9Pe1233HJLQ2PVF198cVrru++++3DfffdNezt8+LhQcCuBLqYy8HoghEn+eRImFSPoTUpCWIcXAgrzx7MGULzS0g3eS81SYs5FEI/79yz1Z0gVH7J4vy9VVWGU0/ZnuaJIQq3vkoSy8PZAFqefGsAXJ3Sb9JAkAqO6323h2lCixYiAwkjFlw79CGj7Dfv9L33+f+Hje/8TItEwutpKOJ+mKJlRBEMi+8yXGlkEIq86LRVL2LdvH4aGhmwSbHDwEKSej8Kkjl8ipRTPnRSfll4+w5XhTzM53AI/AK80IQZ4FMqoUQbny1QgNvRq2ZgsEeRKYtvq4DxTz08531nbHcJla0WyKhosIlMdFFgKXNn2tJyfNmQdx84qaVnWmMoqHgayJdc2qHHb27RQpjY5a1KWNF5qoLQkhCARIRjPUqQ9vCk1nbUJL1EeC+MBMnnnQFeKaaj6GMYf+2+I9d2D4IobkHv1WyAEiMfCiClO0M6ePXuE7bn9be/Ab2+7sD4Nw8PDoJQiEIrCBAsFqxcGxSt3+Umebi+lJbcfvNLSXQ7fCqywMMsCYU2nhGSckRqTeWq3cTdpoRkU2SLFyo7WfufpYyb6f8F2MhxwVOpzprSsXt+TOVYinitRvHjagCITJONEIMlDKkEySoRU+sI8k5Z8G28exCO+1g1aU4pnkRFWG5dlAK5jW89KwwKrfOBtIigvssJkjiLsoWJdDtA5FnkpKy11k9rtxYtUmsoDPR1ieXisga9tsDqxudwJKi9YBM5SJrHdcFfBeCnoytW+JaiwiUe+qwlcREE8AwMDAC4O0tJsQlpaIXb8ZH0sLAaN5uf5HupjYbFIohl8+Lh4IEliWEMz5dbFAr58OagQXLNeQVdCstUxjcCXiFuKIbcCExBJS2uQPheelgCEgZf12251o9vvK58esT/LlZxBPABcvpprJJTi3BOfxYMPfh8PPPAAdu/ejVwuJ7QjnrRazFBllhJ+9sgTzptmBUeGXsKj1ZTwzqrHnWbUJj57KS35c3jw0YMYGhpCPB5HKpVCPB7Ha0NDCEqsxtdS1J2bokIpKiD649QLC2l2vfKkplth0Ai5EkXR5WlaKNOahHBrIJcvi4O6pMf5j4e9r4OwB8GuzLOnpZ0gLoTxsKdXQxI9rSAFQJQwkqkkpgqih2CmIBK7Xom0FplfKNeSNZpBhRAgN06P5HD4hSH7daWYRiabAaiOzEsHMHrwYygO/wqhUAiJeLQmaIfvA+ajHLi3txeEEPtBX9O0umFQWa7DOX7kZeRyLKSpPVrrOcsr0oX08Bk8RSoyU9gCQCrOJlusoDKTOteoScUBS6kyPcLx5LgzGnqGS3mv6LSuj9Z0YJfEglkyPH/StLf52HmzxtMyGRNtX+aKPG0VYnl442W9ysPdgVTWpIxFWronAFv15uYHoWcmxRHsxDL2tRTKw5cwScWnh3uRStkS2zl+UjYRrr8+6x67nNPj68E6jstJaemevLXJKEFpyf4NqrXhm3wQz8VIZC9XNFNali1ym7tPhgPiJNd8h9n5WFj4pKUPHwsAfuZwLtKrlwOE9NppEnCWypElkbL3Iq4EcSuEh/+ORNgAei7AB91Y2+NWmVh+Xzt37cQdd9yBd7/DURrmSlQodVidkrBlFeuitdMDiJIJdKU6kEo5Si7+4W6plNCpcjUlPD8MmFWWUM+DmjomJ5j9R72AFkB8aPVSWk6MM/9Gy/7DUpopNA+AkcO5XBHffOSlhttZb1DVXGnJkZYNkhHdmMjRmrK6XIliKl3EwMAADvQfwMDAACbTOfszflCX8rhmVrR53+J5VbC1vVZbmjdPy+rveiWIm3JtffL6vqs9A40yRSqQP16JtG0RcXk3RtL1z9O/fGsA+ZIz6upKxhAMBHHN1Vdj27ZtuO7aaxEOh5FKpSDBrAnaEUjLeVAF7Nq1C1u3bsVUOlcNBct6hkGViiX86ze/a79+8vFf2JMhTPkurree0nImSnVVBt73xgBu6pNx3y3sOk1xXo98GA9/vRcqdFrHkCdJXj5jCuEWhWka+HsFY/Al1KfGTeF6PzNhYoTru2JBgnhYDIFbzOXh7r7Ly17BmgCw2rjbKiCgkBYnHZ1lzk2Jv7GcfS21ZVIerhsOye1l6WBNBvKTso2C7gLV9uT2cL4YYCstl1Gzd0/elioUukGFsC+rFDikkprwzeBFpLS8mGCdSa97a0V3ql+sc05AEVIhPJt4hZf6WL7wC1N9+FgABBRn0OSXhzPwZI+XaqwRIjZJ6AySrLIzy0fJCuHhvzNXfpYAUyKpMoFhUpsc9fKR5P2+TEox8OVStcyVCgPpaAi4/44A/rn/e/jZ8e8gnkoBelFQct3AkQfNyvAWCwKKVSZPQc7/O2j3W0BGHwXMCro72wFYQSBsFDSapdjk2Bvag2lexcOIZ/a+uwTfUprFg45X2r7/9SUMB26B3MZeB8vHUQ6uF7aznqdls+PMP1BNR2npRXCeGy/iM38llro//8t/xb/88+eQL4cEQufwoZ8BeKPw/Z722n2QiVgKbIVbz7fS0iKc+dCXsQwrDc6Vape/5/0f9Aw0yhTcnpa13+XPZbpAazwbG5HLw6M5QK4y2NRAUCUIBALYvPlS7Ny1U7AjyJSnYE6IQTu8Mrc0D0mXsVgM+/fvx9/+40M4fq6EZCqJ7du21xy7g48exNnz4wi2s9fhABXK2iMuuw7R05L/e/oNRpGBS7olXNLtXCx8nz+epbalaalCEQtZXpTTG7Ty21+sAMdGTGzqYTuSLjjrbQX5EpCIiO+51Yg8KIDhCef3kzF2/+H7D7eK+kKDHxs2UrGZ1YAhHkz1XUtkAlx5uGuOpNV7En/vd4dlWWX30zlXSwV8v2NSiqUaKKGbzr54+VBa/bPbg7keLA/Dig77vnexwCJwlrJdgBs15eGaWBpOqRM4GAywajQeQhCPT1ouG5gNrBD4Z6UyZ0MiSS6lpZ8eflHBp0t8+FgAWGEkBHNXnrzUwZeIdUSnqbQM1a4DYMSkRVq6y30jgbknjCMBQDeJ/dDVrJRYIgSxEJApAmOTRVSmzgNYB4CpT2SJoLcrZJNwAZeSi1c8tZqou9BQFZYSfmjwEIaGvgf62ndBCMFll16CO3fchpIJdHE+pmOu0l2LCFQV2IMZvkTVWbdD9PX19WHDui4Mv8yWOTmqI3jZBvaidB7Z079EYNN64XfqKUGaBRrxNgezTbr8yc+cUneLgH3m8JPo7+/HZW/+EEoV52nviV8MIPI6N2lZq7SMhYhLacmWsYKd5mt8aJH8bqVlplAb5AAAJcP7YipqVPAC9bqmxQTx6W1ne7ITZKxarm6UoLvKrfm05PzkGVy99p1C0A6vzC3N04ArFovhHe+8B6PZ+vKtifEJQHI6TAUVoaw9HASQdZYX0sN5BfsM7l9eRGcq7iItq+CvoWKFqXMqOm2pQiHvKh174ZRIWq6qrZivi0KFIhYSB9R6E3UcT/J1VVXPfHuY7wE47xdvUqYU9ao08Novt5UG4JCWlhLKbRUQbPGexNu7eClpC+XlSVrqBvDvz+l46qiOj78jhFuvWprFb4ZBoRv11ZGW0pInLdsbkZbVa4SCtbGLaWLfmgihS1h564Z78rasUaE0XDMckjbs8XzFKy3n21LDx4VDI09L/lnJIrStfiDsKy0vWlxEtwIfPhYPrM5XbbF86mKA5bMXDZJpJ11bJIy7RDoaBEbBytTcydqSROqq6WaKcJAIM+TNSokBpnrLFIFCRUJ2agpKipGWeimDgYHHMXJ+BIm2BNJTadDSGMwpR8l1bNwhv5eK0lKVRaLHSrV+729uRygcQSltunwOG6t7AJE48Vr39m3b8fMhGQB7elZW/wZAWHuQc6/AyJ2q2c6ZKi272pzvzba00avUHZKKU2fOY5NBMT6VBRAHqIFkFHALFFd4KC1jYSIQ+HGuDF6Wa70MLyRCKoSgrbEsFZRW0aBDPHklf1uY4o6zl3qaJy0brccL27dtx/eOF0EBmFoeBY9ya0s93ZWQcNVakcXjk0/dSrULiWbWBMlUEkR2RpN6OcvI2GQSX/ziF5GZuBVAt/25KlxvzvGcmadl7Xs8aSkmiPNqScfjqhUiw+0R++JpA+/cyq6l9DS9EksVCs0gCHL7azQpsebVIt0J6x7l/fl8wK3e0gzvc+FVOu5W0JmU2gSLNTlXLhcwMOD0u7vetR2ARxS9CxGOkPQ6Jl4lx8sBuRLFd57QYJjA1x6t4NarlshNnAOlrIzTLg/3mKizbBD4SYRGE9P8tV3RL07S0lhGppbu/sSttBQ9C2u/z3taznYieLEjmZzGTNoygTdp6fxdtq0D2L/8OG8+LHd8LB5cRLcCHz4WD6yHsIvpYawZwtUHk+mWhgNsZl6RvEhLRsJcuUb2VCK2T1PR2QzuB65WiES9OAWgHUQOQI102Sqzv/zzP0V2ahKUUlBQtLe3Y9vW1+Pajfc66eGT7GkwqC4d8tsiP/gyeQDoaJPsB/bOOIFV8D3q8rS0yBheaVVTluhaNwC0R5ynXTmxwf6bjj0BWjhb/TViry/iEfJM0Fw9u76TK3euKt0opfj2EzrGsyZ2vinQsmrIs9RdDiC1Yi0Azsje1AA9V/N9d3m4RNg18YbNMp49bmBlB8HVHMk2n0pLgJH6QZUgEWbE/UjatNPdAZYqbSW6p4v115MWUprFz0rFEoZeOgzgKgDAeEbDdB59AqEgIFOAAomoirt37fQstwa8CTyxtK3ln501miXXb9+2Hf/+0uOwWk1u6iyuu+46DAwM4Omnn0bklj4oXQ5pyQcc8cS2+9prBV7VBUJ5eB1PS4vQKlZo3aAsHu7SsVNjjBRvixAUNYqyRlueICvrbPDNn0+tSQ0nrxaxJkH43/Mqpb1QoLS25Luie5MErdha8KXxQZUgl8vhkx/fixeO522F++FffQf/9tX/aauO64FXfnsNQudLoTzfmMxTe8Du5bW7FGCpci1iqqTVMhAWASEoLaP1Ow63Gnk5qmzrwWoPy7k8vKyJ90L++vbyZufHScu9PHznzp0LvQnzhoZKy0rtM4CdV9DkfuFj+WJp1iL48LHEYREuPmnpQJIIggqZdmm4hWiQ1AzAoiGCS3vluuRkI1+lmSASFEtvA0oLLlUc2USDnexfo4yRs8N2AnZbog2ZdAarVvVgz5499iDQIkncoRmLGWodNV9AITahqSrEPmd1y8M54qOVMCWvNqCPP4vc2BH0bVwnlKTHw94kcCvk8KqUZO/faDXg5eh5E48c1vH0MRMPP906c+VOm89ms7j8ymtxx9vfzRawynvNMoieFb6ryrWBVgGFIKgCKzsk/On7Qtj7myGh3FWW58/TEnDa7eqqCjpXAl4547AhazkC2O11x4Mf8PNkluU3+dMffdd+71eDz6NU9DDNrIOy5qRcrl6Zwo4dOzwJS8CbwOMHYe4BV6FMMZoxpxXY1Crq+S2WiiUMDAzg29/+NuKptfb7v7tzB3bs2IGnn34aqVQKQUVcgVpnkmAmQWZe6r5wgNhlwuMeSkvTdLxLWyF/KfUO7XnptLNfU9NQ3Va02sF3I09LQFQNWnYToXn2OLXgRYJodUhTy1fv/JSJ4yOmUFbufNf5O6gA/f39OHz4GfueFY/HcfjpQ+jv72+6bTFugsir/HO5qqsyeb6dL+CGzAKW2lg3aPWaq13GUVo6ftTt0frrFHyAvdrDMiaudMvTcjmXh+tUOIclQWnZuDx8qV4nPlzQczBPfRs4/S3oJ79TM+lutQnDpJwNCftXLA+/8JvqY/HAp0x8+FgAOErLi2cGuRVEgjNXP0aCpEZNGQuRhrP0c+0DGQkQwWeSEKYka6QUiYcJxqz7dZWEMjWW4OtOwB4bPS981/otLx+gxQxVJjUqo6AKgRDsTBBM5inyZUbuWLOr1qwrP7BpxVcvLNeOpuTRR3Hvvffgtttux1cfpRi1eD8tg1IRNeRUvAXFhyITpOIEoxmK0SwbyJ0ed/b12RMm7rmptXABr1L3u3ZshylHAJiQlRBgAKZWwvkzR5C4zvnuinYCibhJy8YKVUUSk6EvNKwBymWrJLx4mo3SDh1xmKA1qdbKuvOcgocv6z/4KPMEjXX0wBoDZovsfbcStx54dVCkSX/h1Q551VBFZ8SXVcI+nqUYOmeAALh8tYwVHh6kM4UXIcWHBlFKEbr2Oshxpq79yAffjc/+zf8DSimCwSCKhmj+yU8SJCIEV62VkQhP38oDYJ6WEqkl0pJx5kE8mae236JFWJU0x+u0FXWFZngPcF88beKmzezvdJ5iRVtr21zSKDRd3Nem5eFcv28l2EsSgSqz7avMIxnnRVrW+33dAMazJv7fb5RhUuAP7wzg8tVi4+ZLPkMBYHh4GNTQxHuWUbI9UhuBLw/nz22pWMLBRw8iNzmMa9ZC8IpdDpjiJlsaBSMtJpgm9fR1pdW/vZ51HKUl+zccbPzsG2oSvDKepehNLq1nnlZhTYQsL6Wl+LrsKg/Pc57UMY/5QJHEXkYHxgNPPPEEAODGG29c4C25gNBzwFP3g44AyK2CqZ0D9O8C138WUFj/XioVgPOPIZ/LA3grACAaZOeeV1qWfKXlRQVfaenDxwLAGrT6SksRPe3SjJQ7ACMoIwusOIwEUbMNzUrEN29YVfOeTMsIBoPQNPa0byVgr+juEper9uDNSpYXG7wSnkMqEYgRPqDlzChTh/XvP1AT/gC0VqJ6+MlfiG/kj6M49iLUADthx150Ph8/dxz79u2rUeS5lYv1sKLqa1nWmHrw7JQjmxjPUgxPtv6gZZW679y1Ezt27ICshu2HfKsEuKMtgg/sfg8kOL/jFcITVIjQ57gJSlma3/xa69rYwhEivO/ZinbJbhONlJb8oIgPeLA9QYkGUMsMNcFCaFoErxwKe1gG8PBWEDt/sxAZ53W2eh4pgKlpeiw2gmZQzzAji8S11HBymPUnYamEcJCFexFCWNiXLpKWPCEbDRJ0JaQZEZYWvMJ4uqtJ8pQ6XrYW8Viqo8ypB91wvsOsQ9j7L5+ZmdKyrNcqK5sF8fBKyljIudisNjGfA3Avizwv70rr/aPnTZs4GTpbu6M8yRZUq20HpnDPglFGb29v023jg3isY2YR7Af6D+B7PxjAAw88gN27dyOXq7XBWKrICqTlAm7INOC+9vhrQtO91cMWEW1NAEWDxPMZwELQNdHjxniOeqp/lwPs8vBlpLR0E/IVl9KSv7d7PWMJ7WGZKy0HBwcxODi40JtxYXH6W0DxbPUZxYRBCVA8y94HAD2H0oufA0Z/iqnRk/bXklHWGYjBbfO21T4WAXzS0oePBYCvtPRGd9vMj0dHjAgKgIWALNWWDzcb2HfEaxnHS9b2YPPmzUJZcF9fH975jrtqfg/wLqlZzPAi64OqSGbyYTxfOfADHOg/gIEfPWK/p0jOU30rSsv05Bio5gx41fFfgFKKifEJHHz0IEZOPOusjxYxNDSEg48eFNbBh4U0Qnebc2sdyZg46yIpD59oItFqgIpOkS+zQZs1oOvubMMff+oTiHDEiNvPErCUls5rxXW9yPL8eqNaSufeDuZr6UZ7lNgWDo083/jys3Zu0GN5gupaxS4/kgJtyAW34BNfK+J7g81HQAVuJr/ZdeY14cJf/7oBITWVJy3Sc+hpV8+TkA92olIIUJj8T6VZyBJTsm3duhXj4+PIZ0aE73r5UM4GXuvjg6Me/tlTONB/AD946GGkM1mhDKwVss8wHYIlFABWJR0LAmsQnS8xRWczmCZFRac1JF+z7zrbSfEXf/an+OIXv4hcLme3iXlVWnqQIO5y90yB4uh5A6fHTWRL4vtu8GRVWCXYtWsXrr3mKuGe9bobrsGuXbuabltQcSYnrLAqgWDvWolkKoXBwcGWys2XCvjrf6mUvbpVzgYnCWQTBbXfKVVYcJOttAx4B6ZZaKa0TBdqr8XlAn05elrWpIeLZBPfv6RitbSE4Gm5RMh9Hw1QOgeAwKSsD9BNGQCpvg9oJ74NvTwFgGKi4gS5dZLXAABRPohnifSbPuYGvs7Lh48FgHUTDvpXoAAv9U2rWKxm7c2Ull7b3RZV8AGPBOx4TDSCkpegpyVgDVicB1WJMAKfH4jwHpPjoZsQWz0JtXQcFneRnhwFkODW1xjJVBLmqaOQk1eDFIdhTD7H0pJTSUyMT8CYehXQ0oDaBiX/CqhREhR5kQBpmRzmyZexDMX5KZExeO6EgTuvm5k81hrM8McqqDLiNhwgyFXVe15Ky4DKrjFFYoMjt9JSkebZ07J6CAgh2LJKxhOvOTslS2xGvS1CMJZlA15Np4K3ogVeydEWIUjnWaLt9m3bcWjwEIaGhhAsTUJW20DUOF7MtKGsAz96Vscd1yk1k0fHRkycGjNxY5+MIkeWeYUz8fBS/PJEvGY4RLNhUqH03CLQZqo051FvQM8HO8lxRwHXHtYhS0A4FsP+/fvR39+PgSPr8AJHXH3/u9/CtydfRG9v75yU6XodK55o/+Wh16CdehiEELz483/Gf//rLwNgtYOtlITphqPmCapEsB1JFyi6EgS0+nezyQhroMyTfHodNSsPK72cagV88YtfACEEDz74INRrvsbWO49Ky2bl4fkyxaGjzht8yabXhAEf9BQMALFYDP/7i/+Av/1nds/q6mzH/7Xn1pbaCakqYbWio9TjCXYACIbjoHSspXLzpQDdoIKqvHIBfG0vBNzKJl5trBnU89osaRSlinPHjwZJw0kQIYjHRVIVK2wCQTeWZ6WSNRGyrEhLo/Z1WTdt+xpeaZn06ItVGXYw41KxUfDRAKEeUErxP1/YheO5Xnz0iq/jio7jQKgHAFDKjcM645PlhP217gCbSOXtRPzy8IsLy7DL9+Fj8UORCWTiXSbrY3mhmdIy5qEwiwQJQuFgje+ee6BvvV56npbia4s04t+/ZIVke79JoU6Y638blfKo/bmhOeWrrZSHb9+2HU8c+jscf3kQ+tTLIHoOfX192L5tOw4+ehCEViC9/JdQQ+3QC6OglSn0djkD7lZLwwFgJUe+HB8xkXElXx8fpZjK01ml1/OEQ7haWs+T1ys8lJZBOwCMQK9QuPkxeZ49LRWZQJUJNIPistWSQFq2RZjqMxFxls8UvQkmflAUrXrb5stU8AT9+ZkgJgwARLIHwiYFvvvDX0DPnrYnBkwpiL97qIySxtJ9efV3M09Lr3bI2xjonI9hrgSB9KJg5eIzDSLjUS9ghSdxpUAAln3YlZtX2qFesVgMe/bsgfrTMl74jsOqfP4f/h5Gesgm3vbv3z8r4tJLHc0T7cHEaiRSKWiahqeeeRTf+f4A3vTWdwJgRAnvDeoFjVN9hVQISt50nqKrOhZipGXjbbUUk7xiyCvx1I1MvgIgAGKWsGrVKpTLZQwODmLd5gyAxIJ7WvLtxG1PwKsAsx6kJf9da/IhEY/Z96x4iCAWa+0BRyJMdZ0pOuFJPMGuqirKGpvcaKXcfClAN0S/3FYS2xcDNIMK1x7v66rX8ZEtVsR9jYZIw+qIgKC0FD+zCK7lqrS0jqe5jFhLr6A5TXdsjfjno06P+zshzE6goi8dRbKPBlj9bjz/0qt45OxNAIBHzr4et248C6xmAZMlZSUA5t8/ySktu9pYp8FPaix3j1MfIvzycB8+FggBhfjl4RcBmvlNeoW78EbTPLxIS9LCbyw2uMn6IGeXYO15RK7gjV1PIlB2PG1o0PH0jIScldQjLTf1yE7CejiEj3/0P+K9v9GHO976RuzctRN79+5FKByyU7pz6QlMnDuCbDaLKy+/FPe993Z7XclpkJY9Hc4GPceVglPT+fvpI60nWBcrFC+cMgSVgegpx46B9cAfUoHuRO322rYU1fbi2Z7muUuy2sKWVeIo1ioL54N16vla8uRPJCiWcVueoJf3rfb87sM/fQoPP/wwDvQfwL59+/DKiYxNdj350hgyOWeUFG6iaPYaiAueloZz3ryIoEyh5q26oLR+aXO9Ab1F4u7ctRNbrr7Zfr83Faix1oi5+qD2RBSrVq1Cao7KdL0UpYwgru5TeAWAaqCLpOLcyJSwbLPk7bJG7RLmkErQFvVuR2cmKM5ONmYgLR81wb+vBdJEM9kFRkxG/gaDQVBKYRplex3zRU5Y5eGUshLw0YwpXDeTLtIyJygta9fHk2zW9cZPOk3H71QmzrVVqrBttPpkq9x8Ml3A1q1bWyo3XwrQTZHIq+hYEj6NhlmfvNcMbxJBM4CxKWcCZPz8SRQL+bq/IaZFi+tLV9tpK7YOSxEXQ3k4IN6zBaVlnecs6z7qKy2XAZQYMms/ab9MK5eJITydbwMCSQAEU2UnKa973TUAxOewVvytfSwf+DovHz4WCG5/OR/LE6FmSksP0jJapwzVi2QKqfPrQzgXcJP1Ae4YqQpBJlu0U45N04QW7UN047sQaN9kL3fVpU6AkVepmUSAVUmCWEjGs8cNUDgElhteKd333bMd61fGcDbLyMLpKOB6OcXYBDc2UzLPwGi/AQDw6LPjeMvVtURaoUwxPGFiwwrJJpL+YaCC186ZuHGTjN95C3tiq7hIA1kieNdWFdGgjqvWyZ5l1BaRwI4/9WhPZF6DeABGWBfKjKTs7SB2SFF71QmhLcKTTd7rsAYyhDDC1suSIRHx3rNQ+1oE6QlomoZXXn0FZ/d/H1h9DwBgJG3iJwcfA2JvAFB/MsGCl72Fm7Qs20pLL9Ky9QFZvsz214sodQfG8LCugbFHKzj+CltwRVst6+8+XgFVAsoO8TbbMl2vazagEETkEgpGGDTQxRKJNQ1EDqEtuUJYtlihiIfrnw+eaAsFgIjiXDCPP/UyruhlfclA9Zpf2RXHf/it29DbVSu7tAZGYnl44/0zTAqz+ohNq6FG5XIZhDCbiXTRWXcz24G5gEWCHD5h4gs/rkCWgL/8bSeq1620zHFzKrki89Dl7zNCenj1euNV2tOxvmHXLeuTKJiayt0n9/XehY/8zjuWTXp4WaMouIj3ir74JyCNqlrcarP8NaEZVAhKIcQJgPravzwItL8dAPDKC09h9+5PYf/+/Z6/wR8Dd3m4FZ7V7PpbqsgUKD47UIYsATf1Kbbv81KG1wQPu2eL5eFBFXX7dOuZZakEVs0UGzduXOhNmBfoCAFgN8GCvAZQHOurkhkBNv4HYOwxTL7cY7/f3c7KJfhrYj4tVnwsPHzKxIePBUJA9ZWWFwOaDUKiQcevx3nPu13Upj0TBJeYnyVQS1jwJJMqiyEMqqqiXD6HiSf/DL1XvxntG9+KZNda7Nrm1A0TwuwWePFFIsyIvGSMoG8l8OrZxqMcN6HZnpAhSQSrkgQTOW9lWN11BQnao8CUS0wipZ+BEd0EqG04X2jDgz/8Md56y5sRCjPywDQp9n2vjLNTFDuuVfDOrSpMk+LIeSa/eJVL8eUf3q2y8N6kjF1vrl9A4fbS9SLB5zvLyhqMAMBlq2UMT7Ids8jKiMqRTU++gMt6NtvHC2Apw5PpHIAoJBgoFQsIB7ia8ip48tOyHQAAEl4BTDJFX6VcQV6PwKokloLtmJjUoFZ5kpmUhysygUQYacQ8LesrLacTxpMvUUZCexBeXuV4bljp3IBoZ2Ah7rKtqJTyCBGHeJttmW698tC1K8J4eRiAHMRk1gC0LK689vXYeuNNGBgYsCcVet+zHd1tIsFomBSZIkVQIcgUnGtFlUwMfP/fgOR7AABPP38Ef/kiSyo9evSoTcj98vFn8Rd/uhdXXhLzHBjxPo5GEykUX8aol7M4c+YMCCHYunUroj2dOHfMWXczMnwuYFbZo+dPsp0wTOBItT/Jl2mNgokn1XWTlfjy5CrfxiylpcKTltMg3ySpVj0TCoh98pqUhFhsjtOgFhBlTVRaWu8tdtJSN0XCqVih+OXLOno6CNakJEFpGQ876vHzkxUE2tnfQVnD4GNMrX3vvffW/EY9paVmOD7AjZTOboJ9KeHxIR0vn2HX5c9e1HDHtUvwAc8FL7sS/vnFmqxrC5O6zx/Ws8tyD+K54447FnoT5gX89ev2yS1VAMghYMVbMKmVAFDEQs79h58Qa1Zx4WN5wSctffhYIMRCzZfxsfQRUGoJNR6SRBANicqWeoPYmrRnaen5WQIe5eGC0tIjhCEYRCAQwGXrO7DzfZcCqA2akWUCg3s45j0oV6ckvHbOmFbJlbWNvUkJkjS92VyZAJ1xCVN5V9lp8TxK559CaPVbAEnBNweewTNPPmGXqY9mKM5Osd96ddgAoCJbctQqmQKFaVJIEhFIhghXnllvMEfAlYdXB4VuZaAiz6+nJSAO0q9aK+Hfn2N/97RLMLQSfvzQt4AO5nX05LNDGH/5+7j//vvx+BOPY+T8CAYPDaKy6WOQwlHolQJ27/4I/vbzXwfgdLCqTLCuy9mxu1+v4l9/VQFAYAZSAABN00AphRx1ZvZBJEhRRw0bnkEQD/t9NtiyPC0pZQnwblR0FmTRiromV6LVc+Xh8dnCwG4sy34/GgTiHirUhEvxMjk+ArM8aRNvsy3TVeocq1UpBS8Psx24cftvYmO3gTfffCP+5m8/i6GhIZuQePaxH+IbX/kLQXmXLlA8c5xdAMMTzrU3MXYWp469gHCVtAxEUnjp6ZcAAJ2dnVBVFZqmYWhoCP/2g4Oo3HUHbr7U6aTKHuXhzZRefMryxnU9eMMf/IEdYvRfvk4AGNXlgI7Gq5oTWOXh56a48KcKsxiYzNW2RbcSOF0QyVW+jVnVBIQ4IV/TKQ+XiGjpUKzU+v0utzLAsu6kaVsoaRSJede6Tw+mKRJODz6lY//PNSgycOUaRSCV2sLEJqSk2Fr7fcXMNFRr8xOxvHIzU3DCrxpNzCwFxWo98DYN6fz0njsWK7yeSax2UtGpfW0nIqRuGKf17FLPr9nH0gLfJtyWEsXqa9OkdgVAR9RpG5JEEKh6nPqelhcXfNLSh48FwmJNu/Yx94iFSEMVVSxEhEFivfLwWqXl0ksOB2rLw/mZ04BCakIYNE0DIQTxeMxWW122Cthz37tt0kKRxcFUR1Ry/QaxH4ZagRU0EFAIVient3+yBHQlCF4757xH9QLOnx4COsKMtAQQ7r4aQ68cwMFHD2LHjh04M8ENWKoKFT6516Ss7LU9Kg7mrDbQKJBIVRwbAWsA4NWe5lugwqtqNvfKeP8bVaQLFDdtlvHLnz2E08dfRLhKWgZi3Xjlhe/i0//t08ikMyiVSiiVSujZzEaohGoYHBzE97/zb7jizb9tr7crQaAZEv7obQFUdODqdTIeeVbDRB6ggS6Mj4+DEIIVPStQ4UlLQCQtZ6C0BFjb5EnLfLm+Z1m62DppWW97Kk0INd2g9uA4FSeeBGIiIr75x5/8GMbOn5yz9HC3ctlSo/IBUpdffwtuuULBwMCAoLzWNA3Pv/Qa+vv7sWfPHnt5vsSZv9b1ch5mecp+TYLtMAyDBTxUJ0ZUVQWlFBPjEyhpjFS2FO9l3Qri4VVfjfePJ9ku3bQGD3zoAft1OFDglpsnT0vKyPJzUw6ZWyiz/Zhy2RKYpphsDTBl8EqOXXWnh1uQJQLdpNNTWhLxPualnvEqA8zlcujv78fw8PCctcv5QkUDCq7k26VQ+qqbVGj7x0ZYe9IN4PS4KZyntgjBqXH2Wm6/1H7fmHq1oVqbvyfw1wfvfdho0qBYWZqkpW5QYbJjuYTOeJaHaxSlYgkP/fvjAG4EAMSCZt1J0+BFUh7+8MMPA1j+ikv+Xlp09YPF6oRupug8J3XEiNA2LNJyubcHHyJ80tKHjwWCT1pePIiHG5OW8TARFDBe4TxArTpJlpoTKYsRbi/XgKs8nE85tpRVGzZswFNPPW2Xcz40+RwO/uibdooxf2wUCWhzVQgHVaA4jUEAv43ukJJmsEhLHsmIho6NG3DsxBGAmgCRgPhmmygBgDOcOixTYD5ybp9DK3XcnR4O1C+5de+P9bdXe5r38nDX4HLb5c6GTo2fhVl0EuMR6kSlXMH5c+fR3d0NgJUrQ7JISx2UUoyeO21/RZUJOmIEw5Ni2E9Ph4SJvAmihHHrb7wTPV1RvO51r8effFNsnER22JRIsyCeOgOugMLSzDWDPaw38q7MFChWtInvFcoUiiyS/bmS4/vphqVCopTiu4M6hs4aeN8bA1jbyTZwIkdt9W5XQvIkWxOu8vA9v//BObUz4dtqQCGIBBh51sOltVsEm1t5raoqIIdrlFo8qVHmiK9YRGVhOEYZkIMw5RhkmW2Ae2IkmWIzFJM5jrSs9hsUTmp5s/TwEjcQc09C8XYY86UgpJS1GZ6MLFYYCeD2syxUHHW3hYzr/qXzQWBcu5BlAHrtxFQjSBKpUVq64SZwcrkcdu/ejcHBQfseMRep9vOFsu4Mzi0sBdWQ4VJa8vuQKVBBacmrZUnVY4PqBWTOvWSrtSuVWoa6nqclb6nRiLRkx3HpPRexRHnn9XSeVxYzvNSRuUIF+/7xr3HkvInQ1Yy0HHrxSZSKrwNQ6ytsPSdoxtIu/2+GI0eOLPQmzAtEpaVzTksValel8RUAHVFxcvVi8Tj1IcJPD/fhY4GwFMkmHzNDvRAQC26rgK42b/WTexZakpamokCViTCg4Qe9qiymHN9xxx3YuWsnrr/+Ohw9ehTxeBypVAodHW1CijFPgrRHpZqHWq9gmobbOIspPUkCOl2kZVwt4OY3vAFBxQCKjFSjoR6QQJtNlPCkpeUj5/Y+tAiGClceZ3n9NPLd5NWsVumml0fqfPdKjcLIVq3oADErgJYBAFA1ZT/cqqoKWZFBCAGR2QGgBvNbXL2qx1bKtkcIAh5kLq/oe8Nb34kdO3bAVBMA8WZ+VblxGwoopC65bbVN3WTljeMe5bgWvAjNyTzFBPedik5R1mndEkmrdPfUGMWPntVxbITiH/+9Yi/P+1l2JohnuwmqRDg39QjZmYJfXzTo+Pj2dDgfnK9O5PDKa4ARjZIaRVfPGntZN8HPk4GbN65DX18fzEqaLavEcdlll+Gyyy6z06mz2Sz6+vqwfdt2AM6AyTRFv0eLLMlk8xgYGMCB/gMYGBhAqcj5e7h+3z1ByStp3b6GFwomBc5OiUxrscL6E7efpZffataVIM4rLXkS1jqv01Zacst7kZYVnQpJ6/39/RgcHEQqlZrTVPv5glcQz1JQ1ulu0pI7V7kyFfYhFqxlFjsCOdz/4d9vSC4H6nha8gRmI6VzyaP9LAXopqi+XS4hI17n6pnnXsTQ0BAiCSdgbXLkBL73nW97roO/F1ntr1gR+wQfSwd8myhrFHr11sS3f94qgZWHO9/x0+QvTvhKSx8+fPi4wGiUcgvUDmrboxJUpVYB4+YWlCXqaQkAPW0SpvLsyYUfsFoDFncwzoH+A6LPZUAVfLFiIWITO15J39NJs5WIUx4+E8gSQVtQHIG+9twvUQkdw4YNG3Bq/AWoEebxtWrLLXj9ja/HwMAAXj11AwBHPpcu1KryrAc5QWkZZE9zjYgl3mPOeuBzK+yYp+X8tqdgAyLwPe9+BwZ+/hJOFM5DbksAahzdvWuRmRiBpmmIx+LI5fIgEtsho1KwFTwvnWMPxu1R4kk2ssRs1v7OT1FsXgmMpOs/AIcDQDJGBPKQRyOPYut4W4SX+7rmkSlSW81nYSpPQQjQ085e50tWubL3OqwBwdPHnZHBSIbiC996ESntMNKBKwD0AQA648RTaSlXw1EqelWBO8ftgidKo0Fi+yXGQgTRIFMEnpsyUSqWoFUqCIfDGBsbgxpQIREJfX19eOud77XXkS2KvsH8gD8WUbF3717893+bxHgZIEoE//mjH0dAIXY6dTKVxPZt2+2Qp8m8CUol5kHKbbemA0Ylh4994o/x0rFJm0Q/NHjI9qYFROKkhrTk+rv5Ii0NEzg36VJUlilG0rWS0bxXsr2LyOTbHt+3yBK7T02n/1RkcRLXS31KwdqiVUY+PDwMSimCQTZhMVep9vOFXInWqAXLS2AAbpru0k7ns1xRvO5+/egPgbY7he9ff1k3dr7rPQ1DlYIuJbJlA/DkCRXtyS5s37YdeiRc9/tL1f/UMEWl5VIgsevBMKntQeilhstkiqCUQgp1wOqBaCWN8+dLtQvDHc7E2oh1fMJL0CLpYoegtKxUPZdlsf1P5px7U0esHml5YbfTx+KCT1r68OHDxwVGNOgEFHiBJzUlwpRHoQARyA2J1BIHhJAl6WkJMDXpq2cZwcfvVz2Fo9vnslwqCr5Ym3pkyBLz2OJDeCxMJxhiNoQlwM7Vy8/9CsDN9nshpHH06FHce+892Gh24Zcj1WXbt+DT/+3TSGdLiN68TVhPpkBryAJbackNaKyy5VbLw1WZFc9JbjXqAoTzNlJktSWi+JP/+kn89wNncbKaxP4f/ui/4jv9f29bBwTDTilZb08n9n+WKXhCAQPZEkVHjHju1wquDPl8VYF2vgFpGQkSrGyXMJHzlvg0svtQq6VM1oN6oxAJCla+zW9fukAFD0wrtKue2kivrv+Z4+ICL4z3onjoC1BXx6CuqpKWCeLZbpSq9US6QBuqYWcK/jejISKU3ve0Szhy3kS6AHzmr/8OR159AWY1SSYaieLtb38bbrvtdhQMh7hIu8j9ksvzNRQOYf2qFMaPsmNSpkEkwpIwMcJDNx1fWfF9im/09+OFF19GW/cmIcTH8qZ1/37U1TbEUmjPn59zUArBz5L9NkXWg6DMevAG7skTra7Skkxb/a/KtUE8XrBSxQGgt7cXhBCUy2UEg8E5S7WfD1BKPScuyksgCdddHs6XtBfKpvDZuRMvIXS1SFpmz78AVXl9w98IcffqXL6C3bt/G4ODhyD3bLcnCP7k/9oLrzJioH77WezQDSoozZaCXUA9lDTHFsNLDReKJkAIgUEcHx+qZbCSU8/z4J/fytX0+orOKhf8qrWlB37io6yzZ6KgSoRJPF5pmYoRoXoqyD1TLWe7AB8i/PJwHz58+JgHNFJb8oRHNMRIM7evZb1UxaUKVSZIxaWaAW49gmT7tu3o6+uzyzknxsdqUowv6ZZx7XrZk0CazkB6NqXhAFMb5SZHQLWcs06D+fJlszm8863XgFDGaoyW2nHyxEnoamfNetJFioyrLNMKzfBKD29MWnL+YoSpD93LLwRpKUuOFUKpWBJKbsulPNrjYVx7uROGU6RRwTrg3vfutD9b3dttlxyGA6yNxUJ1SEsufd4iK3nVmbsFhQME0RCpSdW20Ii0tMrT9eoDdjOMZ0WT+pLGSnitsK6crbSsXZdmMILz3JRpl1dbIHIIsS27ocYcYqcr4a20lCSCm/pkEAA7rp17DwqxPJwIxF4PV7p/bDiHeDyOzs5OdHZ2olgsQg0EEAqHkC44pc3uMBl+wG+RILzPbSvJvJN5s0b9pulM5Qcie4b42L/PkQ9xlygsvEDl4edc7aEeYepODgdqS8Z54j3AKy3lWp/aZlBkIhCf7hAgC7yKb9euXdi6dSvGx8dx5swZjI+Pz0mq/XyAhXF5kJZLQGlpuIJ4+NCkoiaeI7PE+RFXQfKnG96nALEq4oVXjmBwcBDJrh6kUinE43EMDQ3hx4/8pO73l6pCUV9GSku+HXhNrq27pA99fX3Q4JQorO1px/vee4/n+rzKwyv60j5GFzP4NkEpUKhOlNUrD+9MiA8p1j2G0uaheD6WD3ylpQ8fPnzMAxJhItyEefClpZEgI5PcJKc7GGM5oKedYHhCPCb1VI6Wz6VVznnt2rfivt96X40vVjLmPRc3HbXYbJVlksSUoebpU5A7LgP0LLTCmB308ctfPgp9Mgk5eSWkYAfU+BogVKsQGk9ryBTE/ZnyKA+PVtuP0oDYrgk/UmpDdxZqtjqgEuSmiti3b58QvvTiz7+CP/3MP6E74TAao2mK0EbHOmA8a+Lh/8NGejwxHVIJ2qsklSQRyEQsH26LOCVmIzZp6Sywrpvg+IjoG6rKrJzarX4FHE9Gz/3jjr1hNiaX2T5R+xjwCsLxLEUsRBzS0uNh3So55VWWa9RXcLKwCkSNwWy72n6fwER7hNRtN2+/IYA3X67gxk1z/6goKi3ZdR9QmHqG9xsl4RVQjSyAWnKQAhjLUPQmSY3Skh/MWu2ijfMWdi/vhckcrZks0oyqyk9SoGkalGAMlfBGqNE4aHStvRwfohEPidcwryqdL6Wl6UoOB2rTqy14kZbpPPvuaMbEC6dMHDvvrMvtadnI8sELqgx0c8riI+e8SxIEn9BYDPv371+S6eFlj+Rw6/3FDNNkEyKmwbwEJYkIkwOlihjEQ8uTwvdpJY2eNeGmk2M86Z3NV5iiPhSHAacPGB2fqvv9papQNAwx2Ggpe1rybdlLaWlCwd69e/Hn3xjHSJWw+o8fvg/xuPf1y/cx1nGp6LUWC0sdW7duXehNmBe4rW2yJRPdkFAUysPZeSZgz108Ai67gAtRDeJj8cE/zT58+PAxD2iktORVldEgUz5FggSqTGxFi3umcTkgFSPIFNwPI/WX530u37RFmVZCbaNlu+ISRrPOQHm25eEyYcrQx576R5wZPgd99EnQbMYO+vj2t78NffI85OSVAIBg11WQIytq1vPTnx9CtPd1wnsOackTas2Vlu7y+IBS62m5UAgqwMFHD2JoaAjxeNwuuX3yiYP40cM/wMqr320vy4fIACJ5y5cVhgMAOJsAVSEwuEEgIQQr2ghOjlGMZZmPpEVaRoLA+i4Jx0cMbn1sMqGrTcLREZFUIahNiOYR5gjNfLk22d4NzWAK27aI6H85kTOxtpPYKi0v0tIaDDxzzPlwS9cUXvvprxC89IPCshGlBEmK1m0HEmH7fSFE3orMQp8CCrGvt2iQnc8eTgUrRVZCm3jJM+EbYCRae5TUDIw9lZZRnrRsvo3pAkUk6G5vFLt27cK//OQUTshvhZ68BkSSEQDwixFg6uEy3nmjKigtE67zLSgt56mUNV+imMqL7xXrKBqncrVGYcOjWYxNAn/23Vpyjd8fRZ5+v6Iq7P7W3UYwkqY4et5EoUztfs2Cm8SJxWLYs2fP9H6sCssncSEIz7JOPY/9YifbeHubig6oihi8U6wAleo+BGRgzcZenOK+H6LjuGX79qb3V570DoRizAZAZwNWqw/oSKYE30QLltJcN2jDYLrFiLImkr7lJezXV2qitKzo7HlOjaSAEoUiAcm2UF1fbl7NzSstlxtuvPHGhd6EeYHuClDKFtmkCN9uLJFHIiKef0CcoC5rtGlugI/lAZ+09OHDh495QL2yUgCIcZ9FAk5IRTwMTOQYKdKVWH43ZUkiWJ0Sn1JbLVGeLpFSrzxckYD13W7ScnrrdkOuJqB/6j9/qKoM3YRk6kY76COZSsJM/8xePrruNujlbM16pvImylkNgMq9x1R4gtKySpg1IgvcZHBQ8S4LXggEFIKJ8QkhaElVZVBqYGzkLK7m2v6Ii7TkB0SC0jIgeuspMgAX2bKiTcLJMQOUAmcnnITu7jaCroQT1AOwYyxLLCQmEhS9l8LB+snhANDNbf9kjgqKv3oYz5poi8gCaZkuUPZwT619pzV+TppBMZEzcXKMLbQmRXDnrTfhxad/iSPPfgbqut+E3L4FAHDZGsamyXXauywD0Kd/rbUKRSa2ShhgJfaTeYokRzYnOtdg/EQWlFIENr4XiSuuwfrLU/bn7mR1C3zZqqXSaQvzpGVzgoiiliTXDUaW3XLPx7H/F7Xfef6UiRdPlwVfXXffH+YI7vlKOj4xWqterOf9d+zkCIBu9sKsAFIAOgnhez99CWXtMmHZq9ZKWJXkg3im36+oVQL7yjUSfpI2YFLg5TMmrt8gNsy5UiLmcjns3r0bg4OD9vXz4IMPNky0nkvUU1oudtLS4JqQ5SVXdvlbWq8DKrDvLz6N9/6t8/ktWzcgFA41Lw/n+u3unjXYunUrDr0wDImOgxBiT/7pRu09z1LetaJoX2xw+5wuF6WlV2CcNclk9cOJCPMslOr0HW6Siq2DKch9LD3UKi0pChUn9E43KDLVicWOKKkhs/mJjaUavOVj+vBJSx8+fPiYB4QCTvmjG10JJzF3XZdkP2wnwiytuC1CpqUqXEpwKwAJERWm9TCTgbFEANcEL1a0S4gGGTFsfTTbUhO5SiK5E9AtbN+2HYODh3Aq/Srkts2QoyshR1cCAKiWB1FZgjgJJqGZItuqGcz3SkgPryqdGpGt7vT0oLqIlJZqbdCSVmZBS6t6OhFSCdoiTB3nTjvmfR35ErKwKgZXeftaOp8/d9IhKLsTEjpdkwS8Z2V3guD4KPX8zAu8f+ZEjmJ9d8PFAQBjWYpVSTGYwaTA6QnX/htie9UM4NnjzjLXrJcRCquctcLLMKNldK29BjdfxhjDeuoWS6h0oWwDFEksq7f+TnCk7toNV+A3Lt2J4dEiBou3Qgdw8GUJl1bzGkwKHPcg5PgBfyjgpbRsbbDr7ocskjxXVgCwi/D6DTI2rJDwyGEdU3mm9OJ9Sd0qkOgCBPGcHKs9RoWKd4hBjiPkSXkENLwaRFJxKu1IRu95vYIr18q4YYMsfF+WZtZ/KjLB5Wtk/OR5doB/9OuTeOXXvxZS3eeK1Ovv78fg4CBSqZQd4jM4OIj+/v4ZKzeng4pOBe9CC6VFHsRjCEpLCreLQKni3JcCCsHKzigAx9d540rmi9BsUpB/JtCpjP379+Pv/ukHOHq2IrQHzaidjLRIS6/PFjvSLtuRpawkFJWWXv6tzB/VCpWz1Oj1xLEhIYiH/VvRad1wy6WKAwcOAAB27tzZZMmlDbf6NlcS/Z3TBWo/j7uTwwHxWa9UMeFHtFwc8ElLHz58+JgnJMIEY9naB7iAQrD3HUGcGjNxzXrZvkFbg92uZVga3giq0thcm2BmREpQISi6Br6rkhIkiSCkOp+psySI66kFLITCIXxs715880fP2CniFkjxJCBvAKQgpOgqz+9P5qlAfoft8nDv7VblWiVgUCWLJnExqBBs37YdhwYP2Z6WMArYunUr3vfeu/HyeXYNpAsmciWmELOI2nrl4e79tRK8efA+er96xWlwK9pIjbKZJya7EpJAlMUalIYDYrDMeM4E0FwClCtRzzRzN2lb0V2kpQ68ds7Zl6vXsd+qR6AD9UO+LAXmhVNaukjL6jGOBGFPMOTLBDvu2oHXzhkY/D5jddwBQ14TQaU58LT0gqVuzXEJ27dcLmPTShlv2iLj+4d0/PtzItvwvW98Db/7gXudkChun+dLaXlq3Pkd69hSyhRRoYC4LJUiTGRMDZDyGGiYBWFNmSvtZW7sUxAPk5o+R5FrJ6JagaoAfT0SAjJQMYATk2G89PgACAEODR7C3r17EVDmxth5eHiY+SQGg6CQ2L+UsoCleUBZ8w5gmi8Ce6YwXOXhbqXuudEp6FICAOuT4iFxMnBdlwSC+vcpC25VXSwWwzvvfk+NJ6uXn6FeJciWotdhjdJyiZKW7koQL6Xla0dO4Pu50wBuAsCejQlq79sWRE9L51/DPQu9xDExMeH5vmbQWdsWLSa4QwTzJSomh3PVEx1RAll2P786fy/2ftPH3OHiGgn78OHDxwLCnSLLo6ddwtZNzKfRUj5ZXpfLsTS8EZopdWaqEHSvtz1CbDKKH7jPujy8he0LhUPY/c6bcNkqcWEtfQJmOQ0AIHUG6Sw12XkdrpIE7t/d0isjHiKexzMSqH1voRBQnKAlKxX8dz/429i/fz+SbYzo4a+B0TSv4nDWw3vrueF1TjeukO33+ZCs7jaCVIwICeK8Wi4eJkKpcTOlJe/ROOExaVEPXiW97jFarRJQJNR4YrYe6pVRWgE9zUj4mUKRIfgWWjYHEiF2X2mFHmU4D8rRDAsCaQRL6UPgqIxDqtMHzJS01A0r5bfWU9bUyoilf4JLlKcBys4dNcr4i//+J9i9ezdyOaY64/ua4jyVtp0ad9rS6lRjpSdR4wAAquVQzjkJ0GWTbXgqTuzrwc0xyJJIMLSKgMwmFjrUcbYNgTZ0rLrCTos++OhBVPTm570V9Pb2ghCCUkUHVRMol8sghKC3tzYM7UKgrNM6QTyLm4DhCaKKDoxOiCap45M55KoxwAGFnU/rOrbaTCs+k4rktCuLuPM6Nl4KPs0uD1/cx9IL7j6pssjbQz3opkgaW+eEfw45NzKB7z30M/t1W6SxrQQ/EWIRXprBlJbNqnKWAxa7Cnu6qFVaUuFexD+PtXuVh6uN72E+lid80tKHDx8+5gkr26WWZkutB/tQgJEnoQZkzHJEs2PUyD+wEdxm3quSzi0wwh3j2ZbiT4dU/c2t4gj/pmvXoT1auwI+vGXgJ4/j/KiTzGqRIDz5pMoEvUlGhF++upaVWkxtykqLtdSAO3ftxD13vwuxWMwe6PDk20jGIWB4lZ1bMcbDi7TsiBF86NZADcly5MVfw9DKQjmx25dwbZdzjqJNSMuVHc7nXv6L9dDKYMytYtF0h1BT5Nbacr32eqHtA9yelopMbLWsVSKeKzGDfn5Ar5vNj6OltAyqjiqbEGKrLWestNTZgDzvIi1LxRL27duHA/0H8OTD/wvjg3+FyvjzUM9+F6lku11+DIiK4PnyMTxdJS0DitjvudVylFLkq6XLbVEZmy9ZCTfWc23frdJlbW7622fd82KmE91ixi6tSYyfC/+yXbt2YevWrZjMUZwbmcT4+Di2bt2KXbt2zX7lLSCdLeK1Y7WqzsXuzcYTURWd4nsP/VT4XFIjMKsq8oBCIEsE77pRxZpOgntuqnoVt9A2CCH2craqzkOt5620rP/ZYkcNablElZa6Uet/CgAqivZ7gVAUkTYnfLAt0tgXmre3Keus/Vm89Fx53S5mzFdg23zBTVoWyk2Ulg3Kw5fbsfFRH355uA8fPnzME0IBgr6VEl483fiJmr9Bb+xZYm7yc4BAk12eabkq/+ArEQi+hXzJZisDq0aYzvat75Jw9ToJh0+wp/zffOu1+N6gjqeOiW1kddJJPH5x6AzkZBJyvA0ScQg5fjY6xpFBXsmKM1FDXSgEPYg16xxIEvM47W5zdk5QWnp4e3qhHhF+zXoZ/+VO4DPfHIWudsEsT+Ghb34FQ88eROqqj9qJy27SsishIRI0UdFow98FgM6446c6HdKyFbgf/itVz1OgdTVtM9LyQpWHR4O15yUWIihptHq8abVE3FFcWnjoJ4egFo4IHnc8LGWW29euLUIwmmGqjopOpz1BoRkUhgnBkzASBH767wcxNDSEeJypFNOjz2Bi7FnQRBgpV/mxUOo4DyqRik5xrlpSv6KNCOpW94CvrDttqqczgTdceh2O/UxkBdZ3iR6WPCKBmdlOWH3Yhs4yjlT5PCO2GfTsj4XE+LJWmyo+XcRiMezfvx9/+48PYnz0HLasCc9bengul8P/+//9BY6rd0BJrRA+my+rgJmCFy9qBnBu3BUeJ4dBJNa4revuzZepuGmzc0NVW5wICSiMjLImpbxKjD1JS9PZvqWGbNF9LS7u9lAPhumU6QOOMlIyiwCY9JZKAcjBDljcZiJSq6bjwffTZU2sNClWaNNqh6WO5ae0FNt2rsw8li3wSktPT8sAP/F3QTbRxyKET1r68OHDxzyip13CaIZiNFPfQZxXzC33hzEvNCMNZ1quyiuAYiEiqITCAe/lZoLpKtR2vzmAaFDDmk4J3W2SbUrPw8geB7AeABBO9MAIMnJEhm6TBJJEIBPAoM3bzWLxswS8jzd/DQRVsTz88WePInNmDJ1rrkeFOl8ONyDpKuU8BgYOYmJ8AvE4Iyey2RySqSS0SgWZwe8h1vs6BLTzUGNhDA0N4bINZwEwpVnSg89Y1ynh7GTzJABVJuiIEYxnvZOuZwO3N5RmOKWnrZI7dYN4LjBp2R6tXXE0CIxlRZI4U6Q1KqRHH38J2pmfgBBiex7yxKU1yAu51NVuX8vpWm/ophWkUvW/rapZJ8YnQCmFqqqQFRZOY1ITulZGmYrlx2EhiOfCExO5EoUVstsWIQKZXXQFwuS5dJVYmHhOeKzr5pWW4mcz8bMEnD7/zltvwk+/MgZdScEMr0EpuBl9PRls37YdwNwNUGOxGN565z1ojxBsWTV/E4P79/fj1deOIXJtbYey2MvD3UrLaNsKgK8Qlx1zX2siSpVFArFVv+igQpAFtX0LvUvBa99zPC0X97H0gntixouoXQrQDBaiYpgUskTsdhNUCTKFEogcAogKQ3IedNrC9ZPD2Xedv8u6eN+7GJSWi31CY7pwt+1cUbzG+WuhPUpqFP1CtcIyOzY+6sMnLX348OFjnnFpr4RskdYtDVwsqc4LhWYD35n6kfPr5ckLwCESCJqXpzcDIQ55yCMeIsi6I1fBlJC/vd1hEhKR2t+XS2dhkZZGrA9Q2wAAtHASX/ziN2ylkCwTGDr1JBsWKxSZqSz4JFD+HAQUIB5w2JXzxSTOn10Fcg4gcJ5+65W853I5/NEffgTPnTRhmibyBTbSjkajkIiEcDgM0ygjUHgFACBVS1KTxksoJHqxOkWwaWXtRbmijaCsNT/OkgQkq6RlsSIGCc0WbkVRsUztQVwrpKVE6tst2KTlBeqPvNqotc38Z9kiRcZFWgYTq5EopaBpmu15aAUNUUrtctJapaXzNyMtp7/dxQo4YhgoFUs4e/YsSqUSJiYnkEgkkMvlUC6VUSzmYOTF8mOBtJyHATc/qFcVUVXuVlpmOT/UeKhWYSwRYE3KaRBz1TYs4jwUDuGum1L4ziGAEAmhLR/CXbdKNiE9V8ReWWPE83wrzk8Nj7DEdiUC954s9uAVdxDP1de9Hj+qk11kWX64/XLr+ee6YV23FZ3WJaW81JSOp2Vrv+OGadIZ28/MBrrh2DJYWMrl4da/suSck+7ODkyczMMEYFAZBg3BuvzaIqThc10jpeV8WWzMB+qF5bkDJJc63BMO7knJNOdhHQ97KS2dv31Py4sHF/nQ2IcPHz7mHwGFYOsmGV3x2i5YIvXTfC8WdMbFEBQ3Zkrq8gSGmxi0VHqzTQ634CaMQqpY4twIbkIVALriBqhZHb1VCUsAmHz1QTzwwAN20Ic1KFxqCl13ma6gtFQIfv2rR2GWpwAARI2BELYA/6gbrUPS9ff34+mnnkA8HocaUGEaJkzDhKqqiMfjmJqaglbRoGns+GqaBkII1nYr+NP3hfD7twUR8JAjShLB2s7m51QizJvWwvg0wniagR+4UyoqOVspD2/U11ifXShRrtfkgHWNJlyKSPeghoS62TpcnocAG+xbysKGSsv8zM5DqULt8vCwCuzbtw/PPPsMdEPH1OQUzpw5g4AawNp1a3Hvu9+FBx54APv37+fSw8V1XWjwg3pVJoIi2T3gy3EKl2ioVmnZ20GEyZ+5mmDj+93brw3hjZda17eEf3rU8eScK1WVVX443yqtrhWrQQgBlUI1ny32Mkd3EI+O+h2MpbR0k5SthtxZ94OKXr9M+kJ4Wi7UOdCM2kT5yhIuDwfYJKRhOt6ToYCMZHsUABCMxLFmw+X2dxKR1pWWFR0u0nKutnzhsWnTJmzatKnm/WVXHu4i5POutm9NUloWMu5+hLd6Wk6ktY/G8ElLHz58+FgAqDLBVetkIdQA8AlLgCki2zxKRy3MlEThibE212BclgiCCpl1crgF92A/FiJIxVvb8JBc+xT+0uEnoJhiWis1NHRiCKlUyg76UKvJq9FgzSoWNQIuxZPqKg+fGJ8ALY0Iy5iVtPC6XhDP8PAwqKFBVVUY3GjW0A2oqgpVVdHe3o5sNovx8XFks1n09fXZJanu7eHRiipHIkCSO/dzWSLOl8nly6wU2EIrSstGVgwWITWfPZJ1jSbCznvZIq0pnTQDKQAOwWx5HgLiIDYzNYID/QcwMDCAUrGEiOqMlh5/6mWUipy0sEVkio7Sp1JKY2hoCG2JNqxZvQbtHe1QZAXXXXcd/vIv/hJ/+IcfwZ49ewS/RH4QNh8Dbp6YCygupadrsMi3n3iIIB4S+1u+NBxghPxcgL++CCF4/5tUXLOe/VZFBw6+yA74XA1QraCH+VazveNd96Cvrw9Urnr7aTn7s8VeHi6Eq+gUuQaXTtBWWtafjGoEyzKkogOVOteIVwm4XR4+Q6VlSaOgdP7PQ0WvtYoolHTkcrk631i8sG6xhiFeXyrX90hKCCTUxf4mQDzc+NmXJ6ncRHb5IigPruhUmDRY6nCrpPl7FKXO/d5S+rufv/hnG788/OKBXx7uw4cPHwuIzriE46POE3YjM/KLCSvaJEzlveUSMyV2rYFQUPFOZA8H5k5V5lY6xkLsvYBCmioohl58EsANzht6AceODmHVOopJ7uFOzr0EySxA5YI+bpTZ7ywmz8pWwJQ5vCLM+SygMFLKfPko5LbNAAAy/AOUjv8YG9/6Jzhb7IQiA1t6vS+e3t5eEKpD0zTI3KhZVmRomgZJkvD2t78NaiCAifEJz3CXVgfbXrDKwy3MKWnJXSK5EhXM7CMtENeNQq8utKelF6xrlCf9p/IUuaJ7wQ6MT2ZAqF5DMPPkz+kTR3DklYdBCMHjv34c5cAaoPP9AICnnz+Kfa98t8YPsxkEpaxesL0sASDZkcS4OY6VK1ciFA7V7c9DKpAz5sfTkif6FFlU4BbcSkve0zLESPlYCMhWj3/tJNvcbKObPJclgt1vDuDZ44wZs475XJG8ltKSBSvReZssVAJR/Kf//FF84gB7nYgQZKv7tNjLgXnSkgKeVicWbNLS1T5atV6xvm/S+tdIY6XlzK4rrRpENVtf6+n/LkWuKDKtmgHs3r1bUGkvBVjH3jApoHMBh7KlfKeoaLDDwVYlmWdho74k6PIwXK5Ky89+9rMAgPvvv99+zzQpdNMpt18OcCst+XtUSXP6QqviIqgSYfKCrxZYTuffR2P4pKUPHz58LCDcD8fyxRcW7onuNoKhs05i6fouh9ydeRAPS3H28owEmApgrvz73aRltPo6GSP2w3o9FDPnhdfEyINSWlVadjgfjD8JwES57AR9KDJZVMngrcJ9HfDlogEF2L5tO5449L9w4mgWRu4kaOY19PX14b+8uxOnpwJojxJ0Jrwbxq5du/Dggw/i6dMZ5llWffLXNA1Znakqb7vt9obElVsxNB1IpJa0TBcovv24hnVdEm65Qp4xycw//GeLVCgxbElp2WC/HE/L+WMtAwqzhuC9FM9M0Br/PwB48213Y02XUkMw84MYVTIRSzHvy5deeglSeBxtVdIyEO3C0AuiH2Yr4EnLaHWCQNOYktet/Kw3yAyqBLlSfb++uURJUFoSwdPSTQjx6jmrD4uHiZ1svM5FWs6Vp6VXO4wGGcmqG47n2VyoEQtl0U+6rLVG8M8FyjqFKYUAsAO9qjuOl8+w+9piL3N0qxfzxfrbG5pleThv6+BWWVvw9LS0SpNnWB5umIwwmW/SsqIDI+NZAHH7PSIpGDz0FPr7+7Fnz5753aBpoKxRgVTUufJwk1OtqgpBKMBeU/t/sC1WGhFyAVc5MD/xW9HpgnmRzges46kZtR7NSxUV14M2fx/k/autZ/VwAKhwPpd8/3AxBDH5YPBJSx8+fPhYQLgfjmej6FpOUGWCZEzCWNZEd5uE9V0SToyaoJid8iugEE/PSIA9GM1VGEIs5H7NfjMVIzg31fi7K1Ix0Lzh+DZqGRBC0BahGM2yZSSzgNzZQ0hnzoLmnKCP0+ml52cJ1HoPupWWoXAIH//oH+HgowcxMa4imdpqk1Wbq+Eq9fi3WCyG/fv348+++BOMjk3VpIe7SS8vzEpp6UFafvPXGg4dMfDEawbaIgTXb5jeD+RLFJoBKM4YF9mSi7RsIeyn0eBckedXZWkhoBCBtLT8DN24/uY7PI8bT/5IYCMaVVVhGAbM4rizYLC9xg+zFWQ4RdSGNT0w+/owNDTEAlYIEZSf9QbiYbV2Wy8UKoKnJVyelvXLw2PVc7CuU8LwhIGOKMHKDpetxhy1Dy8yixCCtjDBeM4JYtJNpo6cTViamwQra7Qlgn+mODVmQjcp1nVKKGviMefb+aJXWrqIhly5ftsNVtuYW2k53fJwAPjcF76KttSKmn7ai7S0yEpthuXhhmlZbsxvx6cZQEmv7SwoUTE8XCftaJEgX3aRlpyvKD8XF1C8w/LWVidCGt1rAi6Sir9WKNhzW7gFD+eliNmGSy1GNFJapoti/6jKtenhkQb3MB/LFz5p6cOHDx8LCEkSk5OVZTpbPBOsaCNIFwg2r5QgSQTREFMnzWagHFRqE3EthAME3pqu6UORCSIBgkKFba/1kNURY0qyRr9yy/bt+MFXszBlRq7pxUn09W3ClZeuwWuH2DJvvDyC5Pr3QZt6DVvWROz0cCVnLKnkcAtRF2eouDwtAZYsXE8R1ygFG2DE5Tt+8201YQetYjZep5LEzruFc1MmxjLOdvzLryq4tDdkq3GbYSpP8cC/lqAbwCfeFcSbtjCVSb6EaZeHN/K0lEjjQKwLhYDCtl2W2ECNT9Xt7SAYnmTH7nzaBCCemFKxhMcefw7AlQAAU2PyDE3TIMsyAAPQc4ASA1XaavwwW0GB2554VMHevXurZHqttUA9hS4bvFOUNNhk54WCoDxVRDK74EosFsvD2XL33KTikm4JfSulmsHjXFUG1Lu+EhFGWubLDllZ1mZ3PboHue6JqkJ5bknMc1MmsiWKkTS7u/DHPMKpSet5Ny4WuEmTRgonJ4jHPRnV2nFViMNIPjjwMxCYODR4SLByaORpacxQaambFBV9/nu9ik5hegQbSUoIvb29874900G+BCS56nXrWdYwnUA0oDph4qEUbEVpKQav1La9kkYFr97lBIvgY217eexjI09LXmnZFoFn5RBPfi/2yR4fcweftPThw4ePBUZAIdCrA6nl4lkzF+hMEEiSZIdzxKuk5WxKEkMBIoR88AgHiKd6Y6aIhRlpGeU8JgMKS+StV/IGMHKut4vidFUAtnlDDz72no9hvBDAj59jI97brwmiM7EDm3reJiRYK1KtynMpgFeHSkT0LQ228KTSiiddQAYKTZdyUCqWbDJqaF0Ju3fvmpG3mETYYD0RBjJFYHhCPPfZIvDNX2v4wC2tSUVePG3YD+rPnzSgGYCpU2jG3JaHK/L8loZbCKoEpMSukylXwnffSgnDk+wiHUmLn5WKJezbtw9HJ9sQ3MJIy/TUKKTR85BkFZdddhkAYLg0CSkWA1UT2NS3WfDDbAV51zFuRKbX68+t0ChK2aDrQpb98QNCVSYIqkwBRT38ArOcd2gsxJSUkSDBmy4TL0Lr2jj4b4ewalWvPWkyU0gSgUxQY89RDRsGwAazqThBWaOzUpPXEB7cMaCUYjw7c9JyLGMiFXf6+2KF2t6PVrspVMT2o1ZJy3op2YsFbtKykUrYIhrc5HKrSsuzZ04AWAcA6OheDaM4iaEhZuVw2+2/AYD1+bpBbWLU5JKqZ+ppaZWHzzfKOoVOazuBa66/Ebt27Zr/DZoG3MnPdhiS4ZwPgPU9bjWkLAG9yWo4T4uelmWN1pzf5VwirNnHc4E3ZA7hPn98X5LhHtISEVJThQO4PE4Xua2Gj7mDT1r68OHDxwKDf7D3y8MdyBJBV8J5OImHCc5OzY7YTcVJXSImHJxbU+9YEBhBbbl2MtaYtASA9qiE0xNslHjllvXobA8Cson/visEWXJIOveuJCK1pTRLAQHFCSlyE2mSxEqEtAYD0VZ2ma23tQdciwAbGhoCNXU8eP7neOihB2cUimBtWzJee94lwnxbfz1k4HWbDFy+unkHwBN52SIrE7fISkHF1QIH2qg8XJbmLphqOrC2KREGpvLiZ5t6JBx8kY3emNLSwcFHD2JoaAjRdbfZZ1miZWzZtAY3vOEtNjn5l98cw7kiQIiM3/vwf2kphIdXQ/Jq1mgTNWu9IB5R7SiWV841+EGdqrCy60iAKViLriAeq22FA6yPCQeJoL4E+GvjVWjDPwUhBA8+OLNrg4eqEBiuASivis8UgFR89n20e5DLKy3zZcxYjQ04StXO6n1rNFO7LvfEQkBh52GxK4amo7S0lFDue3UjZTePSinrvJBCUFUVlFIcHwE++pUSknGCP747CN1wnpn4CUeDzkzBbBiARuafBJnKe9d4/PXf/N2iDuGhlAokPOCoXA1TJNpUBQi5drI3Sez7faMqI0FpWak9Vss5Qdout19O5eGNlJbcM1JbmHhO6PHvLWfC2ocIX9Pjw4cPHwuMABc64qeH14dF/s2GSEnF639ZlcmclgVaZdpu0pIvFa4H3nezLUzsUt+AIpKSbpK7nl/nUoClEPUi7psFI7RSptrqgBlwCLB4PI5Usg2pVAqDg4Po7+9vfSVVSBIrs07GxIs7GgTec7Pz9P29QQ2UNh98TXLp45kiZcmzJYu0nKbSshlp2XQNcw+HtKz99c6EZPuDnp+iwvGaGJ8ApRRyIGK/p9AyrrzsEuzYsQOhcIh5oF7SbX9e1JvX0J+fMvEn/6eMv/5+Gfo01az1lZYcackRh4Y594NvfkBvTZBZpZS80nIyR+1U7dUpiakyPdqHfW3Eoli1atWsrg0eXiXffH82V2E8JRdRy5dlZ4t0VsodwwROcR6sI+lapsE9sWDd/yuLXGnpJk0alodb6eHcBJRMWn++ScS4GRcpZAdcnaFXQzPYtX/0vClsk3v7ZlI1oS+Q0jKd9z73fF+2GKF52BrwYUgVnrSUa32W+SqRRkrLAEdSFT3a3XIh9Hbu3ImdO3cK7/EeocsFXqSldS93B/F4lf0LQTyLvN/0MXfwh8c+fPjwscDgiQO/PLw+4mFGosxGSdjMU6uZcmo6sMhKt19jItzcK5APbumIkbqhKu5DsRRVlhaiVQLIi0hrVj7bilXadHzwMhPnQCmFqqogVEcwGASldMahCO4wHgC4ep2MbZfJWJ1i758cozg20nz0NSkoLdnA0Ep3dpeeNkOz9PCFCuIBnORQHm0Rgu42i3AT/S6TqSQIITAo14CMAlavaBfW0RF11jtZhyzg8fOXdEzkKF47Z+KVYXN6pGWdNlcvSOD81AUgLV3p4QBTlQOMQLMGi0fOOyPJTT0SFEmcULNgkcNq9YKa7bVhweu6F0jLahufrdLSTXryg95cic5q/brJ2lSuRFGqUE9Fvbv9WH3bYlda5vNFDAwM4ED/AQwMDKBUqd9XWcphfgIqHiEtKx+vunyz/Xc6m0c2m8X6LTdhrNxuv18oW6E5DO5wj5mQPAtVHp4per8/V8GAFwqaUUu2G5wykK+O8CoP50nLxunhjcuBlwuhl0wmkUyKHssaV26/XKC52gyFc92lBU/L5krLxe4F7GPu4JeH+/Dhw8cCg1eR1Qtu8OGUK15IImUuAzGCKit5distZS5UqB7ecKmCl86YaI+yIKJ6IS3LieS2lKmqxwlmwQ71j1crSstWry0CYF1PFIQQaJqGADFQLpdBCJlxKIIkEaRcpOX1G2QQQvCWKxV87SB78v7ZCwY2rGi8M5Pu8nAdyJbYa3fIRzM0InJliczKP3amsPpDd6AUAZu46G4jePkMe+/8lIlYD9uJ7du249DgIRzXCawxzdreTux+3zvx0nlnPbzSuRXS8vS4s8xYlk6rBL9eySNPdvLrOztpoqe9voXFTFD0Ulqq7HoyOU/NI+ccEmrjCgmK7K1wtshhrVKCAsz62rCgeNg38MS1pcDhSQtmJ9F6v13RaY1vJk9SZqtk40xhlVCfHjftSRg3hLCsgNO3LWbSMpfL4S8/U7XLqJZdx2+6GlDaPZe3FLp8/9I+jSqARDQAgB2orTe+ARtX3Iix0DaMvOosM/j087j3dZeiIxYHUKtSnknasm5SmHOs2uN9N+shXfD+0coi9+vTdEZOGia1J0ztMCRTJJJVxQlosrCOV1o2OERiebjHdiwT0nJighmZ88SlHcSzTNSkgDfJXNLYfcia6FEkZlPSzNNytsp7H0sHy2i448OHDx9LE0J5uO9p2RDx0IUlLecanXHiqWZra5Lw3R4l+OhvBvGhWwOMrK1DjiwEqXShYJG7C620DKoE77/nLvT19SGbzWL0/DDGx8exdevWGYciuJWWkSCwZRU7ea/bINul8U8dNWrCZ9xwl4fny9RWuxTLzgN/K/vbrGR+NinNM4WttHRdI7Gw5XPrNHreMzAUDmHv3r3YtPkK+73/+OEPYkUqJiibBaVlrvFIkFKKMxPOMhNZKgRPNEt8rzepwJOdhbKz/kJFVJrMBdzp4YCjtAQcX8sj59l2EAJcskKCqngrLbdv246+vj5k0lM4c+bMrK8Ne9taLA/nlTXHR0xP38h68Cpp1nTHZiBXZKTmTEu1LZXZuSkTw5PebSvPTVbxSkvDvDD2AHOBr+8/gCPndERu+BMktrwP8XgcusnOjVe6r5fS0svuoR74dvem7W/Btrf8Bg4dFZcZfPJ5fOQP/xNyuRyAWiJkJso0w6hVbM4WzZS7ukGRKzmv+T5jsfv1WcfYItxNk6JiUJweN6EZpkAmqrI4WSNLwMqk+LoeFNl57vMi95eLCvHAgQM4cOCA8B5fbr9c4EXAWuSj1c8nqsrskMezL09iL3Y1so+5g6+09OHDh48FBj9YW07KuQuBRJgsqWPU0+69sYkIwZnJ1tahVkNqvLSG8kIkpVwgRAJMTeftadmsDHd2/o3CdgSBzmQMe/fuxcFHD6I0eRxXrH3PrBKSJQL0dDjbeNVq4JEf/wgT4xNIppJ4w6Zt+PHzLJTn0Rd1vGOrN0tbrIjlq8WK6JtnqbgiwebqM5k0txNoZqdwIWCROImw+L5F9PPhXGNZ8YoIhUPoXLEaJ46yEV6qLQxJYl61FtnoLg/nU+LjHV0I9LwR63sC2LxSRroglqCP50SlZb3JBAv1JqH4wbtFWOgGS4Afy1J0zGH2hpenZW0QEHCmmmq/Ksl8xBSZeCotLXL41794BFL6WvT2zj49nN82Hl6kpaW0LGsUw5MmMkWC7rbWbgpepaUUjBwyKbUH02WtuY+uF/Qq6WjS2lRlC3zJeEeUuFRDrSmk5xtnhs9B6b0VCPfCCPciOPEYdJltaCJCUEqL+2qRCrLk3Lfaoq33JfwklaZTPD5k1JATwUg7Dj97DP39/dizZ0+N2m4m6jtWHj63BFih3DjtvqKLth6JMLEV4Ivdr8+6Xio66wt1E/j8jyp44ZSJHdcqeBd3Hwsooj8hH8IDNL8XBRRGAHsp65YToeeGvszKwymlntdmSWOTNvnq/TARYc9NXs9+iszGARfievWxeOGTlj58+PCxwBDKw5cQIbcQiIWX1gNqe52BWqPAHCtR2oI1mFer6drCssuovVjkkhdRNjdKS2ehtZ0STo55K6HCAZauHImEsGPHDqztlLCpZ3aSQyIBXQkJ771ZxbFzGk489g/46auH7VLLDZe+DKl7D0wK/OJlHXdep0D1eFjnVZYWzqdNO+THIkrCLfhZNiOCgYVRftvl4a5rxCoV7uTCtLxUdjwxxYdhWceGJ0/Gsyb27ftrDA0NAaFuhC7/CKTTgCKVcWvP4zgzbgK42V5+ImvaysSQ2nygXVdpyZ0fa7ssUmYsQ9G3suFqpwVRaVn1tHR5ak6dp7AyjTauYButSvWJu1A4hHe96zdx/SV3z9l2el330ZDTH1qkpVlVQp4YNWFSRgJO5MyaoCsveJWWAuzY8+2mWKE19gStoJWSZMt/ViKsTYuqITqnYXBzhZ6VvSDH4/ZrjcRBZCYPjwYJVFkkIgIcEavIBKoyvQkQviRUM4CDL9bKqSQ1Bqhttpeqm9SZSXm4YbLk8VZKultFM6WlZjgKeQBoiwCTefb3YldaWqpU9lxCMJXO4oVT7O9fPZ/GrZc5ExmqLHqG836WQPMJe4u09CK8ZnKulwqsY7xc9tEwAa+8wVKpjOzJJ0FxAwAgEaKepeEWAgqbtF3Mtho+5hbLaLjjw4cPH0sTqiI+4Puoj1hoaSkt66EeOQewhGQeqoc/mIXlcCx4xELEcz/dXlhutHIcLAKuKy5hdbL+F6JB5iloqULmYiLBOtW3XKGgV38UR189zJLJUynE43EcfeVprAyPAGDKu1+94s3Me5WO56ohDoZJ7UFuveAmHq0oTxeCtJQlAkWqtVCwSUteaZlxRnLDEybGMqYw0LfUmrzSSZWJ/f7IZBlDQ0OIrbwekes+BSmyAgCgmwTfP/gqnn75nLANEzlqq6KakUsE3qRmLpfDM0/+yn49mWbSEsu/rqjRhn630wWvTApYSkveU7PilIYDwMYe1uAVWSSf3IjNMbnm1R4lQuzzzqfKpgtUKL8+PtraiL5eMnhZo0LZ9kzJolZ85yylZTzM7v38hMxiJanuufe9CEXb7ddFGrX/DgdqFcc8EavK0/OzBESyfCxL7YAqszRuv29KQUhqFN09qwHUHvuZBfGI5c5zgWITj9SKTgWf0/aoc8OZTZL9fMAKiSlrrF/7nd/7Q6BqxpHOl/E//9ff28uqMnD5GueGctVa8ebSzIrE6ou8SMvl4mnpBWvflss+1tuP8qkfInPusP26TXsBIblOQhWc+4VPWl488JWWPnz48LHA4B/QlxsJNddQZTKnCd8LiUQYGM+J77WFCWJBYIR7zyI3VQVAWVx+ubWXWIh4EmVNlZYtHIeAwtRNG3skhAKMGPMiGawBuFVS7KV4nC547spJX2Y7paoqKKXoNl/AGXQDAB5+RsPNl8pQFYKpPIUsMdWgV3CMRYLMZQjPdJa5EAgopEbpZqmTAwpBexSYygNnxso40P9tGNEN+NXI1YK9gCwBoQBrGHFXqXl7lCBTpCgZAUBtg3nJBwBJPGihzqtAJAK+iWSKzrlsdoy9JqByuRx2796Np0dWI3IjU5R86av/B/fetAtlPWIvN55tXFLqhXrqME9PS47ULlZYMrqFjdUgKKs8nC+fT6aS2L5tOyvDT8wxaVmnrbVF2LnOFp3Aj9fOmoIafSpPMZWndZXtFuqp3sqaWLbdjGiqB6MJsWBSimx1HB4PE6iSO1RiRj97wREMR9GWXIGxLHt91da34tUM+zsSJAipRDh+/D7J0vT8LN3fH+FKz6N0BEWkAAA6VdHX14c73/E+9rqmPHz659C6H8wVQURp82AnTRcT5fk2PJtQqPmAdZwqOkX/N/tx+MVjiK1l70lKBEePnUK4i70OKAQbuiV84l1BlCoUl/aKITzN7vHWhItXmfRyKZ32glMevsAbMkeo5xlbKlcA2bkHJuQJhCb+Hdj4Ls/lnQCz5XvufYjwSUsfPnz4WGCoMmzfJz+IpzmCDdQ/SwmJCMG4q9w3FScIuVRy1sN6wCNBe/mRlt4l7wEFnp6eFlo5DqoMrEpKtsosEhQH2hasz6NBYBRzc00Sbvvs9GVNg6qq0DQNhBCs7ZZhEAmHT5hIF4BfvmJAloB/+aUGWQL+671BT9Iya5OWYsBHM7RCxs4FYTsTBFRGHiuyM1jjiY9klbSsmAE8/OOfIbC+A2ovax/WQDqkOufOTQB2xAhOjrEyRnXlmxzCMvMKzMhaSEoYNN4H6PmabbPIsnrp0Ba8Ksf7+/sxODiI9i1rbTL09Llx9Pf34zfe9bv2cuNZE+u6pndxlzQg5tFWebWWKhMEFTHYK1tkgTYAC4uy0tUVGSgX89i37zMYGjpiWxkcGjyEj398L9ojc2i8ifrlw4yspqDVbW2PMjWqGxM5E+3RxhdrvaTZsi6GocyUPGyWo1MoO8skwgSyDJfScnEOwE1TDLyIdW4AMuxCiwRRE5bBKy0VeXp+lu7v82rqW266DD98ll23qRWrsfe9e1EBIzrcRON0y2l54ssqd54tDLN5UIhmiBNOqdjiJ7Et2CpAHaxMX004H8ohYSJIVZgFzCVdUs19PKSSph7M1vOPF6FMMbcl/YsFlPPZXawhXdOFXmc/ykYAFcO58NsCOQSNqbrrscQey0WB6qM5ltlwx4cPHz6WHggh9sOW72l58cDL17IzIdX4yDUuD19eD+msPLx2nwghDT0YWzkOhBBc0u1cYF6kk0ScNFwrGXou1Ib8Llnpy9lsFuPj48hms+jr68P2bdvx9usdBuO7gxoO/EKDSdmD+dPHDE9PS1tpyZUYRpoExACt7ddCTaIEFDaI5YlK/nrR807ZdkfPJqht62vWwcJknPXx7YcP4wmt2m7/XTr2PciFY+yFEgMNrai7jc2UlrLHsRseHgalFAHinCwiRzA8PCyQG1MFKpRDt4J66kCe+FBlRtjypParw4Y98LNKwwF27vv7+/Hqyy8KVgZDQ0N4/OcDkOa476nX1vg20OiYFOr4VfKop7TMFEXFjhcp2gqaKb6y3CRJPEwgS0TwbVusJJVuin6gfPhXJCAGrABs0oH/vBnB7wZP5PK+td0dqt121VAbQuGQbZnhPvbTVabxJOdclZwy/9XGy1R0Kly7Sc6zt7hI24MFPj28t7cXJJAQPifBDvtvlVPAu+GVEO2GKrMTVNFMDAwMoFgQS4eXgxLx/vvvx/3332+/1gwARgk4/1PoJ78FeuyfAT1XfwWLGJbyuZ7SsmyoyGjORFhCzSEUTdZdn3U/98vDLx74w2MfPnz4WASwSlfneiDoY/EiESaCliOsEsRCteScReK5yczl2FSCKrFJQzfqvQ+0rjjllRhRj/LbUMBRfFgDbWUODrTEqUis9OWdu3bitttuw9VXX421a9fi4KMH0RWt4Jp1bGfcBMaxEXNGSst6EyGtBPEsVHm4pbTiy7rbnMoxkMqE/TcNdAJhllwTIEVcsUaCRIA3bpGF7Y+HnL87ODWTIbOAEQUVvOeu1+Ptb95cu0G0dkTMH2Ov8lfV47j39vYylW0p7eyLGkVvby8qrvP96lnxN/Nl2rDktW7IDK+0VBj5y1tsvHCKLw13NjogV0lWo1RjZZAbP1F3O2aKeh6rXgniXijUSeu2YJr1SwnTrutqJuShYdK6SnALPOmaCDP/Xr5fK2mLM22jXBEVbnzJdtiDtOQ9iHkSrlXw1RR5ToXYHnV+yzrf+TI7r27F1WxIS22OSk4Nk62LeiWPWL9liG03KSgtF7e6ziKgyjrFrl27sHHLtcLnPesus/+2VPtez7jhJtUzuVwOp04cAQBQSPg/3xzAJ746iT//VtG+plvxk11q0Mo54MjngdGfAlPPQj9+AHjq/iVHXJomtdsKf53yiv8S2pGucKRlREZozR1112lNbOgGW79pUkzml2Ej8GHDJy19+PDhYxEgoCw/1ZyPxlBkIhBnqergzu3t5CgtxfaxXNtLvRKvRqEgMymT9/JGjXKD70iAFQi2EljTDO6S91A4hO3btuPkyZM4fPgwHnnkERzoP4B9+/bhtivEB2+L7zxel7Rk/3qRlgSom3zemqflApWHVwe4PGHF/93F+SkasS2AxEY/neEs7r8jiL/9UAg7rlWFa4QvEe/wKFe9ekMYd96xA9f1RWs+29xbe7CssCOZAFevk2uISy+l5a5du7B161akxx2laKp7NXbt2lUTupEpUgxPVNVFOsXh44ZnEJOFis4Gbm5Y6kKJsD4jESaIhZ0GaX0lHACu3+BstCIT9Pb2AqYGTWMrsawM1q9qr7sdM0Eul8M/ffl/40B/PwYGBlAqOrXaAmnpYedgodiEtHSrLCs6Sx0Ham0nNINO2yevlXJk3o4iEWb9Fk/QtaIWXQhkS+JrXt0UCdb2pfw9zOtaa4Z6/oYdUWIrnIsV2GRgukBr/ETrlaHWw4VQWhqmaFnhhYpO7fLwcEAMUVusylsLuu1pCcRiMXzgQx8WPr/xjTvsv22lpUdzcFviuNHf349c2glhiqx9CwylAyfHgcMnDGFbljJee+01vPbaa/Zr7dQPgMoEWCsyoZsEKJ4FTn9rwbZxJjCoc33xE0e8ArtEOpAxu+3Xib53I9TAgoSfdM2VKZ45buDMxOIm+X3MDr6npQ8fPnwsAqgKgSL5N9yLDVetlfHscQOFCrWDLVSZQCbsQY+9rv7rumMvNz/LZgg2eGLx8sFsBq+SxTA3+JYkgnDQOxhoupBIbaCJVqlgaGgI8XgcqqqiXC7j+eefR/8//hUu3fwejBjrcPs1Kg6fMPDyGeZzaakqE2EWCgPUKQ+v7kc0RLCyg+DkGLFTry0s1vRwwFEVv3GLgleGK9iySrJJfQB4842X4dAP2d9G7FJbsXzdpWzQY5GV/PbzwT5eRMoVa1gj6mknaIsA6YLz2bWXqHj1rMggWMe4u01CQCHoWynjyaMO2yF7eLTFYjHs378f//CV7+LLQ9Vtft3NiMViKOu1TMmR8yY6EwTPnzRQ1CjSBYquRM1iAJjSSDOAoOtasAaJlpo/GhRVpxbuvE4RiF1FZiTrN3/0HF46NmF7Wl66aTXu+633eW/EDGCFEw0ODkLufiOIHMChwUPYu3cvQuGQnR4ONC4PNyhTptXzPOZVa2WN4v/5tzKm8hR7bg/gmvW1Db2eR2g9tEKaZLmK1niEQJZEtVGhXPudxYBssT4jGw2SGg9d/hzMxGcwWEcF3h4lVVKPwqSM1AsFWBDTbNPDeZKzMkcEmGkTNbWVEhY0HXbfHAkQwXaivMhDRjS7PLz6rxkA4NyIsmUZADuY1nOM1726WXn48PAwqLnKfk1C3fZEw6+eHMKVvZdAM6KYCx/ShcTAwAAAYNOmTQAAvTAG3s1bpwoAAyid817BIoVpWtcXEQj8eIja4V7lQgbpYpf9WVss1PCZj5/YeOwVA6oCtEfqL+9j6cMnLX348OFjESAgeytzfCxvhAME12+Q8eJpA+3c4DyoOiRT3fLwi420bKC0nIkXrFeCeMSl+IgGyZz4zJZKBezbtw9DQ0M2+RMOh2FSE6qqwjRNTExMoFgs4vnnnkf4tSPo6+vDTXfvRaYg4+UzbCOtcfXKDgmFigndqFMeXt2P9ggrd1/TKeGVYXEk3oqKcsHKw6sDkqvWyvirD4RqVMWrukIAmPyLKE4N+caV4uiX335esceXh1u4fDVbmBCCS3tlPPGac7yuWivhX38lLm+R3qtTkr3+nnYJ56ZMSAR1k6xjsRg+dN9OfPm/shK/iinXLV3WDIrHhwybHGiktDQs0tKlUrPUWqrC1KaSRNDmUoV2xgm2XyF2MIoMhGMx7PvMn+Fr33LI9t+6eztisbkL4bHCiVKpFJT2ACpgvpkHHz2IHTt2COdtIqNjYOAnNUnmFgqV+qQlr7Q8ct5RLT99zPAmLSvTS3CfvtKS+ffy27tY06JzpfqfRYIEURcJXo+gaxVeSstIkKmreFKvUKEIBQgm87XWCdMuD+eWn6tEYis8pVGwT1njlJZBCKX2i1lpyYfEMN9lWtNO+P7KKg/3mmxtVh7e29sLPO8w+hUStwmM518dxr5938IX9+1FVyI+7f1YzNDUHgDD9mvdlAFQINSzYNs0Eximc33x12VcngDAfCtLRgAZjVU5RAMaIsFIQ7ss3lYjX6ZoV8iivl58zB4+aenDhw8fiwABxU8Ov1gRUAiuXS/ejgOqo5yrWx6+tEUF00a9kkFg5l6w0SARSk7DrjJHi/SbLX740IOCqlLTNIyNjQFgJbeFYgGlUgmEEESjUZjUxOHDh/G5z30O29/x4Zr1dcQIEhmCiRx1lJacSssa2FupvSvbCY6NEGEw3gqxsFBprHzpl5cNQiTIfBnzLmXampQ4IuYJ56DKkrPLOkVbhIAQwLKaW50iAjm2ZZVkk5bxMJCKS4K61dqGtjARFJwbV0gIqYzIbOQZyivTSpXG5ag8GZMtUhgm9TwmhulNjtikpexsayzM63eAd92oCv0LgdPftCei2LHDKfNcvWJub1RWOFEwGAQtnIDadg0opZgYZ76l/Hl56rnXMHHogJBkbikyAXYNdNRW9wMQU9TPTjp/j2cNDAwM1BCh9UJ76qGVcuTaIB4gzPVrbjX0fKGRQhUAsqX62xUNiqp11nZmtz1e1441CcBPLBXKQDIG5Dy2b7oehxeqPBxoTD5mS9SejIqFXErLRexp6S55r+i152GKU0bPRmm5a9cufPGJX8FycpTCThmxEu3B0DND+Na3v4eP/+HuVjd/SUDvvgMYOl4tESfQqcr8m1e/e6E3bVowufJw/n4WV3OwSUszYAfxJAIlhNS2huvk+wi2TrLolck+ZgeftPThw4ePRYCAMjeKLh/LA6w8zlJasvfcJJNfHu5gpsciGhJJS3fJuFfC+0wwOjoCSqkQaKKqKqLRKLLZLAqFAiilCIVCKBQKqFQqME0Tjz/+OFOvdIvEZUeUIB4GJnJAvsQUPV6elpZ6V5IIViUJjo2IoSyLFa0Qqp0Jgvyosz+pmOgR6xVslogQjGYY6dcWAaby7P0r1ogsy5ZVMgDGNFhEaDJOBKVcJOioLC0EVYINLZB6IdUhDQtl2jJBRsH8+5IeSlHDpND02vetgZwqO4FBAUXCqhTB6XGKTT0SrrvERfZyBCbfTiSCGpXmbGGFE5XLZQSDgJ49BULiSKbYYDbOEax5TRGIf16RCdRPUAdE4ujspMNQHT8zhed+XUuElrTp1Rq6PRW94FZayrLo51dcIE/Ls5Mm1nfXb7depKCFaIggxvWbAQWznujxCl2zLB3CQZ60rL9d0/Uk5UnOegnH04W1CfVIUN2gQpuIhciSUVrWkpYUedf5SOdr7zfueTBFah4KF4vFcMubbsIPnmavJZWbmQh2glKKs+dHp7X9SwEaIsDG/wCMPQZok9B7rwMuuxNQ5k7pPh9g5eHsb/7a4quL0pUYNJNd+G0hw1bm1gPfR1jrtBS/C+XF7ePC4iIb8vjw4cPH4oSqXHwklI/6sFSFEnEIBFUWNVQXW3n4XAfxACJJ6TV4inl4/80EK7q7WGo0F2giSRLe/va3Yeeunbjq6qsQDocRCoVQqVQAAkiShHAkjKOvHkZEKQrr64gSxKsEHQUjLnmVViRAEFbF0tNVScluP7ySbjHC3da90JUQT/raLvEbXipRoUScK9++fLW4rvYowbtfr2BTj4S7bmAXYzImLhMLESEQaDoghCBQvcZLGp2WQqReibhheAd+VLjycEtpqcjA/XcE8YHtKj68I1BDMvFKOZ5AjlfLy+cSVjjR+Pg4zpw5g4lTz2Dzpkuwfdt2AExpa1+HahyFYsG2VaCUYuT8CAYGBnCg/wD6/+XbyOW8k3VFpaXDUOlSFLF4AqlUCvG4U5o+XaVlK+XhltKSVL1FVRkLXh6uGxQjmca/m29QHh4LQpgsmIvJEC/Vp620FMrD66+DYnrEpZAebjRO/G4Vpkf4CI9CWSy9jwZdQTxzRJ5eCLiJ3YpW68nKnx/rfuPuP5qF8FiIRuqUWqgJEDmAzq6lVTLdCjQDgBwCVrwFWP1u6CvvWXKEJQAYlNpl4fw9KpZcAwJ2kQwXHPVse3t704lLvo/g17mYiX4fs8Minmf34cOHj4sHAYVAkf3SBh8MVhCBULJJCBSZ2OU1XkEfyxkXRmnp/B32GDzNFUHzznfchR//8hXB07Kvrw+33Xa7nSS+b98+HD58GKZpQpIkBINBtLe3Y3JiElE6hgLW2OvriIllyZkirSkPd3sqBhRWAj1VoE1VDAsNQggCSuNyr864uA81peEewjE+4fuy1TKOjehY0U6wYUVtA7rtahW3Xe28TrnUjYnw7Ai8kMoGWKWKQyy2gqk6YTQGrSVHDNPxnQsoxE55VmVG4N602fuiUgTS0tnHxBwpj3lY4UT9/f0YHh5Gb28vtt+xE2cy7OIsFUsoZUcAtRtSoA0T45Mo5AtIpVKgoBg8NIhMOsNIJqOIJ3/yNezfv7/Gd7NUJVAopUJ5OJFUqKE2wMjbROjE+MS0CcTWPC3Zv/EQ61tkiQj92nSJ0rkA6zsa72tDpWWYIB7mbSdm30a8rEAsVRZP6rkT4w8d0fGjZ3XcfrWCrZuUaYUpuQnOit7YkqQVGFwQjxeOj5rCsY2FRH/QyiIuD/c6XvkG7cROD3d1tc38LC2EGiy3fvMN2HHH21taz2JGMpkUXrsnoVqxoFiMME3H31Xj7lHBQAChQAXFCmBQ50K95pJAU4uJoKC05EK0GvjH+lja8ElLHz58+FgEUOWZhYn4WJ6wVFhu1UpAcR5kLzalpSLXBudYmK3SUpW9iau5Qiwaxd69e4X0cD5EJBQOYe/evfjc5z6Hxx9/HOFIGO3t7TB0A4QQrEiUMTrprK8jSgQCKVuk9gBellg78Spt70xImCoYCxawMx0ElMZKo642cf/Wdtb3s7SQCDP1skmBt12v4LLVEnraJE+PSDeSLpJ0tgReSCVIg6KoUUEF2AyZAoVp0hrC1EtpyatOwqpTttvMq1Spo7S8EKQlwIjLPXv22K8rOsVwRgcFmOoxm4Sc7AaRFEiBGIrFDMbGxtC9ohvpqTQSiUS1ZLyMwcEfoL+/X1ifblC7dHwyX1uOr5EYAshD0zQQQpBMJedcaUkpRa6qtLQmHGTJXR4+/6TEVJ55KpaqoTYWRjMmQiqbHGlEasaCBLGQc7HNlugDvD0xPZWWru36/iEdoxmK7w7q2LpJQXEaYUru8+cVajVdWByT1+TLVJ5iLGsKRF80RAT1/6JWWnqUhzfyZK1XHt7Mz9JCo0nLu+65D4Hg0o+O3rlzp/DaTQxPN1xqscAwnW3nn9+CClNM8v1eV4LgqrWNPaEBsTy84istLwr4pKUPHz58LAIE/PJwHxwcpaX4vqoAqCrqLsb2ElAJdI8BdCukkxeCKkEqRrBlldwwiGK2kCRGTPKBJm6EwiF85CMfQbFYxNDQECYnJm1F5ltfvwnPP+ws2x71UFpWlWSRICOn2jzSqzvjBK+dW9x+lhaCKmkYANLlVlq6SUsP4kOSCGIh5k0pEYKNLfhPloolHHz0IF47rwJ4AwBW3hufpXWANegqa9MjJ0zKFHvtrsAZ3axVdPFBHmGXFUIjCJ6WMrGJ3vYLRFq6wauCJ8YnYFYkWGeqY8UlyJx7CVdceQXWr1uPRx55hPOKDYJKQQwPDwvrSxeoHTrEqywtFI0gsuPj9vW2fdt2VPT6oUdeyOTyGBjwnpQAmF+lNWC3rl1VFknhhRhwp6vK3WINaUkxkjawrkuq8SrkEbsASkumtBbbc4dXEA9XfkwpxUSObcdUnpV3T8cj1E1azoViyyKdvJSWR88zpiUvKC1FUnauUswvBNwEWkVv7MlqtQv3ZGsjBSWPRvfndFmFtkRViI2g6czW4sVTJvpWSkuTtNRzME8+BCOdBSoqtMrd9kcBtXZi4JYrFEgSafqMwrcbXSAtl1878MGwBB5bffjw4WP5Q5HJBSVNfCwtBG2lpdtrzgnouRhJy6BS65s1W2vGa9Zf+EehVjlVS3HpVmQqgSAUuQS9qv4JB0TSLMuVeEYCpFoKXPujkSAj7eaCWLjQaOZp1cl5WrZHgF89+iPxmLV5K28SYTFQpxFKxRL27duHoaEhINyDyA2MtIwEZk/OMDsCiooOlMrTizqeyptoj4qEq2HSGvUTrxbkyRAvQpeH6upbAgoBpY2Jg7lGKs5UwclUEvR5J2Qj2LYWkcwJ3HjjjQBge8Va4TySEkFvb6+wLt4HlPeztHDDG25Dh7aihmwsabBL6hshl8th78c+gRePZTyTzUvFEh769ycAbAUAxAJsG2RJVAzN94CbUopMlbQsVIAO7rNskSkwj42YQuq5G/EgEWwXGinipoOgi7R0lJbeQTyFskM8mpSdO3f5eCPUkpbT32Y3LB7N7f84njVtm4ccdz9LVPtma5JgcSstxWNb1GhDpXC98vBWlZaN7gdjGXNpEnouPPHEEwBg9226AfzrrzT8+lUDazsJ9t23xNSkeg546n4Y4xr0chtgHIF2Lg3gPgDsWYbv/0Iq8IbNrKE0u//zpCVP7i/ma8bH7OCTlj58+PCxSNDqw5uP5Q9r4OdWWgqhGHOc4rsUwEgTcWAkL+JAGQvTIZjrKTJft0HGr4cMXLNOBiEiUZAuOARVJNhYBdgZJ56BLYsNzQYtbRFgZQfB2UkKbexpHPiRmAD9V//fXgBxj+8RnJ5obRsOPnoQQ0NDiMfjUAIUNr9gFKHI0UZfbQq+v88UMS1Rl1vRRCkjmNzkCF92HlYJcrkc+vv7cez0BMy2K2vUgBbcpGZAaT0wY67QmSA4ch7Yvm07fn7427BOWYW022pIADg0eEjwir36hjdg165dwromBdKyllhZseZS3HPTlTXvlyrUk/x3o7+/Hy8cnUDs0t1Q8y/DyBy1A30sv9ojI0DoKkZavvz8IZTftBWKHBeDeOZZaZktOgnXPAFomqLPZSMFXSwCxIvOPgTmoDwcsCYFnG3oiDUO4nFPRORLjcuV3XCTlnOherXWqRmipcOJMefHeE/LtqgEQpjKrKwtbqWl+x5SLDu+sV6wlHPuCTwvL2kvNCLDRzMUxhK4pzXD4OAgAIe01AyKo+dZWzk5RlHWTABLwNvFwulvAcWzMGkXDEoAmKiUnKC0oEKq5CNr5zdfKtv3mWZBgfz9k2+L0/GH9rG0MCc6jYMHD0KSJHzpS1/y/PzVV1/FBz/4QaxduxaBQADJZBI7duzAwMDAtH7nb/7mb0AIqfvfpz71qZrvPPPMM7j77rvR29uLSCSC6667Dp///OfnJBXOhw8fPuYSkXkeFPpYvJAkAlUmteXh1dcSqQ0iuRgQ8hgQNyO3FgPmQqH2W9tU/Mm9Qbz39QYGBgbw85/+0P7sfNoZBEeCpGHQTldCWhKels2OGSEE/+WuIG7qeg6jT/8j4vG4kAD98EMPen5vOr6ME+MToJRCVVUQqoEUTwMAonRs1h7EvGKsURm8F9yqIt0mR8T3+QGcTHTs3r0bDzzwAL7ylS/jQP8B7Nu3D6VibTS02/MyoBC0zfMkSTRIEAkQhMIh/MF977LfX3vpVlvBaCmTd+7aiTvuuAM7d+3E//jLvxFCeHSDCkpBL6XlRM5b6doqiTg8PIzglj2g3begsuYDUAIRO9DHIr7DiRXO742cwKMHfwbAXR4+v2OTdMGbmMyXxakhnvzmySNFBkIKQXvUIRN72uemBIAnP4OK0/fzJBdPrLrVoLlSY7LVDd2kyJWoHRgyF+fC5JqVpdys6BRpjkTnScuOarO12sRiJmBq/HP1xt68FgnltlsIt6q0bHA/GMtQT6/rpQzdYPvE2wfkigu4QTNB6RwAAoNKMEzWL2im04EEVYJkdTJCIqw03MJ0lJb8ZF2j8D4fSxuzftR/5ZVXsHPnzrok4C9/+Uvs2LED+XwefX19ePvb344zZ87gRz/6EX70ox/hM5/5DD72sY+19FtPPfUUAODtb3872tvbaz6/7rrrhNc//elPceedd0LTNLz5zW9Ge3s7fvKTn+DDH/4wfv3rX+MrX/nKtPbVhw8fPi4k/PJwHzwCSm0JqvU6FZOahmksR9i+WATo7ZDQ1UbmzWdvNmh1YNYIkkTQESpj376/ZsoyOYLoG94KADjHETGRAGlIqMXDBGVt8R+zVspMYyECtXAE1NSgqgkAsBOgx0bPeX4nXC2fb0XFlEwlhfJjcvTLKEkrsfX2y6HIW6a1P17bYaFYodNSTrv92yxyRHPtE08inDr+GgYHB5FKpRAIt6EUidtqQLey10vhfaFCeBohFScojFOs6gzZ9gg02C2oQ93KZOLqF3k/S0opzk6xV+1RIJ1n5JylxMyVKEbSJi7pZoq3VhPEe3t7IZ1ayV4oEVQifSDkPJKppE18y8E2WGNrWslgcoKtmx98z7enJV82z6sS3QQgr6Drbic4NcY+DyqsXwqpEj70lgCeO2ngD26fm5IRnrRojxI7REoM4nH+distcyWKsuYdWuWFZ44Z2Pf9MlIxgv/73uCclJnyFdRlnanDxjJUIITz3JyBVQJvqUznokT9QkE3KNIFilNjJrasYs8jjZSW1vnkqw5UmbTsGesVitSdIBjJUIznKCra8mItSxpgUiqoiac7ubXgCPUAoDCpBI2yBqBzKeFBFXj7DQoCCnBpryxYvjSbWK2ntPSDeJYvZkVa/uQnP8HOnTsxMjLi+bmu6/jt3/5t5PN5/Pmf/zk+8YlP2DedH//4x7jrrrvwyU9+EnfccQeuvLK2LMONp59+GoQQHDhwAPF4bckPj3K5jN27d0PXdfzgBz/AnXfeCQA4e/Ysbr31Vnz1q1/FO9/5Ttx9990N1+PDhw8fPnwsBIJqnSAesIHjxYigypSCfT3SvJerzgYBhUAm4iB2JhDKldUAytQAiIyxrLNMJNjcszC1BFS6rU7iuIlFKwG6Z0VX3e9EAt6edVbojuWN+fobX8/Kj199GRRSNagljrfe8uZZTxrwRPZ0CZJ6SksKVlJoqZp4pWC5mAalFMFgEJRqNrk7cn4EAwMDoh+oq/Q9pM4+eGgm6IwTnBpnxFh3G8HwBMVIunFAjtvzlifmJvPUHtSuTkkwTROZIjCZo9AMir/8ThljWYp3blWw41q1ZaXlrl278LeHJ2GdFj1+Dfr6zmP7tu04+OhBEEJgkLC9PNWy6OpcB0AkY+bbjy3NEX2lCrVL7HOl+uT3ijYJp8bYnlqEqywBl6+RcfkaGWs750bGHeTKw9u5UDFZIgiqjJzgidaMS4WWKzFysNiiL+kTQwYMExjJUJwYNdEVn71i1OA6fCvYZyxbW8YOMFIvGpDsv53vLE6UKxT7vseulx3XKnjb9UpDtaM1kcaTltOZzAu6JnCjQWBlkpGWhglM5KnQ9y11FCsUxTLAa8JytaL4xY3V7wZG/h1GRoZhqgAkaHLS/jikAkFVwvveKDYEVSZNJxr4ST9+sm4xXzM+ZocZkZYjIyN44IEH8IUvfAGSJGHt2rU4efJkzXI/+9nPcOzYMWzduhWf/OQnhc9uv/127NmzB3//93+Pf/mXf2lKWhaLRbz88svYvHlzU8ISAPbv34+zZ8/i/e9/v01YAsDKlSvxuc99Drfeeiv+5m/+xictffjw4cPHokRQqU1QDMgseOZiLA0H2H53JZZmAlEoQIQU3oBCoOkU03nE5suVAQroBUAVn4ku7ZWbDtysCeTFjFbL/rdv217ja9jX14d77n5H3e+EAgQouIgZLnSH98a8//778cyhX+DsaFYIamlGDDcDXx5emWYpqpu05P3cNN2Z7OBLXNtiYRBCUC6XEQgGoWkaKCgGDw0ikxYDZL76t3uBNqddpeJSS2q1uUZbhNlkaAbFynYJwxMGTAqMpCmiQeCfflpBW4TgA9tVe/tKGivzjYXY63p+lr0dEnJFikyRIlMAhs6aNqH08hkTO65Fy0rLWCyGQNi0y5Hl5BX4/XdfCUkNItS7Deu3HMOwHrIHXWtXtmPH7UwlHawTKHGhoRlU+D0rvCYcQC1pWd2voOKkeAMO4cpfC3MVEMeXh/O/CTA1eVmjQtCOWx1qkYHFcmu+pJmCwxj//LFnsKnrCnh54k4H/CRVRWfqRLcVQa66D7EQsY+jRdguZu/h8Sy1r5fXzpoNVZayBPv6lATSsvU+xa20bItazwLseI5mKHSjuUJvqaCsQXheAIBscYmpSZUYcP1nYT79CIyJIrDuKmilu2FNRgQUp3/n0co55K2C+OuEgvWjSyFs0Mf0MCPS8s/+7M/wD//wD9i8eTO+9KUv4ctf/jK++tWv1iyXzWaxdetWgTTksXnzZgDMC6YZDh8+DMMwcMMNN7S0jQ8+yLyM3vWud9V8tn37dnR0dOAXv/gFpqamPEvNffjw4cOHj4UEU1q60sMVglRCarmkarlhKZBt9RB2kZadcYJEWMLLw/VHpm7lXzweE1SF0DM2aRlSgd/aFsC1l8izJtQWAwIKy6bhhzNeA5x6ieuJeP2gHC9vVF7Faik2h4aG8PgTj2P3ve/AVN4UlESzPca8+osvaXOfc6+wHN11DAyTV5o4JbS8UvCGay9HZutWDA4OMuJ75Ua0tyeRnkojkUgI+/ztb30D//HDH7S/u1ChX5JEkAgD4zmgh1OXn5uiOHrewCvD7IRs3STjijXshFAAzx43cMMGdh3wZNYwZ6OwsoNgNENwfJRNHBx6zbkOLa9Ht9KyrLGS2O42kZmjlLqWJTh0QsGLpys4eh4IrbwPKZJFukrsfGTPBxCLMeUlT8bMZzmwV3BJsUIRDtQqLYvVfQsFRNWjpXbn7SjmqosOcaRDu4u0DAeBybwriMc1CWGlcrfia5nL5fDCK8eA4CUAgF8+fhjDLzyEG7/0CcEfdboQPS0pxnNOojjA2o1VHh4NOX2Ko7Sc8U9fcPDl+OlCMz9L52+e1G6bhuWEm4RqjxBh8nasqrhcyti4caP9d7FCBesAgF2HjVTmixJKDMbK34QeNoH1KrSXy0A10k6RWHtwk/OtTFiG65SHA+x+uhS8zn1MDzM6pRs2bMDnPvc5/N7v/R5UVcWXv/xlz+XuvvvuhkrGJ554AgCwevXqpr9p+Vl2dHTgD/7gD/DjH/8Yw8PDWL9+PXbv3o2Pf/zjCIWch7oXXngBADwVnJIkYcuWLXjsscfwwgsv4I1vfGPT3/fhw4cPHz7mE2wW2v0esKJtCT2w+rDhJsqiQYLepISKTnF0pHa05aX827BhAzZs2ICjR48y4in8GAKX3IvVSeD3bgvaZMpyIC1Ziq7oPbkqyUgmN7wS1xuVb4c8Ss9FFavjjTkxPgFVZoSpzjEOs1X08EpLqyy4ntrTCp6xoJuwP7deW2CkLnufJ0MTsSD279+P/v5+DA8PQ0tch+Fz43jkkUdq9vn8uTOz27k5BFNjUazscNiOc5Mmnj/pjFTPjJs2aQmwMIZnThhY3yUJpDevtFzZIeEUl+L89PFa0rKsi56ImSLF86cMrC9RbFjh/F5JE8s4AeAHT+rC56Wqao8ASLWH7PapSMyj16SM2OLP64WEVylvoQyEA7WhJpZiN6QSgWiy+jRJIvY+zFV1Lk/muknLSLVN6IajqvJKDwfQUoL4/v39KJRvhFwl+4PRTgwNHcTX9h/Ah//g92e8DzyJVtHE4CCAEapWlxILOvd7a98NE/j0f/tTrF61Ert27ZoVgTqXoJQiwxHbmSJtSA7zfaVE6pPRjeAmoTqiBF0J5/ujGSr0fUsRd9xxh/13qeKleGZtfq7UzPMF6zrQDSpMOqpKVV3smhxqpcQ/FHAOgtvLuaxNzyPax9LAjEjLP/qjP5r1Dz/33HM4cOAACCF497vf3XR5i7T87Gc/i66uLtx8881YtWoVnnzySXz605/GQw89hEceeQTRKJtZt9SbK1eu9Fyf9f758+dnvS8+fPjw4cPHXCOooqY8XJWBVMx/GFuKcJfCRasc1PpuGdkiMJoVmQIv5d/Ro0dx77334PU3vd5W4r3u9UCyLSQQHcuBtARYOaqlNpKr4UvHR1uT0zQ6Bl5eavW8MZOpJFSFXYtFbnClzFLtwpesWqRQPbWnV1iOZjgDeV7RJSSpcuqnUICVMe/ZswcA8MRrOr753Yc993lVr5N0vdCwyF1eafn8KQPn07yCspaYKpQpXjotSnCs5HBSXV9HTAKqTpQ8wVussGMXVAlKmqNctVSbx0dNSIRdu4CY8NsM0RDzZbTUiYycZ7+vG4xMnI8SV9OTtKQ15BCl1C79DQWANk7AzBPvskRgGlQo/50N+LToGtLSFcYTULzSw6vl4S2QlmeGz4EojueoFGwDpRSnh70zG1oFf4zLupjWzm8jAIydO4F/+9dn8OHfeSdk4nzxf3/5KyBmGQ8++CD279+/KIhLzRC9Yyu66B3rhqLw7YT9G1CIbeHQCrzLwzmlZdassc2YC7Qa5DTXKGm0pjy8WGH9QwsWrYsKZpWZN0zx/qTKgOzR17WikuSD+jQDgFECxh4DtEmUTQm4/E5Wnu5j2WBBuPqRkRHcc889MAwDv/M7v4Nrrrmm6XeefvppAMCHPvQhnDp1Ct/5znfw85//HC+88AKuueYa/PrXv8anPvUpe/l8Pg8AiEQinusLh9nNKZfLeX5eLpeRyWSE/8rlsueyPnz48OHDx1wjHKhN1iSkuUG5j8WJkIsoi3Ak5sqO1pV/2WwOO3bswM5dO7Fjxw6k2sM1yqzlEkbA+/2FgwShAGnJnw5oTPx4hTht37YdfX19yGazGB8fRzabRV9fHwumkWrXN/vycN7LkP3bSO3pBj9AF5WWzt98yXLEtc+KXLvP6UwaibYETp04hi9+8Yt1n5HnExZB1dVG7NLjYyPiYP7slDeRzS9lUopzVXKzM8ES5JMNJoCcEnFRUWYhzw0J+L/5UmlFBrZuFBtKIuyE11iwBuCaUetXeqFgmLUkU7FCa9RdZd05jiGVoDshwboF8aSRQ8LOzfaF6ygtE2EikKUWKVkbxFP9vIWhW8/KXkB2SEtDioAQgq4VzSsBG8ESlZWKJfR/4yH889f6MTAwgFKRbdxE2tm44ZND+J9//Rns3r0bYyOO0rl39XqkUikMDg6iv79/VtszHTTyc9WMWjJ4JF1/MklQWlbbiduntBncQTztUYKOGLHbouVpOdcotGAvMFd4+OGH8fDDDwNgfbd7MqRUoZ6TDYsd1nWgm2L/piqA6vE8656s9wJ/D9c0AzjyeWD0p8DUsyiffBB46n5AX/j7l4+5w7xX/A8PD+P222/H0NAQXve61+Hv//7vW/reo48+imPHjmHLli2QOVr+kksuwVe+8hVcf/31+NKXvoTPfOYzCIVCkGUZpmk2LbEw61z9/+N//A/86Z/+qfDeJz/5SXziE59oaXuXIiYnJxd6E3wsMvhtwocbfpuYP1BKUSksfvLJbxOtoViiyKQZO6UQoJBTUbA+NCmKWQMaRySEwypMo4JSIQtFVaFrGkyjgnBYRSZdS2LxyKaVBW07c9UmClkDmepgOEglTEzIUHTnvUZITyl1S8Qppcim9ZoQpD2/90H88rFfYXJiEh3JDrzxDTejUikgny0jX6DIZJzfzaQVlGZh9k91wHoMz2QLyKT1aZ3zkVEFiWqp7sS4iUyajQZHZQnx6nPyZJoAYH/r5RwmuFUUMgYqFdPe57GRUTz1zNOYHBnGl7/8LRBC8O1vfxtf+MIX5kTdNdM2Uaw4100qFsRYtlZvcW7SxNTUBBrN50zkCco6kzd3xnRk0hMIUALAOxZ9eCSDEEycPS8DFfabZ87qzjVaJlgZZefv5FkKgLFsV67WcS4tYapA8N4bK+jrMXHkXBATebaOiMp+O6dKmAiyc6NIMgCCsmZidGxiWgq0mWI847QZC1qBIBMkyHBhMYwMZISeQjTQSh7vvVHG8TEJt15axsQEI+ByWR35MkV6Uml5Yq1Rm7hhHfDDp2V0xini8hQyafb+qpgMmUqwrp3RiQwixES2GAJfGnx2NI3RczpC4RDGUo236a233Y6/eyFi9wcGCWP92tV405tvwcRE47624f5NaCgUSvjsZz+HI0ePMA8BQvDYL36O++//CH7+2GsAtgIAgnIFaiyExx9/HCtTw0B8FQBAMyXIsgzDMHDkyJFZbU+rME2KTIGiPeatbZrKm5iYAgBnJu70aAnWOZEIhUk5dSUx7e0uVa/nFWEZExPe6/dqF8UCwNMWQeSQz5qIh4JIFyWk8ybGxiYQpLXrzLcYxuSF0Yw5b+F/hw8fBgBcf8NWjE/omMgosPoVAJjKFjE2VoQ2TcJ3oTE5oSNbohgdVZDNy7A0c8V8BjAN4b4KAPmQjAlVPObuNlHMAVZ7KGTOIzNxBtb0yohuoJ2eBp7/Z2Dt+y/ELvmYQySTyeYLYZ5Jy+effx533XUXTpw4ga1bt2JgYKCuEtKNSCSCK664wvOza6+9FqtXr8apU6fwwgsv4IYbbkAsFsPk5CSKxSKCwVohdbHIpuTqPYj98R//MT760Y8K7wWDQc91LSe02nB8XDzw24QPN/w24cMNv000R8KgeG2CkS+JMEEyKT6CbSwbQlDIHXfchRdeeIX5GxYrIIRgy2VX4o477qoJZnGjq7M+YTdfmIs20a0ZKFTTYVd2SUgmZUhBirTROKGCAOju8kjb4dfdqaPoDo9oA9519701y3Z1yggUKEpc2WZ3Z+vkjBd6OjUAVXmYEkaiTZ3WOU+0y0hWSYW0biBRZtsWibPjBACS6oQedCbjSCadY5IqGKjIpr3PAwMD+MWvHkd7sh3h0BqUy2U8++yzePjhh+2S8tliJm2CUor2cR0mBXqTZYxlawlrzSDQpPaG5MKJtAGAyabWdgeRaIthtUoBlDyX10gMiTYF4Rg7noUyRTiuw9LjBRTnGpZGnHPZ2R7C7/+GAsMEFJmNce5+vY4v/4TJXrvaA0i0xdCZkpFMsu0NB7NAnsIwCeJtHeiIXniSRJNMJHIiaUkASApBQnauiwI1YbWhWCSAQEBB8exBqOMTePaJEN5y1dsQi8WQmtIhFyg6Oxtfd27UaxPv3UYRCmuIhwFFZjXpQYVg8zoZyUQRAOsDiBKHEpJgmOJ5zBVNfPFL/4S9e/ciEo8J6swaqO2gkqPKinX04lMf+a/oXBGxr6WZIJbQ8IvHBnD85Gm0d3TaFgzHT57GU88+h3zJGYNKtIJwOIQ0IZCJc17kQAx6cQSyLGPjxo3zcq8tVSgMhSLZ7t0O86YJKpdgnQMAmCwGYCV5JyKSUC4eCshIJtsAMA/SxJiODWuUhunh7v1k7dA5Ryu7Eki0SYiGS0gXKUoaQTTRUXO+KjqFUaBIzpB4zOjGrNrAdGBZ3EXiSSTadGi0Asu+AgCoFEJbR9ju95cKYuM6SJAi0S5DVsuwTCyTHW2QCFAkYp/e3Sl7tj2+TUhBCiDLXhCCRCwCq/1FglEkEwEglAP8Z9Nlg3kjLX/84x/j3nvvRSaTwY4dO/CNb3xjTn05enp6cOrUKRQKTLewatUqTE5O4ty5c57p4GfPngVQ3/PyYiAoffjw4cOHDx/zA0Umdvq11wB6RRvBMCcmqJeK3YywJGgcQrOUIJSHVwe4bRHvFHEerex/KCB6VDaCqgBauYCBAXYuUqkO3Lj+LbN6jvUqD5/OOefLwPnADz6UgC9tdocPucvbrdL0QLXONxgMglJqe8QvFAhhtgCFMgvjOXzC5D5zAnDOTlJ0Jeqv5yw3IWD5Y8bDrEzbK3V4qmB5IrLXbs/EChfSw5cmh1S2zfzxvX6DjOOjFMdHTNx6FRt68eXhLBmZznN5eO17FCx8iEeJK49VJQP79v21HRRFi8MY/Ml+7N+/H7IUaqh0nS4kAnS4yvdXdhAQQoSQjUKF1oTwAABRY7Yf7Ou3vK0haen+voYQgqHgrNK7DZOl0jcM+Iqssnh0ECOPcikPQgh6ezrxapWfGxnPgabHsXXrVuzatWvmGzQNVPTGyeWZIq0JFTrPWTR0RIGpvPOZOz08rJKGhKUX3D6HlmVAWHWunWK5th0UyqJf7XQxn+XhFqzSey9Py6WYkG4VtbLgLOf9ehYurXha8sGGFTMI3gykbAbY61DPtLfVx+LFvJCW/f39uO+++6DrOn73d38Xn//856Eorf/08PAwPv3pT6NcLuNrX/ua5zJHjx4F4CSRX3nllXj++efx4osvYsuWLcKypmni5ZdfBiGkrnrThw8fPnz48OFjLhFSGdnkVarWHmU+e3xatlcqdjMsF8ISEM32LdKBEOZFyAexuKG0IEQJBwgmG4RH8KiU8vjYR/8Tnj9RBqUUMCt46dEvzSoYI+IK4nn0RR2PDxl4x1a1pXPOk1sG9zdPZla4wbo7yMJNWlpBRJVyEWEwb3dCCHp7e1vYmwuLSJW05MN4AOC6S2Q8dZTt8PCkiavX1VdE8cnhvdUkcokQdEQJxrLss64EwWiG/Z2utg0rzMiLGCtqQDQI5Ire5HCpWBII6Ptv345QmP02f/wViY3qKzrFV/95P/7D79x9wQNXWiU/eOL7/NmTQlCUnsnaXotvvPN35zTVuMbPGc55E0jLsve5AZFBpRAmxicaJlsDQNr1fd1gnoLlWZGW7N96AV/xeAxnOKuDzOQ5aGMj2Lp1K664bBNeHWTvv/Pd78e16+i8podXdNpwUihbpEIQDyB6ijJCkU+Jdj6TJVJDRrcCfgJLldl1BwBhTl+UdvmaAowArOit9fNecJOz8wGLZM27ROAljS5J0tJqSoYJoV0FFGJPOvFQW7Bd4YlNXWoDAkmgMgGAoGwEgfBKYHXzoGcfSwcXXF/8/e9/Hx/4wAeg6zoeeOABfOlLX5oWYQkAiUQCX//61/H1r38dzzzzTM3nDz74IMbHx7F582ZccsklAIA777wTAPCd73ynZvmf/exnmJycxM033+ypwvThw4cPHz58+JhrWObxEY9CDkIIuhOzJxznI3l4vhBQeKWl835nvPFxaiUkJzSNKtZv/tu/4NlnnkQ8HkcqlUIiHpl1MAa/P5kixb89puHYiIn//UilRtXnhbpBPBzR0khp6Q5rskJ50pPjOHPmDMbH51fd1QjWserhSgZlCXjLlc6JPuuRIM7DTg4nwAqO/OQJlNdxoTnpquFsPaUl4ISV8OE1FjlcKpawb98+HOg/gIcffhgH+g9g3759dgiL1UZzuRxOHX+t+m2Cv/nbz2L37t0XPARJ9wji8QJP+GnFrKAaDAQUUEpx/PhxfOfb38DXv/61OQ1w4nnLjhix+88ER1oWK7Tu9SIF4kimkk2Jp0yh9vNMgaLito+YBix1mVfA14YNG/DUU0/j1SNO4E5INvDJT+xlEyERp3N63+7fwZ49e+Y1NbyR0lIzKAoV9l89uEN2Aq6+JtWk//YCP4HVHiV2ZgUfMObVDooVKgSSTQeU0qaE94XA8lNaOunhQhCP/P+z9+Zhclz1uf97aum9p2e6Z9GMVmvxvoNsZIFkgx0Z2yw2S7DMmoAxkEt+WLlJuCFgskESTMK9IQlrHBKNgRgMGBkLvEnGq4T3VaPNWmY0W0/vWy3n98fpqjpVXT2bWtKMdD7Po0fT3VXVtZyqmXrr/X5f/9/V03FaShKx/9bRDAKsuAXougJovwBa55WgF/2LSA8/yTimTsvh4WF89KMfhWEY+MIXvoAvfelLU85TKpVw4MABALAdkrFYDDfddBO+//3v4xOf+ATuvfdedHV1AQAGBgbwmc98BgDwxS9+0V7ODTfcgM9//vPo7+/Hu9/9btxwA1Pbjxw5gj/6oz8CgJM6VEcgEAgEAsHcwhJfmoUC9LRLOJQ+uruSo021nktYApAqE5eAmYwTuL08bqazD2ZSnjg8dAjUqDlijUKOunSaHwP7hk37ZrRUBe56XMPH3hpoMidD5xwrfKYk72ThRcupnJZWafozj58FLf0K+vr6jqu7azKsY7WgnSCgMEHljD4JSzpZkrVJ3eXfXkxKcSRTTw6Pu8dSXwfBwBATyC5dJeNXzzC1xurJV9MpDNNfGLMEjUKVF4fZ/9u2b3O5EjVNs8uVN2zYYDsJ+/v7UcidBiW0EgDQ3tmHHTueQH9/f8t6ifox3RRifgy1xQIu12CtWgallJlHKo8AoU7cM/oktmzZclQuZAtZIjDr47mXE6ytACqAOeFyZec11YogKusNuGT5WVi/bv2UolWu5PNemfo6LQuFAvr7+zE4ODjpOWKdhn4tH7RaDXfd9RNEznu7PX0lP4xweBFisRhCqmOxmyzF+1hR02lT0dI6D7xOS552j2jpTYNOzsJpGVCYGDpRpOjr8H+YlS01DupyDbN2WmoGu55abSCONatXs1AmqyWDV7Sct+nhdnk4dVUCqIq/CDvdB68BhR2jmgFADgE9VwBgfxfUoEA0+Tu5OKai5de//nWk02koioI9e/bggx/8oO90a9euxac+9SkAwFNPPYUrrqgPOs4z/LWvfQ07d+7Ezp07sWrVKqxduxaGYeDhhx9GtVrF5z73Odx000329LFYDN/73vfwzne+E+9973uxdu1adHZ24sEHH0Qul8MnP/lJvPOd7zyGWy8QCAQCgUDgEFIJJOK+0eJJRAjaI8TupzcbTibRMqCwstCIZ3+pMkFikv2kTOMGM6hO7yZUlQkWLuwDqO4Sa462dJoXTXOessYdewxcusrA2YubH0y305JyP8O+yeb7EXq316+EPhQO4YM3vg9Lu+ZW0IPlTA6qBB+7IoAXDxj4vQsVqDJBV4JgOMNEyWbiQjrviDC9nOBBAFx9kQpCgBU9EroTEiJBJshk62OLAkgXKPyqZa3S8SLntLTcgJP1MgSc83RwcBDUcMZRINR2XHqJ6tMVLbkxdN7ZqzD+6iq7p6WZG0dPTw9GRkaQWrwQSjwFw0zZLuSjFV0Jdyj5dgrtLtESULjgoM6YjvG6oPbOG25EKBxyCa9+5CuNn+dKjWXFhUIBN910E3bs2AFKKQghTQVaXlzytvm4s/9ONr8acxLLqxn7mIe4a8OJcPrVdHdvXJ6c3eu1+T6dSrT0lv5PB0ki+Mv3BvHL32lYf46zQH5cFHwytco1Ous+sdZ8mgEEj8Ml8ZJLLgEA7NjNLlbe8nDmtDz+IvbRYNZ7uwL18nBuXKkyASHu7WHvTfN3s0KAqv/xrWqND+oE85tjKlr+6le/AgDouo4777xz0mkt0bIZHR0deOyxx/C1r30NP/rRj/DAAw8gFAphzZo1+OxnP4vrr7++YZ6rr74ajz76KL785S/j0Ucfha7rOP300/HpT38aH/vYx2a/YQKBQCAQCAQzJBxgN1mT/VG+rFvCs/udv8KjQdbPj//TXiLM7ed3M+4t+53PEEKgKgRhH2dqMjaJaDktp6X/+7EQcZX7KhKwceNG3LNlK547xMpjaSV91KXT4SlsIHc+quEv3iM1lHVbaJwoYpjMGdgWZjf31k122VUe7p6/We/Trha0KGg1fAnoBctkXLDMOcB9HUy01A3g0GgFrzy7vSHEiC8d7+1w1IdIkIACeP9lzmBIRNj5li1RW5gaadI/1Qpy4sUS60a5WS/DZIql2VqicV9fH/CCs4CaIR+XXqKz6WkZjwZcrsEzF74d44dfwX/8x38goMqg1GxpgBOvbfECRAenD5Zq1CVunr08iUdeYddPjbKZpgpiyZcbd0auzBy8NZ3aztz+/n7s2LEDqVQKwWAQ1Wq1qUA72f61xgaV6ln0RhXEqNjHnC+FPiGipdG8PNx6wDKZ09LbszLQogdpb1iuuATdSrmCA/t2A1gBABjPVgGPv65cc8qTZ4rGi5bHUQCraFZfUe/7FPNMs0RFp/jBthpqGvC/3xl0bZMio+FvIa/APRmB+sOKWq0CDD8OdK5hjktY7tq597tMMHtaIlrecccduOOOOxref/7552e8rMsvv9zlsOSJRqP40pe+NK0yc4vVq1fjl7/85YzXQyAQCAQCgaCVhAJk0hRbAEjGJCTCJrJlCokA5yyW8fzrRkN/wqAK37LHk8lpCbAbeK/TEgBScQl7R/yVgensg2Dd9eq9CeyMu0VLVWHVO/3/fQf+5ttMrFm2IIg//oNrj6r8VZVZwjTvEiEEOK1bwt5hE+N5iu89UMMtvxfwdSbx5eH3P6/jjodrWNEj4dZ3BOybbHcQz+Tp4QAQD009Pk8EQRW+xwpgIuQz+9g4+PYPfoFDL/7KFht37tiJTZs2YWjCLXICbP9HQ6ShBDMRIRiaYIJBqQpEQ8B43n+cVXx6zxGzhq1bt2NkeARtiTZkM1n2PiFYtWoV1q9bD8BJD9+4cSO+8+Rvka/PnyvpvoK4btCWhmxNW7TkRLNQwO0aPHexjJ/96Lt2gJMaMFsa4CTV95FM3P1t2yOO8Fyqut1nTJRmJ5V1HmsGnXT/+Tn0rHCfmu702BscHASlFMEgE8YmE2gNk2Ika2I4S3H2Isl1Dq9ftx47d+zEISkCAsDUCjjrzJX2MQ/wYU4nrDy8idPSKg9vsl6EAImwV4hqzbjl9S2rZ+zeXBeCq5ho+ZNfbMWnrnq7fV3WDCdQSDPojB/oWa5A7TgJYHfeeScMk6Lvgve5fgdZlGvu6/584LFXDTyxi52P21/RXT2X/crAp9PPEgCgFxAwxgB0smWOPgRkn2O9LeVQU9FdMH+ZW/UfAoFAIBAIBCcpYdVJPZ2MZd3sz7OVC2TEQqTBFRgONO/JeDIF8QBMbPNzWsbDBMEmN8PT3Qchn30YD7t7HlrLisfjuO6aq3Hjxhtx4++/tyW9Hr3uxwXtBB9er9rH+6WDJn74qOb7MJ8XO61ywj3DJiaKFIfrfVF5Udv7XX77qKd9bt4WEEKajvdeLlTnSBZ2WFI8Hrd7SA76OC3DAf99wJe2WiXizUqpLRccXx7+33d8F3f234n7778fmUwGifYErrzySty48UZs2rQJoXAIBI7TNRaL4a3rL7Pnf8/7bvQtNx7Pt1asmE6ZaaVcwcDeA/ZryXTb/uS6C3n16tWYSI9idHSkpQFOls4X9B57owRVYoN7/3ANYzkuZCnhXy48WV9LP4HI6t1Y5R4W9fX1gRCCapXZDCcTaPNliq/eXcW/ba1h+8tuy1woHMLnbr0VJMCOcUdbAH/zV7fZx5x3Wk5V2n4sqOmsJ6d3jFQ1avd4beZeDamN19VW/U7i0+mtnrFh7ro2Ml7E5s1OOBovuE/ltvXDOu+nI4A1M1zNhHQ6jZFR1j6i6ONkNczJx/FcwJs6P8Zdt8bzjohMwI6n9+HZtIXlQz9FQGK2X40qAChLDx97HMDs+5gK5i5z868TgUAgEAgEgpMMSSLoiE79p1cqLmF5t4RFKUtgcf8hHwqQpunXrXRjzQUCirs8mMcv0KGvQ8Lizun9eRv22YexkHvfqj4CZqv2sddVsrCdojsh4ZNXBezy4UdfNXD/C413zbxoWeZuZLNF4HDaxFjOtAUPAhN/89dfdiU7yz67qBXp9ceKZmOAL/eWwr2+PSSHMo2iVjhAfF09Ca5f4lS9ZTWDCTglThzZt+dVWzhNtCWQy+bQ3dONDRs2IBRmpYve8RPn0qLXvPkKX0F8tMWi5VSBHpaTbffeQ/Z7m3/wHTv9HGC9CWOxGDZv3oz//Se34t3vfhduu+22loTwAMxhCbgF90KhgD/+X59GeWgHAECnCoaZmRXxEHvoYMGLydVJxD9fp2XJEi2d9yyBdnx8HIcPH3YJtKZJXYLN/lHTFph2DTU23aNSCJZ7b2F3Am3xiP0ZX4p8IkQqyw3nFessEX8i67PD6oQDBPHQsXFa8oZzq2esTLgdJIex/3Dafsm7QSc7/s2w9oO3TNuPZi0kZkqt/l1FHyEdAPK7fgTs/wGgF1ryfa0mXaCucny+92mx6pS8W6Xh3t7K03ZaVo4gWH9wYVIZBiUACKBNAIBviJZgfnNMe1oKBAKBQCAQCBwSkamnAYBl3Y4FwStaRpoILoB/wMp8JqA07z+ZihMMZdjPMgHOWChjwQzcgqxfpLt/ZThAEAoQuwySdwnZomUL9nGhUEB+YgRQu+33Bp5/GJW1V+D0vhA+tF7FfzzEbsp+9bSOt5yluPpb8jfSfAnpRJHiNACvHDZRqjBlytSr+Na3vtUQHMJvfXuE+DpP5wrhIGDXUHN0J5wyfynaC23I3UOyI5nEE4NsK7vaiC2gMKdl4/byIS/Z4tRCRLnGEqwtqF6BqiYANIbvWHgFY/64NuthmClSV3/Fo8VykRWLZfzywadh5A4i1dlh9wG1nGyR866259n76tN2+jm/HbFYDB/76IdxYNTEeUtbZ/Um9eXz+6e/vx/PPL0D7eddCLPnMtf0bWGCGCeY8Q7KyXpD+jktnfJw5zNLoPVLDz84ZiIeJmhnweWulgHpQuPy+c+jIeJynPEO8uMtWvLiq6a7r72W+3Tbo08BuMR3/pDKtkeVnWtUq5yWfP9Dqy+oUXXEO6JE0Na1zH5drvKi5cy/z9oP03HtZUsUoQB1PfSYDfoUomVx5FUg8CQw8gBw8TcB5egfDrSSTJGiPULs4CLe7VqsUlsItnpXEkKgysTe19MWLUMLEJCdg6qZKmRZA9QOANNzxwrmF0K0FAgEAoFAIDhOTDcZk8cr2oUCaFoafbL1tIyHSVNnY0eMgID1uzt/qexyWU0Hr0hnCR4upyUvWipM5mvFPu7v70eldDHkhCNaDu97Gtu2S9iwYQNWr1Tw6mETj+8yUNGAZ/YaWHOG82e71dvMNKlL2MgUnR5uY5kygDAI1bBw4UI7OOT73/8+QqEQfve6ivZkF9avW4/u3ujRb9QxpFl5uCIT9CQIhjIUUmQB8vkiKDXsHpLnXrwOvzjIpuWTw1kgVuPyElx5+FROS4AJxpbTksAEgdE0fMeiQbScIi1aN5hgWaxQBHzcxbPBNJmb8ovfexXl8MXQRwzUfv19uw+o5WQjStgWts3KqEuA5bdDlhyRsVX4OS1Z2noNavUAqnrBJdrEw8TVfoMvsZ3caeknWtbn84gfsVjMJ3SH4vUxE6t6JVjuyRJnRkz7uGT574yF3AJ6gNve2TgEjwZe7PGGmVhC7vhEyZnI1ADJWeFwgCCosN9RWn0fziRcZTL48Wb1Bd19cAz1OCO0dXRjzfqz7Wn46+JsyuztIJ5pCGBVDRjJmkhEpv/Lweq9y2Nd1/mxq8oGNIMtN6+HAJhAeQg49FNg2Yen/X3Hg0yRQks6bmHeaVmqOkKw4rl2OAL3NK9vi25AUHnVfqmZCkLhOAvjgTulXHByIERLgUAgEAgEgjmMNxzFKm2tlCt2kq+VlqzKc1t8mil8j0EvqkywuFPCkk5pVg60iKe/aNQWLbmScJ/y8FY4hwYHB0H1M13vGYUDSI8vt1+/+SwFj9dDDH77qlu0pGA3uCZ1CxsTnDtQM9m6E8ru3oPBIAzDwDe/+U3k83lIXZdCUiLYuWMHfvxvfwIgfvQbdozwC2Oy6O2QMJQxAKLguvd9FFrusH0+7BpRATAlsI8rJQ8HCEyfPnQup+V0REvNcXSFAxL6Vq3CwMCALUjw4TvOtrjHalvY+dkvydrunVl1J2cfDboJPLxtG0qBS1mPzdR5CHJ9QJ2E6/qON3UQrYjOVIe9DF5EUiRHZGwVEmEPCXhRt6+vD4Qa0LUq5NwLMJJr7M/aIsxJG1SZiMQLg9PtadkRJZgoUuTLLD2+Ng2H3uE0E5X5aXknZbHKzlE+DIsvSY8GmzstT6Ro6S2LtoS0cDwF1Nef1MZAQ732NKEAu36GVGI7MwMtGhgSN95C4RA2bdqEXz/0KO47zN5btuIsmFII5RpFOEBc5eGzcd5ZrsfaNMrDKxpFvgKs6p16WotilQnWft/JOy07wyUMFdi1uaRbFwsCVI5M/8uOAzWdolil7tYlLtGS2seBfxCpyADq5860nZZKDMHk6cAIe/kavRbPvL4Kl4VVnLNYOC1PRoRoKRAIBAKBQDCH8favDAeAcqmI22//BwwM7HWlJW/+l01AbO6KTzNlKufFygWzVxCTMQKZsNAJYDpOS/Z/K3pa9vX1Aa9yykV1HMSsulx5y7oI+pIEg2mKfSMmBtMm+pLOnbt1c+jntAQAWv8znxpOcEi1WsXw8DAWL14MJdWOmhnEa88+hJ/8z8IGB9lcYrLS9d4OAuxjP684Zy0uOs05aIMTmnu6OuEAUNMbl5mYcXk4tW/MQwGCTZs2NTxIsHpZWsTD7mXwwny+0ujAssrPm5WMzgbDoDgyVgaR68q9HIISjIHmckiPp3H99ddj546dOAgVEgBqVHDeeWdhw1WXw5JVeRFJlt09B1uBtXy+x+PGjRvxyy1b8OyhPMiBRxDmREvLaR0NElQ16hIjm4l/VY2ixLnauhNMtDRM5gyrTuHYMkyK10et4Cu3QMOTLlDX+Cu6nJbudh9Bl9Ny0q9vOXwptFvAdBLFV515Hh4frb+fPwSFFy1VgoDKxEuLVjktveMrFA7h2qvfivu+x66jNYMNmPE8xaIUQZk7rrNzWlLX/5NR1dm+y5Uo2qZZIp4vU1c7gw0bNuClA2yn86J3KqpjqF4Fn9esiwcFQgum9T3HC+shD9tfbLv4302lmvM7i/+9ygv2/APDqQiqCgC2v/7r2VXQDGDfaA1/tzEsgnhOQk6yzkcCgUAgEAgEJxeKTGzxLqAQyBJBf38/dr38fENa8k/u+vEJXtv5gywRpNqcP4Vt0ZITyNw9LUl9vqP/7o0bNyIRd+7s9fz+BlceIQRrOXflo6+57SO6ycImDM6c5xItCZvX0Mp2cEhPTw9CoRCCwSBg6lBlCiO3F4ODg0e/UceQ4CTCBx/GMzThdioOccnhybCGrVu34kd3bsYd3/8OapXGMIu2iFMQ61ceXilXsHXrVtzZfye2bt2KdLZk35iHVCakbNiwATduvNEVvsPj7XvHi5bFKm1IK7ccY8VqC0VLCihxty1MI1G7nN1ysoWi7QCAaEjG//vGP6Gds4Xy2r0iuUXMVmCJVLyQEYvF0L95M27a+Pu46rLTEZEd4d9aNcu9VqzCdtM262lZqlG7J2xQcR+bXLl5SrbFkYzTA5IvJfemP3v7WhY8oiX/cMbltDzOjrHG8nBGkXu+olFHVV3e57b+8k5Li2ORHu68R+xrg3WejOVNmCZ1Cc6zSg+3nJZTHAPTdATd0dwUCVcclhPVYuXKleheuBKA24nLu5uLehSABIR7gUU3TPu7jgfW7x7eacmLxeWaE8TDC9mq5FQ4TFfwBdzivrXcTJF9p26yBwqCkwfhtBQIBAKBQCCY40QCQLbslMmy8uJyQ1ry8NDhE7iW84/uNoKRevqw1Q9v6vTwo//eWCyGN7/pYmx9nr0+b0USn3jHpgaR65KVMu5+SoNuAE8NGHj3amqvk2bQhhtfqzzcNB0xc0FXEm/75CfR19eHSqWCr371q6hWq1CiOvTMLhCYzPk5h5EkgoBCfB00vINtcML9uSViSgTo//43sHvgVZi1LO4Zfxq/3LIFt3zhv137XJYI4mHW09BbHm4lavPl37978hHUev8CAFzlv5Ph7b3aHnXUmGKFlVfyQg9fHt4KrL55vcvOt8srAaCkB13CeSgcgknYtMlEBIm2KPQ8BUAhEXZMLFigRmvWz0KSmIDsFaxjsRje9Y5rUKpRRJ7S8OvnmKrUURd/2cMHCkqBchWIhpgA59dDsFwFyprllIVLNMmVadP5LHhHZc0j0PB4RcsjGed1POwui+V7WtaOe3k4116CE5/47eR/vvIt5+P7DzqKIBMs3X2YWxUe1awddCjAxF1rn2eKrFSbZzblwnZ6+BTz8m7C0RzFimkaIP16qVZ1ChgVFHNpAEys7OQKJ0qBlcDSDzLBcg6G8ACekDi+r2jNca2q3vJwAD2JmY2TZtfbibqruao1toARzF+EaCkQCAQCgUAwxwkHCbJlp79bX18fYFagaRpkWUYmm0GlUsGePbtQKBQQi82tG5q5SipOoEjsJsoq+7acrZpBfcvDWyXOtMdUWM283rrmDITCjQuOhgguOk3Gjt0GilXgyd0G3nwmWxHdaLzxzZYoTEpdN459vZ247XO3AWCp5Q888AB27NgBFF4CqmNYvXo1Nm7c2JqNOoaEVH/xoTtBIEuAYbqdlqZJMVwXhyJyCbsHXkU8HkeAmtCRws4dO/DbRx7Clb/3dtfyElGWHp8rsWVY4pyVqB2Px+2gndf2DSPa66zfVESCjcFSbqclXM5ZwBGJNIO5Ao825d0yIE2UZADOQLn0Lb+Hj1xzBg5kVPzy/iouWCrZrqlQgPVdtNxNsk8tuNoiccpCIkzwkvy+q36qXHm+gn0jJhQJdnJ5NMTvT4poiICCCSjeULNSjdqicDhAPP1FadP5LPh0ZG8pLA8vWpomxQsH2I4NKMDSLsnlPONF2qruH9hyrGjmtOT7Q/KuVSv8yO53GQACx8ppye0C/gFGJECQLVG7HNykwOG0+yTSDArDpL7jthmW41k3Jheuq579VKxSRIOTf49u0IYx8n//379g92AV715tolh6ByzRMlnYCmADW37kImDZZdPehuOFbjjtGHSunL7KbWO5Ru1rj+JTHt7TPjOrdrDJ9ZaJlmwsC9Hy5EGIlgKBQCAQCARzHOum2fp/48aNuHvrU3hhXxbFYhGGbkCWTDz4wOO46aabsHnzZiFcTgNZIkjFJZgeoSioMseItzxckWaXAO/HhgtV/GKnhq42gtP7mt+wrT+biZYA8IsdGi4+TUYkSKAbjSWGhgkUyu5SXV4EicVi2Lx5M/r7+zE4OIi+vj5s3LhxXoyVYD293YsssQTxwQmKkawjTozlHfE2SDOglEJVVZBKBcFgEJRSTIyz5nx8qFXNeAuAFEzKekwmImwZVqI2724m3N13aJKwIIuET8I9X5JcrFBXTzjALRIVq6oUCTwAANfPSURBVEcvWlpC5HDWvS/7lp6NUFjFz39dxb4RE7uPOCdFSGXj3xJm/bSfaYdoTBNZai4EW7s9FiL43HWOMiERd7hJoULRnWA/VzTakEJf4ErAw16nZT0ku1JrnM+izDkheZGv7O1pySWI7xs17fLfsxZJCCqe8nBO8NMNJp612sXaDD50hhcw+R6cvNMyEmAlvUU7jIo5bl09LVu07rx4nYgQ5EpMMAwH2XWhqsM+90eyjWXaFQ2udPnJoNQp+6dgvwuajW9v6Xl5GqJlrd4Dk38oohsAivuAWhxFjQ3isFxGFKP2fN5eqXOFbInaV2bemcr3kuX3Ez8mZImdx3x/z+nQrP+lVW3AzsfjI/YLjj1CtBQIBAKBQCCY41g3zdb/sVgM3/rmP+HTn/9XPPnkk4jH4+hoC8McPYQdO3agv79/TgerzCW6E6RB/AurBJUadd0oB5TWhPBYXLhMxlc/GJpSCF3eI+Pi5TKe3mugUAF++TsN778sUHdaNk6fKVJXCbJX+InFYvNybDRz1gCsr+XghAHDBEayrDyQ72eZimo4SAg0TUPQKKNarYIQgu6ujoay78DKJNTedQCYa8cSFa1EbU3TbKcl4Uo0pxMi4S0NB4B4iJW+UoqG9N2aTl1BIMUqkDrKnC3L7TTiES0nCsxR5u0LCjCnpSxxTksfIWqq0KyZQggQbCIWqk0E7EiQuAQj/vzw62vIOyCZ09JdHg4w0bijYc7GZZqUHa+AQiYtD39+v7N/z18qNzhU3b36GtsFHEtc5eGc+MSHFfEOwUiQCYjWuRYNsgc7LqdlCx24EmH7OR5i61fV3S7Yco2J1n7tDGva1GKihTc5fTLRsuI51tPpQ2oJwjXdEXh1A4BRAtCGQj0lPKaUEZadHV6qTe76nBK9ABz6KUseDy1oWZk530pD4y4fvAuV30vutitkxqXhQPM+x45oOeNFCuYwQrQUCAQCgUAgmON4RUsASHXE0Nvbi1AohGRHEsQoQq07yOZ6sMpcIhUjDSEPoQCgVtw3UqrSmn6WForcXOjhnX/JVBLXvmEdXjzAbsS2vaQjt/c3OH9hGaddeG3DvBNF6nI6BWaQyDqXmTJBvM7QBCsP5MW3S89fiuLAKgwMDCA9ug9Ez2P16tW49uqr8JMtXNm3rKNqTtg32PtGTCzrZoNj/br12Lljp6un5dKzL7J9UJOJqhbeEB6AOchiISBfZoEnfHm4N0DGrw/eTNENVsI5nveIlkWKXNld5mwRUlEvD6+HUR0XpyVBWPXf3mYiXjTodmzxDkGvuEQpRbrIuUmbiJbNkqd1gzYkS1c1th+8x80lWr7OFDFCgPOWyA37je8BqRmN7QKOJd7EcIC5F5slo0eC7n0WDbLxrErOgh57dDveceGbWuLmliUC02APZco1FpYV4a4LpSpt6tibSaiRbgCPv6bjkVd0XHOxiotOk5sKnl4xvFlSPY+1byuaR7SUIzAoUDaYaBlVywhxYVOVGhsPs/o9pBeApz8DlIfAHIgUGHkAuPibRy1c8g8HtGkEIHnbrrTPIIDHgv8dp8iOg9w610SC+MmFEC0FAoFAIBAI5jje8nAACKtu91eA6LaDbK4Hq8wlJIkgGXPfNIVUAkV23/SocmvDRpqlkHudfxQUW7ZsQc/5N+EwzgcFwVODPdj+sz/H8jeOAqmPuubPFClS8eZOy/nKdBPEBydMXAzZ5bRc2hPEpk2bsG37NgRyZ2DxwgXYuHEjDmfDrrJvUpuAWj0IS3PafcTAFeeyL7YStXkxecGqdfjWA2zaZiXEFrKndJknHmZuX6/T0lsOWmyBaGmYTLD0utEmitRVVhtUHdGhI8pKmKV6MI+f07LVoqVEmodtWKKNV9z/yHvWI8YN+BznoPaKsRXN7SAMB9xO2Fxp8uRxP3G3ojFBzSt0ZkqsbcFYjtpl+St6pHpyuHsZvPitG2hoF3As4d2VJmXfXdXcLjlrTBKwdeV7ssZCBIVCAY89fC+QuAYA8PO7/gsHHvt/LWlZYhkMrfMFAMJcyXelybFin03/3NEM4KdPaihWmbP9A2ub937wHuvpJJVb4nCVK2HWTApET0OJON8VVcoIhyP263KNzl60PPTTumDJPxUZYu8v+/AsFujA9zxtFsTDE+DWPxkjswprioWca/6a02U88gr74glbtJzxIgVzGCFaCgQCgUAgEMxxAgpBUHH/cS9JBFe9zXF/meURmJnxeROsMpcJBeAKx7CYSpiaCbJEQACUPcKLVqvZzj9ZljF0ZAgHXj+AwaF/Ruot/wg5nILSfiYSHSkcODyOUMq93ImiO4hnOmXL84HJ0rn7XE5LE5VyBa8dyAOIgcBEW6CKUDiM6665Gm856zp7WrVoOMJ/rYSgUYWePwiql0CUCAaGTFc5ZigcwoYNG+z5n9lnAHWJsyM6+X6Oh0nTss62EMEgKCoa8Pqogb4OFkDjLTMuVo+yPBTMqeXtZwmwm33+/Xe8UUWhTDGWp7j8XKakyRITL/2clq1snQBYQTz+n6ky8U1zf/HRH+P3Pvx1AEz4ee6VA1h/xkKEwqEGcalco679Gw4QREOwQ52sktdmzjk/EcwSrLxiDaXA8HgFP314L4DlAICz+5h45BVsFMkpg9YMCsNTqnysMM1G52hNbxTK7eCiIBBUJFyyUsYTu3QkogRnL5bQ39+PQ88/hOj5yyDRKtpqL2LHjqGWtCyRCbueBRRS72XpviaXJhEmZ+K0LJRNO1wo47meTrXcZs5cHssFWOPGiWEAkFQUez9ivxeNJRA6/aPAY9ayj8J5WzmCRvGb1N+fPZRS17ngbW/hh8Kd17NNl3/zmTJ6OwgIAa69WMVTuw1UNSFanqwI0VIgEAgEAoFgHuB1AwJAsi1su78qE/txzpL3zZtglblMSCW+pdutFC0BQKs1Ci/hcBgmNaGqKrK5LLSaBhBAJib0wiDkcAogMtRwEpAjDcvMFKmrRO9kcVpOth2dbayXnm4Cg2kTX7v9n5Dt+iMQCTBKw/jGP9+JTZs2oS0Zds0XUIhd9r3rpR0wSsNQ2qNIGkOoKCtQqABHMhQ9CeD+53UEVIJ1Z8l2r1NeoGiPstTkZqKFXz9L+zOuPHIkR7F/1MTyHrkhYdikwAsHTCRjBKk4mdV4ZH0/G5UPzQD2DTvv93UQnFl3mYY5wTiowjfRu9VIUvOWAKrin+b+uyfvw/7C3wNn/SUAYM+BNG6//YfYtGkTKkH3sS9V3S7KsApIhKA9QjBeoHZvvJk4LS2Rxk/Q/Pfv/xjD8sWQ68FAT/76+7j87D+EmnSfw4QQBBS2fM1wUqy9aAZtaR9RP5FH02GH7FhYTstIgAUz9SUl/N3GEAgBwgEJg4ODoFoeocObQWpjkFXSspYlRAJi9TERsVumOJ83O1bA9ByQFhmuR2OxClQ1E4C/Nb6hPHwGPS35no9XXfcBjBdMjJYDsB6ExNoXQFYDCChl1HR3AveMCS1AYx9YWn9/9lQ0dw9RV3p4U6fl0Y/bzjYJf/lex7qejBIMZdh5SykV5eEnGUK0FAgEAoFAIJgH+ImWoQCx3V9LOiWsXHCcEhtOckIB/7554WkkRM+EbQ8/2CC8jI2NAQA0TYOhG6CUQiISFEWBqRfteWsIgajRhmV6nZaTORTnE0HV7sTWgCwR9LQTHE6zEufS4QLCPUzllGqjGBgYwLbt2/Dh97/dNZ8qO2XfTz/2K+TGD4AkzoTZuQL3PM2mGRgy8cIBip/tYEpDoUJx3RvYsiueUJJkjKWY+9E2Sd82PlW8WKE4MGaiOyH5pgWP5U2M5YHgKMFlZ8gzdl1aYUUWfUmCwTR7/dqgo5B1c+EYfDl4SGUC8bGGlYf7f6bKfmnuMsqFCYwc2o3UGRogqZAjnRjYyY79NVdvcC3D67S0BNKOGBMtS1XHZcmnPFv4idPpbAn3//xOjKavA+B+cDSYCyCwdHl95mHse3UHtm1fibM/9PaG5ah10VLX3c41C0opMkWKrrZjK1rWdGo7Dq3v5Z2WoQBBvuLsG1kC+vr6QKjBWpaAtrRliUyAWF17tnoaenta8vzqGQ3pPMX1l6ooBacvYvFp74bpJMl78etrWpuW05L9z4t6oWgH2mSK/RnngFt9NEMqm8fqaTkrFt3AeljyPS3Dvez9o8DrBtdN53xpJhz6VTHMFO9zk44YEy01gwnNrf5dLTixCNFSIBAIBAKBYB7Q4Sdacjf1x0NIOFVQZX8XW2Sa6bPTJT0+5hFeVKiqimg0inw+j0qlwhKtgwGkOlMoGGV73mJVQs+ypch5lpktUldvutBJcvPGHGjE5U7i6e2QcDhtgEKCnDzffl/WRlnoyni6wa1p3TyHwiF88Mb3oC1C8Ox+A/tHTdzzNFNrXj5kYP+IoxT86mkdp3VLOGex7CodjgWlumjZuG5dcQld8eZjp40z2xWrzE316mEDZR/R0qKqU4zk6IyTd73l4Wf2SRhMM6HECrGQiIH7fv4jpDqTWL9uPTqijksxqOC4tFgMKP5uZ4Cdnw1p7tUSTNMEqAGiZUCDXUAgaR97g0v3BpjAxYvOlsjB92jMFCl62gkqGhOleRp6ZJYr+Opf3YYXf3snYu96OyTPeacuvJJZBQHI+Rft9fJL1w4qBHkwAUY3GsdAuebux9kK/ASmmu4WArP5iu2qK+fTgA4AjttNkYCNGzfiJ795Ga/sHYVRPAKaa13LElmCHfzDegwTlzj12JPPoPB6GuvXrceRfAD37GQXws42grdfpE67tYLlsm322sLPVambU7tgLaGTv36MjqVRMygmim32e5YDOxwgyJWZyG6as+xxqsRY6E6L08P9xqFuAgGpeYl2K/rfNoiW3HmbLlC0hYXT8mRCiJYCgUAgEAgE8wC/3k8dUYL99fjiVveUO9WJhRvfa7V7o6vTI7xoGiRJwrXXXgM1EMDI8Ah27NyBbCaLbCYLtd25Q/y9a9+DQNdFeOBF9zKZ05IvDz95xkVIbV5+ySeIq4t+z/6ZFgdBCBO5vK5T/pwKB52esYtTrPS1ogHPv+62NlEAdzxUw59fH3QJV7Ewe7DgdYMmYwTnLJYmLaluczkt2f98iEwzDqdN9CRm9rTCMJ3AnViIlfcCbjufXhjC1u33gRCCnTt24u9u2wQgDoC5H49HorVXJORR5cY0d+h5LF++HONUAq2mgWAXIAVA1DiSqSQAYDRHsTDJ9nU6W8bzL+0FcBoAIChpABSX+PHrbTugFPfi+UUUH//we1xtNyayJWzd6u5F+8pre5FMdUJXmJBHqQlSFyqJWp+XmqCjT9pj0s/RbTlMazr13dcVjTY43I6WZk5L63sq5Qq+8S/fAbo+AQA4cngv/uxPvow/+vw3EAqz7VVkIBaL4St/99f40S+2oTqxH2cvCbSsZQkh7jYLkQCgwDkJX9v9Ol568JfYuWMn1r7rVvv94Qx7EFDRpncN58vDAWC84D/gm/U7rWrNE+6BRqclpRT33fND9t6SP7SnW5Bg1xNrnauaO517xiixow7d8eI3DjWDCZPNRcuj/50keS57/EPdiQLFks7Wt1AQnDiEaCkQCAQCgUAwT+mISehOMBGilcnWAiAearzZkVvcy+/qDVfi0Sefc/W0XLVqFa688ipbCHjf+95nB/WMq6fj5Qyb9+zzL8Urhw1YglN7lNihERnOGXQylckFVAI0EfMuPk3GvU/rMEyAKI7iXEzvw6pVq7B+3fqGfcGfM5GAk+QsSwTLeyS8fMgtViztInh9lJXM/vBRDZ2ce5IlQRO0hQmy9XVsjxCct0Sesgdke9S5A/f2EJyMTJGiUKGI+YzVZuTLFNl6uWtPQnI5Cy0kLY1UKgVN0zAwMIDfbP0VLj3z/QBYSXCrBTM/JhM2FLkxzX1RdwSf/MCb8eHPfg17iiNQ284AACxddSHWr1sPABgYMphArBfwN1/5GvZr50JdxETLn9/1nzjrjz+Kjphze/zwb5+BPvw47s3twkNbf2onYBcKBXz5r7+KXbv3u3rRUqIiEIpAl5jiSitjIOFu17rrEy+hlj5oj0m/7YyGmPRdrvmLVKwfZ2uPgV/YTK7s9FDctn0b9h8cRriLvVaJjueeeQrbtm+zw6nk+jCOxyLYsGEDFiUlnN7Xul9MkQBpeNDwykvPALiIvY4lgXgcAwMDiLx8AMASAMB43sDWrVvx6x+/hOWLOqYUUbMe0TJT9J+uWVp5TZ/cDal5elryZeJDXHuJBR0Sgiqpty6goAAKFcA9ok4sfvtA0wFNad5/sxV/q/iVh1tYjvGpxGPB/EGIlgKBQCAQCATzmNN7JUwUKBTxx3lLOR7O1Xg04hJekilWjmsJloA7sfq3r+h4+bfsDrdQoa7SvAXtxBYrR3KNvfpOBiYL4+lpl/DZawL4/oM1W5QjMPH+d12BK9avQygcanBa8je04YD7RnhVr1u07AxOoK/4GI5IV6JqBvHaoInIac4CrP5zyTgTLYMKwblL5GkJ3Xy/y5mIlgBweNzEGQsbT/5mpbCHxt19K/1Sz2U9DYC1K6CUYnxs2P4sqAKafmLHlCITSMR9bvR1SFiwQMbf/NWX8JX+vXgly6a95t032eeTSYEXDxjY8eBdGBjYjeiZa21X7P5dz2H79ofRfcZV9veE4gug1jowUQrjwQcfxC233IJ///d/x+bN/di1e79vL9qq5FhEaTUNOZKCQZ3jc15vCadfeKN9nvv197NEaJPC1VPSoqLRpoLZbPH2ZgRYqwmL9Hga4B4GSLQM09DY+3Wsa6YlXnrdcEdLwjNWQypQzI3Zr6kUQqA+ZjNF58v3HprAc0/cCT3zGlAexJYtW2wB2g+vy9naD6ZJUdacc71Z6NZkx4ZPaTfrLQt49/iRDDs/42E2DkIB94On6Tiwjyd+ie2aQVHVJnvo0AKnpWcRSW5sTBx5DegagbZoDRCKH/V3CU48QrQUCAQCgUAgmMcEFIIVPZIoD5+HSJJbeJkKvqdmqUZd/eZywwMAVgAARrmehSeT03KqUvdVvTI+f30Idzxcw6uHTVy6SsXbL3dKxb2ipyQxdyQhnOBCAIMCS5Pu2sZDO3+A1zOvIHjmAihdb4BuAIfGHAEwXtdzkjEJr4+aOHeJPO0yyA4uT8kqD58uRzImli+QGsogCxVnnVzbkXbGRk+C+PbKNUtMpNQ0DYQQLOjutD8LKgQl6cQLJ6rs7m9q9clLxCN408Wn45WHmLifq7lvd8saxcuHWMIwUcO2aGnWMhgbGcRE7QkAF9cX2o7h4WEUcxWUSyX8/Oc/Rz6fxxlnX9i0F212bNyO4ImGFCTaZAzXBdS2MPDpjetdQrZffz/e5Z0rN5Yml6tMMJtuj8bpoPmU8vI6ZjKVhKQM2a/NWg6E6nbpPeCIldZYbHXIPN9GAWDXw65kDDhsrUDIHrOa3GZ3PdBJFLF4HMHIMuiZKnbs2IH+/n7cfPPNvt+T8zgtsyWKFw8aSOdNUAq8YYWCWIg0Tcdu1ncXaHS01nSnzLymUeTqIvWCdrYzwypxXfcKlRN/7vH4OX51Y3IncEuclpK7FUdH2FGKM7kKMPoQas/+Enjz3x11307BiUeIlgKBQCAQCATznL6kBKNZLZZgzjLT8CS+z1+pCpSqjqCx79UnEVzJREurZyFwcvW0DEzitLRoixB89pogMkWKBBdwo8rEV9hXZbdwpCoExVwZd/3gn0GTHwdRY6hl9qI4/Ax6e3uh6WP2jfJQxjnn4iF2MNvCwFkLZSQmSQv30n4UTkuDAoUybRAfsyXq6v9nMcq5cJNxJoiEA7BToQGgPHEAhdy43a7g3e+6zv4spM6N0C9Fdvc3tYQyVWHbZcEnQVskkt0ghMAkvCsyiycfewRZ7RlELmWiZcUMolKpQFIjkGUZbW1t2LFjB0JtvU170YYCMn56gC3znLNWoqxJGK6fj2vOUFyCJeHWm4cXm/Plho9R1lipcLk2ee/PmWCJaYZJUdPREES2ft16bHuhhEz9dSU/jjdefIFdeg84oqV8jMaHNwgtHCB467o1uO9O9lozFVTzeaxatQoZ4gTaEDkANdQGVAoIBoOglGJwcLDp93j3eaHKWrBUyhVs274NP0wP44IlJi54ywfABxFZNBMzgcY+j1WN2u/lyhSo77sF7Wxbg6rXadl82cebSo0rATcqwNjjgDYBXZdh9DZ/ENeK9HCAieKWsN5eeRLApQCAdC0OgKJWSrPgoSZ9PFsp+guOLUK0FAgEAoFAIDgJaHW/RcGxR56h4yTCCQmlKkU6UwAQA0wNsYBmR1JkSv7zzHdmIsB6ezUGmwiequwWaFSZ9e/bPfAaYgt+gGr4dGRe+yXMmoZ8Po9Y2yj8NIlYXbsghKCnfWb7nBcci7NwUvmFE2VLFItSje/zQre13R0xgjLnwHz3NWtRzKyy2xUk2hwrqCSRpvvyeKIqALjSaUt4VmWCVMxRzcYLjfvTCvE5ABXWKZhsUzAxPoK27jhMUwckBVIwCdM0IclBRCIR9PT0YGhoCL0Ll2GVVsHAwABM04SmaWhvbwcAvHHNFbZoGQnJ6EoQvHzIhCIBa89wn/B+yeGAO2wm71MObKXKVzTaIOTNFk2nqGgUf3NXFfkyxeeuC2JZt7MfQ+EQ1r/1avz8d+z15W9+A/7vH/8Bdr4eskV8q0WJ9f+x/pUUDgCxWBiKXIZuAPH2btyw8UZc+qZ1+D8/9mwfogjIIVSrVRBC0NfX13S5XjdjsUJRKVdw++232/2H76kM46wH9+Bzf/IXrnYeQPOAHqAxpb2qOdNnyxSon2q9HXWnZYC4WnwUKschBWua2G5KowLs+XeglgZAoOmDMEd3APgL3/laFY4jSQRGXbUMmGnElCIKehQTVSZY14wAS0pvQqkGRFsk+guOLUK0FAgEAoFAIBAITgAzdSR5nZZVq7egWQXRMr7zBE+q8vDZzxtuIngGFOJyMqkK699HKUWgdhjlsZdhViZgUhOGbsAoHGpYRlABlKNIxHU7LWc+v9fZVdPdrQN4eEeltT87ogSDddEyEgTe8fa3uhxIXmdlq9x9RwMTPhrLwwMK0BZh66yb/k5LK8TnSz8qIK8BMjFw5RVvxo9/9msEVAVVPQsaSEEOpyArMtraOpBqXwlNY4LXgoVL8XvvuQ733/8bbNlyLzKZDIqlIu666yfY+eIhoPOjAICgSvB7FyiIhggWpwg629w70q80HHCXQXtdf1WN2u6ycgv7WmoGMDBk2iEmT+8zXKIlAGhUAcAU8iveshrxeBCKrNs9Gq0HZ7ZoeYxVy4DC2jtEAsyBGIy0Y8OGDfW+re4TqWwEkZ8owBgfx+rVq7Fx40bfZVJKG0XLKnuQMTAwwPUxbcNruw+6gogsKjNwWlY01tPy3Td+Cnc9XsOeF5nltbf+4CMUAMLcda8ww/YRx5KSNf7GHq8LlhQAhWbK0ArZpvO1KhzHNbzUDnQEcyjoUWRqcRiUoGqqQGhB0/kLFWr3JxXMbYRoKRAIBAKBQCAQnAAU7q5LJu4ecn64elpWKShR2X2iUQUtD8OYeBlyx9mueU6q8nAFdh+z9ghBtkQx2S6zyjnT42mctiCI0/7g2obwDVXxOi0JkqmkXf4bj8VRKBRQrVRRqVSA8QOImjVQyVE6gyo7frMlHJQQUJig4XVa8tvgF9QENDq7anpz4YQXuqxgIj6MpztBGkomvWX10+3VeSwJeIQPy7WoKoBEWK/O0RxFuuDf+zEUDkEOANAooiEZy5YsBDE1aJoGaDkgkIKkRrFwyXLkJ8ZwZDQDYpSwevVqXLnhOuRqIaiBAMrlMjo7O+0y8QNDEwjVW4AGFRaE9bbz/G+5m4k3vNOyUHU761xl/C1MENcM99jz9nUE4BLCLaFdlZ3ScttpWb+uHY9RElJZiniuTFGuMUfk/dtfBHCOa7rVa69CotaHC5dchY988PebhvBoBlDw9BHNlQyEc+mGPqaUUlcQkYVXmJzss5oOZHIlbN26DU8fPgNADwCWHA6wHrK803I2TuxjhT3+tAmA6zCpmTJKRmPZvEWrQgNdomXnGiRDe3GwCFBIyNbiiEbiwKIbms5frFAgMbPvNEwqqlpOAEK0FAgEAoFAIBAITgCW01KRgAuWKXh2nz6pcBkKOLeGpRqFCSacGVoJw4cOwNj/N4gufjPiZ3wAUiiJgAIs6ZwDDQhbBCEEQZUlR5+3VMbz+w1WUumDt5zTzO/Bkw/0N6QGBxS3GKzKTvmwNW8wGERPTw9Wv3E1unu68UwxgAPjzneFVHJUScmyxBKJazp19bT0bgMhBDt37MSmTZtcwqW3PLyms4RizaANpZhlbvm809KiJ9G4Ia0SGVqJtyyaLw8HgFSciZYVjbmSoz4aSqUuuoQDBL///vdg6/3b8eLreQSKI1CipwEA/tfn/hL7Xn0CJHsBli9qx8aNG7FrJATUqO3I5YUsEKeUfqoy+mbl4W0e5y0vuvJCZTOnZak6s7JxStlYyeQdpXvfoVFUyinXOBvmerim4m5XJeA4cq22F8ejXWA4yJyWABPqv3b717G/uBiB5W7RsnfpWXjX6vNx4TIZsVjzk3VwpIDDwxOA0mG/t+/AMFYujjX0MSWEuIKILDSDusQt/jz0lodnciV88ct/h5defAnB825FqKcHxKzUr+xhqIqTJg8A+TnktCxbZla1A7zrWacKynpzO3azcT9TCH8Y5RA6ek4D6tflicjlSC47DwU9ipiP4lXT6aS9R/1gLly4eiULjg8nz18xAoFAIBAIBALBPMISLRelJCQiBMt7JleHJEIQrt8L5sus/BVgKcWKrKC9I4Eu5SCUXV9D9ZVv4/LuJ103vCcD0SBw7hKZOSLjzbeNL+dMpVLoaIvYqcE8qkwQ8pSHW+XDN268EVdffTVuuukm/MPf/wM+9OEPYcOGDVjU6T5OocDRhY9IxBHVChV2c+y3DfF4HAMDA9i2fZtr/prHaWndjFd8RC3egWk5Lfn92J3wDyuaa8S4sBoWaMN+tsTLJNcnNO3T19I0qb0vwgGgIxHDv37zG7hx441YttARrAw5hg0bNuCWP9qET3ziE4jFYvZ8vCMXqKety47IF5zC5ex1i1q0cdtWrlKXO88tWvoL9vtHTbtkezpoBhPIH3zkKfu94fESbr/9dlTKTCXTDYo9w+yC0x4F+jocp6WFkx7O/j8ehrSw6nZK7953CMG23obpJupjoDRF+4Xv/+h+6NStNlcNNqhWrVqFfD6P8fFx5OuBP3wQEY81RqoaxZ4jjnPTmx6+5b77sWtgL6iWQ/kIaxiqFw5j+yPbATAhmC9hnmlQ17GkbF13OtcAgSTYmShBM1WU5OZl2a26nnjd7R2cOjkRuAiQQxj3aQ8BMIerV0CeCsNE07YbgmOLcFoKBAKBQCAQCAQnAFliN16LUuxuf3GnhNG8iUyx+Y1RJEBQqlLXNOGghFAohGQHc/0EFCA/9jS0XEezxcxbzlok2+XJqZiEfSP+wRReF1xQMX1Tg6MhdzCE9XMoHGroVWfRl5QAOOpDUCVHVTIo28IES+OtaExI83Py+ZWkep2WVrl4udaYIM4LXZYT8LwlMrrbdFR1iktWNioKxyoN+mjgxXhVcUraLUEkxYmWP7t3O07vqbpK63nxNhQgUGQmXG7YsAGhl3T86DE2wUT9PNs7YuLAmIlYmNj71+vIJYSg97RFsLr5Te209H8/EXZ2eFkDhrMUSzotp6UzXcVHtNQMlnS9oJ24hNvJ0AwmkGfyEpS6UVQKJDAwMID77/8N1EAA+0aAmr4OAHB6rwy1bqtUuN6iVhsBa7wcjft4uqgKcbtKpRBIsLOhbYR1HCcrqU8XTAyO5AHFbaUjShT5fAGbNm2aslWDRU1j/RIPp02MZE2s6pUgS6RBKBsey4FChhxsh3Uam8UhpMfTUGU2rnmX8IkQzXSDNrSIADjXthwCVtzipIdH4igrb266vGPS0xLuQDNLpB4vmFja1TgQmRt9Zt8nRMsThxAtBQKBQCAQCASCE4AsAwtTkqtH4FkLZTyxS2/aqzESBJAHTG6CcEDyLV3s7GwsXZzv8PuqLUIQUBqFAMDtglNVFdVy3jc12CvqadUitm6dXJhY2OGeJ3yUadqKDEQ5Ea5YoQgHSMM2NCtJ9W6/5czz9rVk7kI2LSGOeBAJEnzx/UGANoanEDT2tJwLqDJBWCUoa9QVaCNJBIoExIOOkvvsywfwuwcedJXW8+JDJMBCZAIKe48XP/iHA7rpfm05cnkhS0uuw73Pss+DU5TBNusNGufKw8s1isG0abd54EU33WTHnl/OcIYJ3/kykPRv29iApjOBHMoy500lCtOk2LLlXpTLZSiL3o5A/ePlXYZdFq7afSydWWWJgOD49LRUJHdAF1EjMNX6wxqjBkp1ECVii1iThRftHzXRluwCKblFLqKEkEimJn2Q4aWqM8f00ASFbgKjOYoF7aShp2VHMgVCCAzJEUppeQjJVI+9T2NB93g43uTKtEEA5wOhAAByCPuwHoFgFUuNx1AefgTAW3yX16qeuF5RvJ1rc5Gt92TNFv3bZFS1yXuP+qGbUzt1BccGIVoKBAKBQCAQCAQngIBCbAeVRThAkIiSpm7LSNCdmgwAi/q6QFatcjm+Vq1ahQ1XXXmsVn3OkIwRHMk07iu3C86AMXbENzWYv5ktFAr4o8/cghcOYNIeksxp6cAHZcwGWSKIudxUzjY8+btXcCDXjnz6BaDqX5JqUrd4ZTkvvQKHbjql4yGV9Qi1RF+JEF+VaS4KlhaxUKNoCbBjemD3swAuBABE2vuAQtzlHNw7IgFgbrBUnB1Pazm8+DHhU1rO4xWyfr5Dg5WwPaXTslkQDydgl6usf+1EwURHTGoQ3So1dwr50ARzHuea9Hr1QzMoE8gHOZWTSNARRCaTQWdnJ2jXubA8zeOvPwlFehvbhvp3e53GikyOS09L1dOTdtGy0zEW6AABYFZGEQgEoCOCTJHCpBSlJqJftsTc6xe9cS3uPdT4+RtW+wtwzajUKMbyQLX+QGFowsSCdqlBKLOuU8+9egjWcOjtkLF+3XpbGI6FedFyRqvREio1tj2uQCCP4/C1QQPf2FIDAcUXL3wBZS2CZqLlMQnigRMOBQCZEvufAkjnKXraG0VXbabl4QZQPAGisUCIlgKBQCAQCAQCwQkh2iQso7tNQqbo1K4RMGecSVl5uJegAt/SxWg03DDtyUYqRnAk0/g+74LLpYdx0dK3YuPGjU1TgwGgv78fz/zuKXQsf5vtbLR6SPLCVDxM0BYGcmX22u+YzBReqCrUBYFQOIT2Cz6JI4NAx6qrceWi55qWpFY1R7yybsa9oqVpwnZaWoJae5RgJNv8Rnwu9rO0iIWB0TwaXFSqAtTyw/ZrqnYgoKowTdN2DsrdaxA8vS5aRpmSJNddmnww0cQkrRr84JPcQ1P0tGxWHu7qaVk/hofSFLEwbehVWa5RO7gnX6bI19Ol8zMRLXUmnm3ZOwZeU0ukFqGY3g0lEEY1vBgAYJZHUckO2WK2te9lzzhRpOPT01KRWSsFi3VXvQ9372Q/93UG0d7RiVcHmWBfrACK5J8mf2CUSbIGggAa7XQ6aR4s40dVBzJpp3VFpkhRqVHkC2U8vM19nd60aRP++P98Hbn6A4VP/8ENCIVDjmjJi9gnQDQzTCZS8qJlvuyeZtcg21YKgn2FXkRlJzEopOio6M5gb115uPsBHh9glSs5748XKHra3fPWdMCgzUvf/TBMdgz58UMpRbnWGAwmaC1CtBQIBAKBQCAQCOYQXW0Eu4a41wkJNY0iU6IIyI2NuJ793RO4fvWahtLF4yEanGiScQL3rauD5YLriBJcdNrUtz2Dg4OgRm3KHpIAc1vmDrMb9dDM9AxfXInRdeGJUor9o/X3zATecvnvIdTk5riqOf0rmwXx8E5LKySmI0owkkVT5mJyuIUl5jQ6LYEFnWHQ1w0QSQYNdEDTNGiaZjsH0bHE7ko6uPdpAJfXl0UQC1EoMqAbsxEtnZ+nclo2K5P1c9aN503kSj59BTkRa3DCEckqGm0oHW+GZrBzJRhNQef0ukvf/DY8fM9eaIE+QGIbY2Z3IXlGctLycICNm+PS01ImriCegxMyrH6zZ6/oYc7GQfZ6osDOEatnrEVVoxjLs33XLOjGOienS6ZIXcuiAF47WMDXvna7yxFvObml2GLIZTZmFqTYQwlLTON7Wp4Ip6VuUhSrBKm4817esz/4yoCyHgJv224LVI+NaOkZXwGFIBxg+yjLi5b5RqHacqNrxvSvcbrJHhwykZK9V6yyPpfHSrT0E9hPReZgW2WBQCAQCAQCgeDUJagStHHCxeKUZJesjo0caJh+fPRwQ6I0YDlRTm5UmSARmXw7w9N0Qvb19YFQ3Z0G7dNDEnDSk4HWOC35bSjWhaN82d2XkhelvPBhPFZJakWjdhI5ABgGtacLKkxWmGrfzWXRMm6Llu5tCCgEl69fD8XMAwBMJYF8Po/29naoAZWJ0QHnmBqFQ9y8rGzecltOVR7uhT8Osy0PV2ViC7GWKGlSYO9w4/GvaExoe+F1A4fT7s+n67bUDArTpCh5BLGqGUA4HEZRcvrAdoWzWL9uvSNW1v/3hjXJ8vHpaanKQGeb80079zgPdVJxyeWaTdeFNW+a/NCEaT/04Hud8i0bijPsZegnfv7wZw9jYGAAsd6L0XbuHyDWuRwDAwN4aNt2SEvfi7Yz3ovutsZQqUiA2KLwiXBa6kajaFvwvOZFwrIeQkl3dl5b2P2gTW1VT0ufxVjXs2zJufZpBm1whlpu9Jn0tTTqpxc/RjJF6npQ0Up0g8647+bJinBaCgQCgUAgEAgEc4yuNgm5soFEmIlypkmwfxTQy/mGaWkth/R4owJyPJxOc4FkjCBTan4zP5V4ZLFx40Zs2bIFzxycAIVs9wb19pAErMR3djOeaEEVfruP03I05xahBtMUKxf4zz+RLWLLXXfi0OEjqLZdbJeR13Rn+0tVCkvDDKkEQZUgEgAq5UrTVOSjSUU/1oQCxCXwWagKcw6uWNyOXUMAUSK4/vc/Atko4K67fgJN02zRklITS3uC3LzMt9seJRjNUVQ0JhRNV/jmy8MnC+IhmDyQJBJkvUZ5Z53X3QawgJehCf/grlyZutxxzdB0oFQDqGchO555FbViEaEVZ9rvffZj1yAUDtkipVUernjGiSqR4+IQU2TgzD4JKxdI2H3Efb50thEUnSplW4DeP8LS1a2xPTThbDgvTnYnJBQqdQfmDJ2WfoyNT4BSwFy6EVCikNR20NGvY2isYu97XoBVbGGYIKgCerXRPX08sMrDndfUSQ6vw19/y0YQlPPGJdragHFn2mOVHg4w0fJIhol9lcOPIIwxQO1AsWcN2iLOyWAJjUy8nN441evaK39OZkuNPXVnA6Xs2swHoRUqbkfwqYwQLQUCgUAgEAgEgjlGVxvBnmFgcT01uC1CIBEgEVcBT7Uy1fJIplY0LMPrfjpZ6UpI2DvS3IU4VW9Bi1gshs2bN+Or334Aw2PZpunhAHDxchlP7TZQ1SjedMbR31IlorzTkgkAI7lGR5gflXIFm277P3jhsZ+AyhGo3a/ZZaelWtQuBc9xwksowG6IS6Uivn7732PXwD7f8KG53NMSYG44vyAeAFjSpWDXELMqrTh3HVZ2aXjuuecxMDCAsNLOktHNAm5459X2vMH6sniHXqbYXLQsVij+36+qyJYo/tfbgy7X1S9+2o9kqtN3DE3lNosEgEwRyJc03Nl/d9Ox6O1xyeN1lzVD83HSAUAw1ol4Vx+qieUAgJhSQk8q5UqUVzyOSwtZPj7tKWSJQJEJPnK5ir/9SdXlTO6MEwS49bJK/as6xcExE8u6ZaQLJsqc0MwH9XS3Eeytt0b1Ogung/dhQDweAwnEASUKADCDvSCEQI0tQHnf7wAAXRdcas9vuStliV3DilVqu6iPJ97xkS83tuPIcuXhJfU0UKlov26PKQDXLbVZL9eZ4vdQjneOZwafQTiSBkBRfPYB4Iq/BBTW07g6U6elXoBxcCtwREepHATarwSUGLIlpy3H0VDTmZDa5oTIo1CZ/sOSkx0hWgoEAoFAIBAIBHOMSJCgKy6hq+68kSVWMn7BOafjuUfc0y7ubfd1A54C1eEAWKBRNEia9qMLTdNpCTDh8t3vunbK9OWAQvDZa5hDLxk9enW4w+W0ZP97A3IGJ/zXadv2bXj51QGkUikEot2ohOJ2gNBFK68Bou7lAswFGAkQ9Pf347WXX0BbstcOH9r10g47fGgul4cDrK+l17FoCa2LU877h8ZNnLuEhTPd//Bvce8hJl4s621DIh61p7ME0I6YO0G8t8P/++9+SsOBMXZcntptoFKrB5KYOn71ix9DCnb4JtAnphA6Qipbpk4V3HffVhAC7NyxE5/5zGfw5FNP+rpivUy7PFynvqIcCbSBhhYAhO3QGEYApFzBJU56uHteRTp+Tm9FJkjFJbz/MhU/2Oaolqk4cQmnfN/FA2Mm+pISDqfd213inJZdbfyDhJmtU6Vcwe23u/tXLl++HItPO9sxHapxrDj9XPSddh4q938HANAZf5Nru9j/jlu6UmNOx+PpgDZMCoM6jmPvWNF06to/ZWkBECCwnOjJmHtdpxt8MxWTlYcDQE6LohdjAIBioQAc+imw7MOsHUJ9E7TGFtGN6AXg6c/AGCFAoQ/FUh6gd6N63r+gooVa4rQs19j5yvc2zpepawyeygjRUiAQCAQCgUAgmIOcuUhylVi2Rwna4wEA7hrBT9/8YcRj4QbX1anitASYwFAcbSJaztCtMh13Ie+iWtITxml/+PZJk8mnIig7trjXD4+iUk5hNOc+gINp0zeYIT2eBpUCCAaCMCXVFSDE98Djy4uDKhAOknr4UMUVPmSUjiCXZhYzb8DKXCMe9i8PB6wSfsbOl4Yw8cqjSKaSOPP8dbi33sayu112CbOWAJqKO/t4LO8/rnYPGXjsNUf1GMtRTORKACIgZg1dyShqUtw3gX5havIxWcqPAegEACS7+qBX83ht12v44pe+iFw25+uK9VLVKaoatZ22zdAMVorqxZSiMINd9uvOOLOl8WNClUndeeme93iK3YrMnGqXrpKxa8jEE7sMnL9UQkAh6OBOSb4/qW4Cu4ZMjOfd7mXeUdiVaGzZMF22bd+GgYEBxONx+2HA3r17sf4db8P4hDPd+z/8Gbx42BnAfHk4H3LEjiGFZrAWBMczrdqoD3HL+TdZP0uACXC8oOgVLY9VEA/gdqxnas7BL+phoHIEgNtdWZuOc/XQT4HyEHRzEQATJT0AlIeQ2fMrANe73L2zpapRZMsUi7n3/NpBnKoI0VIgEAgEAoFgjlMoFNDf34/BwUH09fVh48aNRyWQCOYHqseR0hElvqEvqUQYqtLoGpnD7QhbTndCwv7RxvJpAqfkd7pYfQ1d78kEZt1x5HVR0fIQdj7439i8efOszstCoYAvf2ET6MJ/BCEShkZzuP32O4Azb3VNV6yy0kyrhNASTnfv2Y2aDpSNMoKhgCtAiL+hLvFOS5Ul7fb19QFmDZqm2eIKMco4rZc16myVK+pYEQuRBhHEEh57EuwzzQAOpSkGdt4HQggWnZsF2t8BgIlEqku0ZP/zDqfRXKN4oBsU/b91qxVjeROaUZ+P1kCMEtTggoYE+rBKkIxNrgZTrQhLtKRyCKpaQa1aw/CRYXR3d9vHyk8Q5cmVKbqmJVo2bqMpRVAyYrDa6q29aCUAP4GSNDj/ZIkct+tPQAZKYAFKH1qn4uoLFVt0DigE0SA7d7xJ8CPZxuuFqzw84RyjQhMX9yuHDPzkCQ2rV8rYcKFj6U6Pp0EpdT0MoJS6yqgBIFNRMZZ3LtxdTXpa8m7xQsVJrz4eaPXdVKxQdLU1Oni9/YRLNeoqAecfAACt7GnJ7SuJCdF8f+EcJ1pWDBV6oBcKgBrXDmBa5eGVIwAIDMrGQ81UoZsystkskGDCp2lSVz/KmVLRgBy3H02TugJ/TnWEaCkQCAQCgUAwhykUCrjpppuwY8cO212zZcuWWQskgvlLIkIQ9akEjYeZ2FLylDCeSqJlLEQQVomrPx3AxLmZ3kz63VSHA0w8SBdog4tKzxewY8cO9Pf34+abb57xuvf39+OFZ3ci3p0DAu2QIwsw8NwexBfo8N6ubf7J/VjebeLSSy7FN7/5TQwMDMA0TWgGsOf1vUgu6YASj9sBQhWX09IRaUIqS1XfuHEj7t76O7y0P29fXy6+8Gx84oPvwHOHWycwHCtiocZja62zJBFE5RwyRhukcDeSXb3Qq0UcmdAQaGfTdMaJS5i1BE+3aNkobj3wgo4jGfdYG89TmKirS0YVxCj7JtD3dkw9HuNhCcNW5pYUYuFB9ePjFcJ4QdTLVMnGlFLoBvV1EoYT3YgmVKTrmtriemCRV7RUZZ/y8OPU05J9l/OQgRCC7oTnYU+MtY7IFKcWl5qWh/s4UQHg3qd1DE5Q3LNTx/qzFdvVnUwlQQhxPwwgBEqkw2WUf/zp3ThcaK+vO3PTu7eL7csQF8iSr1B0J5puQmvRCzAGHwHKORSLIdCOt6JYdf8SyniE2EqN9WS1SHmclpMFUM0E/jB2RCWM5k2X0zKrxQFIACgQSKLY+W4kAFQ5oXKynrA2oQUAKHTqDPySHkRW7bNfV/Xph+b4jcFKjaKiOc7oYhV2CbtAiJYCgUAgEAgEc5r+/n7s2LEDqVQKwWAQ1Wr1qAQSwfxFkgh62hsdWrGQldRLG6Y/lehKELu/oMVs0lf9hLqgynqKpgtGg4sqoMqglGJwcHA2q10v0dYglQ/ADLQDcghS+1kwaOOt2s6XDuPJ+x/Gli1bkM1k0dbWBlVVEa/GMTSWwDnnXYSLL9tg9zrMlSkOjJlYnCIuQSakMmFBCsXwf//5a/juj1mpeyrVgb+4+XLE43Gkcsac72npB+/yCpnjANoAADTYC9V8HZVQp/15V5vkmt5yWrZHCRSZJQaPZhvVg0deYUoeISz5faJo9fVjCzC0EgYP7YXa0+tKoJcI0Jecuub+zJWLsfsZ9nO2WAPN59GzoAfZTLZBCOMFUS9Tlb9qBrtqFHx6NmpmEFKsF8hTBBVHUPO6b1XZx30pHb+eulON0fYowaFx1scwVwbanRamDWE5hfI6e5nhAHM4VjQ07Zc7XHdrmpQ5chd3so1ev249du7Y6eppuWrVKnT3LsfLGWf+l/eMQe7oAQAQowStWoVcL/VXGsrDGYVp9io9auq9HPUjCwAqo1gooajfCzP2NUB2hMtsyT1bqeoOpzlmTkvuNOqIEYzm3T0ts2Q50D4OqB1A5xoU9SgS8JaHT+OLFt0AjDwAI63AEkHz8jIU2tbZk1S06YfmlGqsZy1/HlXqD9uyJYruBJl2P9pTBSFaCgQCgUAgEMxhBgcHQSlFMMhcLsFg8KgEEsH8pq+D9ZDjb2niIeJyj1g34vdmduH0xbFTpp1AV5uEA2NuV9xs0lf9kp1DqpMo7XVR1aoVEEJYqfUs6OvrA6gBmt8DJM4HACgL1tqftwfyyNTiAIBIcjlQ+B2Gjwy7XHfBYBCRWDtOP+s8V6mwSYHdRwyMZonLERUNSbao3ZGI2vNEggTxOLtF7OtoLPudD6gyK002KdAd13Gk3kPQDPXCyO2GxImWzcrDJULQGSc4kqEYzVOYlNrlqON5E+l6f8TTeyV0RAmeGHD3ZljQ2Y63/uFHUW17A9atu9zuOZmKS9NymrXHVADMJvmG1Wuxqme1y13LC2F+IVwWU4kyGtev0CIZY45ikzr9PLvbib39XtFJaeK0PG6i5RQasDcJ3hJf/cJy4msuAuQ4ovXy62iIoKL5O1ErNerqBTqSNbG4k61MKMxCn3hBdP269bjzCQlWQA0AKO0rASWEQOI0GIXXXaX+dk9L2d3i4rj1Ojz0U9DSEAzaC9bLMYhcbgSoPA70XGFP5nValmssnAdg48LbCqFVD0KsSxOBE5zlEi3NbiY41rGE5yrnxtemI1oqMeDib8LQHgbyRUDtwJFFa0BrjnA7laOZh5WTE1dSuNXGI1dmLtrZpNWfzAjRUiAQCAQCgWAO09fXB0IIqtWq7bQ8GoFEML+JBiWEAuzG0CIWIqD1mxz+RlzPvAKUj5wy7QQSEWL3NrOYSXK4hZ8TKKQSxMNMEPO6qMz8OFavXo2NGzfOar03btyIe+59AM+PvIjwousBAErqfPvzBD2IDM4GAJjBbqiyDMMwYFIT6Yk02tvbYegGiBxCR7Lb9zuyZYpxLojk6ScfxrfHDmDjxo0IBRzrGd8zNRkj00vXnYOoMkFVp7j8klV4fit7r0yS0PJ5xON9MMDGRjToPt6SRKDKBJrBknuPZCh0gwkzVqDIwJAzwFb1SqA++sLC3k58+XO34fHXdFfLggXt01PyeKfa6jVvwaWr2G27nxDWLD0cmIZoWf+cF0l6O4gtytrrzfV39AqUqkIahMPj2dNSnULRcCXBFymW1X/2C8vREQCBcx5Eg8B4npWNe8t6vQFNI57ep6FwqKHXaM7bw0Nldd7RZVdCG9zmKvW39qksEVeY2ExDgWZN5Qh0zu1tgmCkkgTIhGsybxCPSYF8XcwNKGwfEgL7PPH2ap4t1qEIBwjCqvV9rFdvuda4XpZoWZtpeTgAKDHo3VcDiboj0p2F52rDMRVVDagRd1K4Nb/V11KIlm6EaCkQCAQCgUAwh9m4cSO2bNni6ml5NAKJYH4jS8wNZ6VCKxJLx7aclvyNeDDYDS2vnVLtBKJBgixXWheeRcqunwgSUFm/vPYIgWa4XVRn9Em45aPvnrUoHIvFcMd/fBd//71tuPdgDToCAHGUtN52A/sOjkMKpUCDPRg6MsRKgyWCzEQG+Xwe0WgU51/0Rrx5/duafk+h5NiBnnrsATz2X5uxZcsW3PGf/w2ACV98OT0hjcnc84WgyvrMLe8LgZAKKAU6+s7BFatvxJZD7QBlLsuAIjWksVuhVt4wnmT98O7yiJZegQ9wehCGAkCZc2G1hac3HqNcr05XWb+PEDYZU5eHs895IWxBu4SXDrodyz2c2OpXCu4tGT+ePS2nEsF4pyWfIO5t86AEQjBkZrG00rnDKpueAthy34O46oq1tkjsDWjyayPgJTdJ2S+tjiKZYg8dZOJu78E/TPBLej8mhBZAN91q9EQ1DrR1uN7zOi0BR3wLKEzUDqnOg7apRObpYpWHR4PE9bAhEWG/H7Mlav/NBDh9SavcOWFSdg5MR0g1rFPCqABjjwPahF16XtUik87LU9Wosyywc9TSTnNl1ndViJZu5umvIYFAIBAIBIJTg1gshs2bN4v0cAEAJgZE6u4fgIkzsuSUtbpuxKvmKddOIBryiJZTJCf74XcDG6ovpz3Keqfx4tHZi2TEpkiDnoqORAxXX70B+39dxfOvuwWjy9ech2cO7UUVKUAOQUMUoTDQ09ODXC6HcqmMCy+4EH/1fz6NTLW5625g3wEAS9j3RSWYqRR27NiB//nxnTh97cdg0tmV089FgvUE+IDCglmGMxQVtOPiN12Fe/6HqYCd8cbkccAJtepKOKW8ozmKM+rmdstpqcrAsi4JhDQG9VjiLxs3loBDXL0JJ4N3Ws7ExeVl+k5L9n844A6DsVjA9dL1nh+q4lMePod6WnqdlhYNYTmmI41EQ8y1fmDvK0D4LADAT3++Fc8//QQ2bdqEUDiEMU9Ak9dp6UezXoXF/fcjVnoF69e9r75N7p3Hp4U366/ZchbdAOPwo2AF2Gwc00AS6FzjmszraAScEJmAws6xcMB50Na69HD2vxVOF1TZw4ZEhDmkazoru7bOxapOoRkUtUoZGHZER+20NVCj8Sm/zzDBBMs9/w7U0rD2CbLPoRK7BcDUywDYOcmXk/M/m5SNI73xknJKI0RLgUAgEAgEgjlOLBY7JVxygqlRJFJ33bAbwJBKIEuOkMDfiAdhnnLtBKIeZ2VoFkE8kQAa+oZaZeYdsUYlphWOMqleTrtygeQSLSUCLOwK4bKLluOhl9l70dRKpAIjkIiEZEcS4+Y4ent7YZAwvGFMPMUSp2DpWZegfZ7CktdnE1w0FwlybQEWpyQMZwzoJvDiAafevbNN8hW8LMHT5bSsu+jSBRPj9bLgZd0SVIWgM94oWFviLz/+4s315AZ4R2Z5Bv3yvEwniAdwylFjIYK2cON0CyZxWqoyaXxPQYOD9Vgxk56WltOyUq5Aq9UQDocxNjYGNaBCjvTB2vRIgGD79oeQmzCg1kXLaKILAwMv2X0nvU7LkezkSpNh0qYuyVp2Hy4+d4Ht4vTuT/66dtyclkoM+nlfYz0sOVchH8JDKfV1WloEFSbAhlRnGvnAfwGLb2C9Io8C3mlpfVcBnr6WJXdATrFYQO3VfwWqGVhX+drTdyOy5h9d65MrUcTD7jGsG5Q5LGtpsOtsfZtqaVQGHwFWXDOt9a4ZQJF7EFHR3Pvv8LhQLL0c3SNBgUAgEAgEAoFAcNywnJYWwQC7abecluvXrceqVauQz+dxZHAQ4+NH129xvhHlhCFVJtMKPfEiScR1oysRZ//GQqTBaeZ1mc0WVSZY2etWK8JyGVq1isVdjgoXO++T0JJvASWKK0F6KgeWEnR6V0pa1iVoWyLfyeK05MvaF6WcbXqSC8xp5rS09kW3qzycCQnefpYA0BZpFJls0ZJzVsanWRoOAAlOOCwfhbPOKn9thmZQGCa1S3djocb1JMRdKs+LhIVCAT/64Wb8zV//Fb797W+jUCgAwHENcPILzuLhnaMTRWr3/b3rrp+gWCwCAKKRKK64yhGdIkEgmx4B1Qr2e3I4BUqp3XfS29OyUIHtJvQjX3Z+5q/hFomYM2i945KfvnS8nJYAdERZ6M6iG9j/slt5L9cwad/bgEqg0AJOj7wAALigYxfIgf8Gnv4MSyc/CqxgKKuVguVibvOIljz5vVuhVzNggqMJgEIrjQOHfuqabixvurbLMCmTKLUJMLGTh6Bayk57vWsaRbnKysABoOLpj5kVyeENCKelQCAQCAQCgUAwT1Bkt+smpLJ+XgGl7rzkUmvl7LlYtih1SrUTiHG9AIOzCOGxiAQJSnUBIqgQl+OmI0YwwvWvk1okWioy0BWpgpg1UIlZ9HKje3H77Vvx8U/dikiQlS0TJQLaew3KqbXQD/8GKxYMYv269XZJJuDfp6092YODTKPB8MFXgIojaO+fICA4eZyWfHDJkk7nAB1KOzups434Cl7B+nsdMSeF3HLV8aLl6XXRUiIESc+YiHA9LS34sTkVcU54KXtEja3PahiaoHjPm9RpCaE1vXlJrm44vf4AJgB5+252xt37ySpdLhQKuOmmm7DzuQGY1QkQQk5I6NdU5eGqzEK08mUmWvoF8OTzeVDJUYojQYJ4VwLQRu33tL73QK3J6EgmATT2tASYI3dJl/8xyXECmtdRHQq4S8K95eEx7pp/3IJ4AFfvRT8mc1kCdafl0M/wnsWv4OL2Z/COxY8AMIHyEBMKl3141usmS6iHJrHX1oOK9klEy3S2AK+PvmaqQOWIa7rRHEvxtpZp7we1A41udgpdSU27N2ZNZ0so1dhDAq/TUtCIcFoKBAKBQCAQCATzBCuIx8IqW1ZkJ63X6rf4+c//OW6++eZTRrAErB5q9RCNo3ANRnk3q0f8jHlcUnKLymBVGfjtb7dDz+6231OMDAYGBvDszu34wntCuOwM2e4VKAUSCJz2XuSXfhajRXbnTinFt35dxZ/8ZwVP73VboDQuVOOjH3o/brvtNltgCipsfx2vkt5jTZCz5qzqlWxXJE9X2+ROS1ki6Iyz/TGWY6EelmipSKw83MKazsIJcnHen4loybsDS5x779C4iZ/v0PHUbgMPvDBFw8o6k5WIa7o7qTgWIg1CaI8n8dwSCfv7+7Fjxw50JKJYuHAhUvUeqf39/dNar1YxnR6JVol4tkQxPjbhCuBRVRWUUkzknMSjaJDgXe/YgCWJPMzyCHtTCiCw/P0YC6+DYVJXqI/FSK65yseH8CxKSa7riretRUN5uCuYyaeHpHlshK/JXJQAkOFEQb9eqEEVUGpDkAmwsu0Qwoq1j0mDUDhTCGHXLCuwyHFaOtN4RcuM2Quv6KiZMhBaYL8uVSmKVQqNO290az90rgECSbb+kNj/9T6f1Wm2cajWRUrrOHqdlgBY78zhh4DXvg7s/8FRu1LnO0K0FAgEAoFAIBAI5gmyRFyCGi/MeUuhW+UAnG9YJeJH4xrkhWFveIp3P8stCpZQZIL0eBpGdsBZtj5hl6S2Rwk+uC6Av7ghiItOcw5uoQL89lUmYE0UKZ57nZU2PvySW9Tib6r/+kufdwnaQZXMqv/nXCXAHTNZIvj/rg3gc9cFcNFpEiQCnLNYQme8sRcj4D7eXQn2c1UHDoxR2123rFtyjYOUR7S0ztGgyuQNxfOwYSraQsQuQuWDeA6nHVHswNj0et9NFsajGRSFqlu0jIbcfVr5EB7AKQ8fHBwEpdQW305U6NdUPS0BR1CjFNi1/whqtRpqNaYWWS0WApF2e/pIEEglovjTz30K71i1C73KXvuzJwaAw+PUdjbzounIJAnivGjZFmYBURbefqfebeIF72IVDYzlKeu52GKmclpmOadlb7ufaElAwgugEjYIJVgLpC6hcDZIxC32Bv2clh4nqJG8rEF0rKl9rPy9jtWzlj9v7P0gh4AVtwBdVwDtF7D/V9wCyKFpBWbphpMUbouWXqelFfYz+hAw8hDwemvK6eczojxcIBAIBAKBQCCYR8RCzh0tLzQFFJaWanEc28rNKaJBgkyRuvoJzmYZFiGP0zLguYOaRkXgtFBkFqRkjNwL9K0HCIE5/ozds9KiLynhE1cGsX/ExD/8nCkYlusrW3SWd2jcBKXUdk9aDp+A0thz0HJaniwEPceIEIJVvTJW9cowTQpC2Hv+SfHOz3wvx3ufdk4ur3PTK1paAiUhZFaCsKqweco1d3k4X5I8NDE90VKbRLSsecrDY0FW7h4LAbl6D8aehHvbLKG3r68PhBDUqhUEg8ETFvolSQSKhEkTl9tCzof7BzOoaTUMHRlCNBqFRCSsWrUK3QtXABk2TSTI9n8oHMK1b78K1wL48WM1PPySAZMCj7zq7NTT+yS8dJAtf1LRknP9tUUIehISDo4xC9+Ks9+IM890Bq1XTI9xPU5LPuJYoUIhEdbyoJUYUzg4+fLw3g6CVw67Pw8qABbdAOX5g9BKBUhEAiAB4V6XUDgbJOLuYWw9bEhwjs9c2TOTJTqOcenhK9a4QnhG8+xY8i5Tnd8Pcoj19/RQmYbTkhdCLfG5YT5X2I+BVpXTz2eEaCkQCAQCgUAgEMwj4twNbKTBacmltJ6iTkvLleQXdjFdXGFHUzgtW+VoVWUWpLRzx04MPPV/QClACMWqVauwft36humXdDk9F60ySL5cs6Ix15AlZFhOS68IC7BtjJxETktFbi5kSZxg61daHFCcrnddbRKYcAC8cIAtjAC46DT3jN4EcZfoHQDiMygNB9i5Gw4QlGvUFe7Ci5bZEnNrTeXg9JaHTxRMSBJLCfeWh1tlyG1hYjsDF3Q4yydw+i1u3LgRW7ZswY4dO2xx/ESFfikycQtLdYIKQVWnSA/vBbAcAJDoXIIoHUU6ncaqVatwySWXYP269fjOQwSoOwF7O0iDiH/uEhkPv8TGwlNcoNNZi2RbtBydZnl4wuO0PPeC1TjzdD6Ix/3dce5BVTk3Arz6P8ypuIilcJdqzMXX2db062fFTMrDezucc8UiFCCAEoV8xmeBQ4+DdHcB8U57vY8GSfI4LevXNT493Lfnpkd0rFFnes2gtjuTP2+MKfYD4DwUmnQabpmlKgvBagjKssN++PePvpx+PiNES4FAIBAIBAKBYB6R5JwkfFKqV4A5SdoTzhjrRvZonJaKTGzBwyvyqV6nZQuDePggpfR4GslUEuvXrUcoHGqYXiIEiQjBRJHaoqW3h9uhcUfIsMoQQz6OyqDK9W07SQioBPoUScveYwk47siKRl1OS4s3nyVjUWpypyXfgzCsNvaJnApZclzUvNNyzCOK/WzrEyCF/ZOOE295+MFxirG8gYBCWHm4p6eltT2H0sy9tyDhbCsfEBOLxbB582b09/djcHAQfX19Jyz0S5EBeBxrYZUgqLLSfqM8Dku0pEoCwWAQgUAAK1esxIYNG0ApxYExZjmNh9k11nver1ogIaCw/cmLeX0dBO1R5u6evDzc+TkeBi5ZKWP7yzqCCsH5S90Xb6/Tkn9QVSrlgOEHAFBg5AHg4m+iVA3Bp8XmUTPVNSHrEi39rysAoATCQM8VkE6/CmiRo5s5Ld0P7STC/g/XXcq50jSERM0JLhvPU1sq1PzKwydhpk7LUpU2hGwBaBr2c7Tl9PMZIVoKBAKBQCAQCATziDMXS3jjChnjeRNvO9f5c/5YlS3PN6Ih5lPxcxTOdDnVQqPI17CfW1SHb7mrrCCl6ZCIAhNFloxsmLRBtNz21ABeefwZJFNJVLV1APx7fQYVwJxBz8X5QFBhaeuT0SztN6QyEcIrWkaDwDvf2DiwvKJlzBPkNJMQHoCJg8xFTaEZQKFQxqOPbsfBkUsBOAt/8NGXoA09AkIIdu7YiU2bNjUIl97ycKuXnuUkK7pES/b/2y9SUdE0XLBMdglDDb0WYzHcfPPNM9q2Y4Ffb1Jnn1N0JVS8Vs/ToWrC7mNptV0Yz1O7XHdJpwRVkexwM7t3pUJweq+EFw+6FayuNoLuNiZaFqvN3a/e8vCAQvCVm0IgAB6+70cAgLde8wHf7QkHCBTJhG5KKOph2I7GetlwuXojDMrcfl5n+NEw3fRwQoCeROPTG+vBkSqxsdzKliWyRBAJuK93AYU9bGiLMJey93roR6lG8cgrTDzm4QXGyVoPWIzmTDy7n6IzLqGn3b/1RI0TNg3axAnauQbIPlcvEZcBGC0pp5/PCNFSIBAIBAKBQCCYRwRlCX/wVqY88TeKjWXLJ5cINV1UmTkQj3b7o0GCdIE2pIfLXA+9VgrDfsLLVLBSSOYOypZoQ/DES/syePrl+0AkGZG1lmjZuNKSjwAw32HizRROyyb7PFCfNxUnIIQFuADAuy9RXSKeRTToCJ0AEOOcldGgOzxruvDl+l//xr9h754BRNasc00TTCxFW+1laJqGgYEBbNu+rUHw5stcTdNdbg6wICcLS+hb0iXhj69tXOnZjNHjgSWK8cTCTkuEyy+7EL/9Gfu5SsOo5fOutguvjzqq1NIuyRZnVZm4SnrPWSK7REtZYsnk3QmCXUPsvZEsxbJuH9GyXh4eUp1rtfXAI5edcE3r3c+KDARlHboZQEUPomIEEJJrAAgqhVG7KDtdoL6Ox9niV3LPY4mCbWEgHGwsarYeHFnb02r3v/caH6z3de6IEgxnKKo6MJwx0dM+tR2+6mmjwJdtT8dpaVK2/9MFA7Ik+x6HxlYNTcrXrb6b3Z1ArKsl5fTzGSFaCgQCgUAgEAgE8wg+rZp3PvEOwFNUr7RJxY++ZjsSJJCJvxtPrffQa6UwPJ0UZC+upNySu8ccACjxJYilUqiZip3bG27ixCInWT8BbxiPH81EOEdsIVjRI2H3ERMreiSsOcN/BkIIOusl1YDbWZmMk1ntW96tt+/AEGKppfBqJyTSCwBQVdVOmQeASrlitxjo6UzgjJvfhliM9T70yiR+PS2b0R6dm2PEr8w/FiKgdbW5JxUCQQUUQHv3aXjbxTe6yulfH3P2wdJOyR4XiszKyy3OWeRpCxBjD0e6uP6UIzkTy7obT+Z8XbTkW3o03R7PNUeRCIIKRVEDKkYAeS1SFy0pStJCqxVnXbSccvHTZrLy8EKFIldiP7dHCSTihEdZWE5La38e699LAZUAZYpzFkl49TDbKTv3GLj2De7jMZ43cXCc4pzFUlO3Ne+0rOkm7n5SQ0WjeM+b1IYHhF6KTdpSVD2u54liEzXU6rt5xlVAC52z8xUhWgoEAoFAIBAIBPMIXtziQ2D4G/dWlSzPV1qRohsNEgSb9F8LKEBZa23YkTIL2yYfOpEt+pRDBjpApTBUJQCrUvpoAormE1OVyUqk+T7nS0Vv+b0AXhs0cdZCCdIk4uOKBRIOpQ20R4lLTG4mikyFS0CUw5AjPQ2ipRnohmmayGQzqFQqGBoaQmYig29+85sYGBhgATkw8cK272Pz5s0o6pGG77FES0IwaRgTAbAwOTfTvfzE53iIoFIX0GSJoC3ChH051NHgRuWdlku6HNFSVQBwLQY62yT0JAiG670rretMd5uzXw6NU1yy0r0uNd3pX9g2jf6m3gcYsgyEQkGgDFTNAPJaHF2hHBDuRSl1LTDKppvwKzc+CiYLoHnhgGEL4Kt62Q6L1MOjLKy+rMdLtLQeVLxhhYKfPqmDAtixx8A1Fyv2gwPdoLj9nioyReDtFyl4h0+7B8Dtinx6r4nfPM8UxyWdEtaeObmMxrdcaLZMoF52blRcaeboXMNES4GNEC0FAoFAIBAIBIJ5BH+DLrucls4d4SmuWbpSZWdLJNi8L6aV1N6q5HDA3y02FQnO+ZYt+fdwM0MLYFQy9mu/8vCTEW/vUS+TiYl8S4BIkDSkhfvxrtUqlnRJWNEjNRW7ZwJfUk6UCHS53X5NqQlCJECN49DwBPRyFrIi47nnnsMXv/RFZDNZtLW1QVVVaJqGHTsfRX9/P6581x82fE++7HzfZM7hVFyaMqn8ROEV+VSZIBQgUBXnfOiIEmRLzB2oG9QWrE1KcXCMiZbtUdZawvqMjRH3OXXOYgnDWabmWT1PF3cSuzR620s61p4hu0qS81xy+FSiJUGjCKvKQFCVAJjQTBWZ6OXAUgIsugHFkTAsq2VNZ8FKM+2h2ozJejk+u89RNC9cxrY1HARQcKaxe1rW9+exblliPahojxKs7JUwMGRiJEtxcIxiSRf7LF2gyBTZ9LuPNN9A3WTtFCSJYDjjTDc8SdiSRbFJL92qN6zHqAB7/r3ev7I+grLPgS7/JIZyQehGa3uUzlfm5qMSgUAgEAgEAoFA4AvvDuN/FuXhrSWgkKYCgyUwtrKnZbP+ipPBO/pGc6Zv8EzBSKBUcQQG4bRkTNafcTbJ86EAwZrTFXQnpFmV+nvhhae+xcuhSW3266A26PycWIr2jnYsXrwYsVgMhw8fRqFYQKlcgklNVjpOVAwODjaIKZTShrLlZlu+KDV3Lyqqp1w3Wjeq8eeUVdpu9X+1GMlSuxfp0k7ims/vnLyQE7CXdrEDnYxJuPxc9r5mAP+9XYNJne+wyqgBoK3R7OpiaZfU4ABWJPcDlLHkRmDZhwElZgcrWaQniRHn+zROB91veqOCyuFteOUQ22mJYAXL6HbAqDQ8ELFeK/Lx+Z3EP2xYvdI5Tjv2ONe/HCcgTxXUY5WI57m+r96+wX5UNOq772qe8nCMPV4XLCmY8EyBWhqjrz+Dv/lJFWu/UMA//LzSsJxTDSFaCgQCgUAgEAgE8wjeXcn/zN9gE/FXfkvoiDUvDwdaXB4+i2Xx5eEHuL58fOr1olWrsXzVWfZrVfLeOZ+cTNXTcjJnqzd8aaa0woHLi5ZXXv0O9Cw5x3694U2L7J9D7cuQ7EgCFEin09A1HbqmIz2exvCRYdRqNRA5iL6+voZee+Wa46azBHo/J24kSJCMzd2LileAjgXdDj/AfS7zASjeEB7AOa/9RMuVC2T84VtV/P5lKs5bqGHr1q24s/9ORDLbkKpnpewZNrHtJX+hLO7zIGT12quweu1ViIUIlnU17mdJInapNVueiarGlul9UDGW93cPagbFs/sMGFOE61gYJm2Msao7A1/efRi6yXbO+e0vQRp7CNjz74io7u8OW+Xh0nESLblz+qJlsn0cf7dXt0VkXkDOTSFaavVDWKw42+XtG9yMgkdrNE3aKBprE2h8TEBwgLWmRUVz9uGpzNy98ggEAoFAIBAIBIIGlCaiJSHEvklvpQPwVCbRxGlpleK3sjxcksiMjxsfjGKVuALAmQslWySYqEax73XHmffgb36JQoGr4TxJCarNXYPA5OXhAeXoRJZWOC15cStfVaDLCQAsqXlVn6NkSJE+aJqGfCGPSqUCIhGoKlNdy+UyxsbGcNEbVuMDH7gRFU9yeN4jphH4t1ZYNEd7WVp4xUVr3/HCdAd3rvC9H739LAFwPS39B8EbVii4dLmOf/6nr+PO/jtx33334X9+uBnkwI/taX6+Q7P7heamKA9fuGQlFi9dibMXyU1LqHn3b1VjyzRM2pB6nSs1uvwMk+KF1w3kK3RaSdhAkxCeujPw2fHT7bcuSr4GyyEYpqPudbadlrMLo5opAW4fRUMEZ9eDkzJFpxScH/MVDbb464fVg5J3KGem2TfU+4CgwWUJsB6WDdIwxcFCj/3qnMWzsOCfZMztq49AIBAIBAKBQCBw0aynJcCEmlSMYHmP+DO/FTQTECyRRG7xjfhMw3jCAWdd+GTazjjBgna2LE1OIhTvtD8bOrgH/f39R72ucx1CSFPRCQACk2gBhJApE4KbIZHWJLGfuVCyRdfHXtORrTvEutok9HY453e0cwXy+TzyuTwopQiHwuhb2IdkKolAIIBzzj0H//SNb0IKxOA12XnFNEV2O/osmjmO5wpeAdpyqfJiZjPRkncoL+mUXMubTHzetn0bBgYGEI/HkUqlEI/HceDlh7A0NgSAiVSP72InJV+G3Cw9fFFKmrQXJd9PtKox8c2vHYRJG0vEXz1s2g7ByRLBeXz7WWoT0EwFL2ZY0lBELmNV24H6hwRhqeheZy6I53g7LQF3ifjz+9mG82MemLxE3BIaeQEyU6R2Kj2l/mXgQGMYj69o2bkGCCTBHq9I7P9AEgeKffYkQrQUoqVAIBAIBAKBQDCv4JPBvTfVF58m44JlClJx8Wf+scQqD2+l0xLw77M4mXBCCHGViFskogSLUmxGIsmQYsvsz6hWwODgYMM8JyOTlYhPFXzULIRpKuQWqTN9HRLOWcKOYZYrae1qI4iGCBL13ohqfBE+cOONOO/88xAOh5Fcdhmk6FJEwhFEIhFccsklUAKRht6HgBPCAwDxMNsnfr1AZ7svjhf8eUPAxvi3v/1t/M1ffxm/3nofKuWKS3j93R4DmSIrlz5Qd1p2xoktGrrSw5uQHk+DUmq7WlVVBaUUXcaL9jS/fcWAZlA8sctRrF57/lFUyu7a4Z/d+W+4a/O/TbqNfLJ7RaMoVOB7TAG3aFmuUYxkHQWyqdNSLwD7fwC8+g/A/h/AqBVwaNzEP/68ip/vqDf9VDvwWnYpKgZrjHtecgCyZC2QIuw54SwBXJVZOv2xRpGJ63q5oscZGON5tk/y5UZnajOc8nD3e+UaK7f/6s+q+PPNFZdb18LrtPQ6YgGwlPAVtwBdVwDtFwBdV4Au/yQO1svDO+MEPQnxu7wle2Dbtm2QJAnf/e53fT/ftWsXPvaxj2HJkiUIBAJIJpPYsGEDtm7dOqPvqVar+Pu//3tceOGFiEajiEQiOP/88/G3f/u3qFQaG5Q+88wzIIQ0/femN71pVtsrEAgEAoFAIBCcKPgbdK8TcKZOPcHssBx8rexpCfj30JvqppUvEbdIRIjtGgMAI7Lc+VAvoa+vr2Gek5HJwnimOldmm9rbqjEhS8D6sxtVs856v1LLbVmsAhdcehX+95/8byy95KOgKz6O2vJPo7b0D7D0rLdg/br10PRGEQXwOADDBAG5sZ9nQCEtE2KPFS7R0qzgQx/6IG677TZ861vfwub/+k/cfvvtSEVqaAuzaQ6OU/zV/1Tw7ftrtqPw3CXOgXN6Wjbf7mQqCUIINI0JepqmgRCChV1BnLmQLWA0R/H9+ytI17sxGBMv45f/833cfvvtDcLlVD1Yeadlpe609DumgCPQAaxtBD+VztttLaHy5b8FnvwQ8Pp/A8MPAK//N/Rn/hwPvVDFvhETW5/VMTRhAp1r8FLubHv2C5K76j8xh2CkYxF4Isc5iAeAyyHdFqyA1Ld+IpsDjMoMnZb1vqGetgqZIsXAoImDY8ztumN3o43SG3rl67QEmHDZcwWw6Aag5wqMl4K2g/ashUKwBIApTo2pee2113DjjTfaFlkvjz76KDZs2IBisYhVq1bh2muvxeHDh/HrX/8av/71r/GP//iP+JM/+ZMpv6dUKuHKK6/E448/jkQigbVr14IQgieeeAJf+MIXcM899+CBBx5ANBq153n66acBAG984xtxxhlnNCxz5cqVs9xqgUAgEAgEAoHgxNCsp6Xg+HEsgngASyRx31ct6JAwOGE2hmLU8XNatkc85c3BpP3j8qULsHHjxhas7dxnUqflFFWXsw3jadWYUGSCsxZJ6GojGM01hiyds0jCq4eZ4vbgCzpueFMQWmodUKuvR/uZGMWZeGKvgsXd/uW+eU95uCoTBD1l8XPdZQmw8yYSIAABHrpvC3bs2IFUKoVgMIhyOIyBgQE88dh23LLhKnz3/hrSBScxHAAuXi7jnaudDbVE0MkS5tevW4+dO3ZiYGAAlFIQQrBq1SqsX7cePUcUvHqYHYjnDjjzhDPbEIjHsevlp7Ft+zZs2LABAHMhTjUeo67ycNbLMlMCC8cZe5yFuqgdQOcaVBFCvkwRCoCJjRyGASZWHvghMPgLwLDstu4rjF4aRS47AaADALBvxERvRwj7aueydQbFGQsAhM8DAl1A5xqE9ygAnB1rhcjIEjluv6sCKlCqATAqkPd9C4nAjcjU2pApAtjz78iXPumaPlv2XQwAR2j0OlonihQjWec9b+gOm5eiplP7OjxZ70wevl3BWYtEaThwlKLlgw8+iBtvvBEjIyO+n+u6jg996EMoFov46le/ij/90z+1+3v85je/wXXXXYc/+7M/w9VXX41zzz130u/6yle+gscffxxr167F3Xffja6uLgDAyMgI3vnOd+LJJ5/El7/8ZfzDP/yDPc8zzzwDALjttttw7bXXHs2mCgQCgUAgEAgEcwL+RroVgR+CmaPKrAy11TfiXpFEIix4JRoidqiHF6tM2P0eQSrOluftYff5//2/EIvFWrTGc5vJhMepRKLQLJ2WrTonZQmQCMG6sxX85AlHCOqqO28vO1PBvc/oKNeAJwcMqAorW7XmtcqAn9qt411vVOHXes8VxBMhUJVGkTLkkyY+F3nT6UzauG/zq6CUIhhkJcwBhYBSivR4Gsu6JPz59UHc8VANLx8yocjAe9+k4i1nya4+pJbDMuCjllTKFWzbvg3p8TQuuOB8XHDB+cjnC0imkli/bj1C4RDOW0rRHmUBMBZS/lVI5UOQVBVmeRTp8bT9WUCZug9qOMitQ304ZHJlYM+/A7U06oXxQPY5YMUtGM+zC4P3uBtaEXj6j4DyIBpDYBx0qqLCCW37Rky8cQXFoXH2ekG7hPDpH3KvY8B9sQkFnJPBb18eC5hISO3QoPZAHplaG/JaFEYlg1yxAiBkTz9pebhOYZrUPq8sMkWK0ZwjBjdzvBYrFIF6WwKtmdPSwwEuUM0KEjrVmdVeGBkZwac//WlcddVVSKfTWLJkie90Dz/8MPbt24fVq1fjz/7sz1wn4lVXXYWbb74ZpmniRz/60ZTf+R//8R8AgG9961u2YAkA3d3d+OY3vwkAuPPOO13zWE7LN7zhDTPbQIFAIBAIBAKBYI6iyMQO6JCFEeOEYIW8tLrk0dtDLxpkba38EoctEtFGZ1wowEJV+BJxi+4OH5XzJCU8ieA2VXn4bEWWVvU5tQTxNafLLoG1u+60DAcI3nIWW0ndBB5+iQlGhACfvz6IaF3kypdYSJNf/8PGIJ7G1PXwPHBa8vT19YEQgmqV1dhq1RIIIUimmNs4FiL49NUB3HpdALe9P4h1ZysNgqElPHsF6Eq5gttvv91ODL/rrp/gueeex/XXX48NGzYgFA6hUq7g/t/8Gm21V9wzD7HWeJqmgVbH7PUB0OBu9SPGOy2tcuW6MMfERxNWijfGHsdYnuJQuvGY64fvA8pDmEywBACDEpR1R9zbN2zi4Jhphzkt624c6GFPiBMvgM822Gqm2O5qbQIAQXsgDwCgIMhoceQq7gE9VRCPbsIl3lrzuJ2WTUTLqvP5SG66TktetBS/4IFZipZ/93d/h3/7t3/DypUr8eCDD+KKK67wnS6fz2P16tV4+9vf7vv56aefDgBTNoIuFApYsWIFLrjgApxzzjlNlzM0NGS/Z5omnn/+efT19WHBggXT2i6BQCAQCAQCgWA+YAkuooXliSOgtC50xcJb6m0Fg8QnES3bPfPwyzjNR1iITpJQfLLh1+/TYuogntk6LVuzfy3XbSRIcOX5bGXPXCi5ehteca7SIKy9YbmMvqRkC925MkW5ZjYkhwOO05IAiIWYw9CbnD7b3p4nio0bN2L16tUYHx/H4cOHkUmP2WXbFhIhWNkrIxnzl0OsfS9J7mAXv8TwgYEBbNu+DYBb1Hz1t3eAGky1CpZfQ2HkVYyPjyOfy2L1RWfjqrc56zMdgTzq6IeoWq69ujDnhgDaBHJlavdk5DHK4z7zeOaHBD2wAGXTecAxNEHx8iFHUDutp3HfRTwPCXiX7vF1WoKVyoPaoiUADJeT0Ey3EDiVaGmY8HVa8qJl0ac8nL1PUapSPLufBTJNBaUUB+uiZSwEdCfm17l3rJjV0Fm+fDn+9V//FR//+Mehqiq+973v+U53/fXX4/rrr2+6nKeeegoAsGjRoqbTAEAsFsO2bdumXM7ChQvt91577TUUi0W84Q1vwN/+7d/ihz/8Ifbs2YOOjg5ce+21+NKXvuSaXiAQCAQCgUAgmC9Y5Z/eIB7B8UOVW58e3h4lVpEnAEe0nNRpOYloubxHwgMvuKePh1uxpvODoMoSof2cUFOWhwcm/9xCIky8tkSJVrmfeUH8ujcouOwMGR3RxmN96ekyHn3VKcvdcCG7xY+HCYYyFJoBVDX/7cnV+/lFQ+z7rH0SVB1hzOuem+vEYjFs3rwZ/f39GBwcRCh1Bs5/03UIhUNTzwwm2fEuXFUmdnhNs8Rwq9SbFzVVVUFt73dQkRdgw5okQmfciPR4Gj2dCfz5zW/DvvEwRvNMoPrAB25EZ9vkF5MY97DBdv7VhTk3tP6+P7raDTRxBkKJA8k3AuHF0JXrUb7PmY4C+O2rTo3z8jnqtLTF0c41QPY5dHCi5YHy8obpvcE8PJpBUa7RhhYbY3mKcS6hvVl5+ESJYny/4SseW1BK8fBLBkwKnLtYst2ZSzqlKVsGnCrMSrT87Gc/e9Rf/MILL+DOO+8EIQQ33HDDrJdjmia+8IUvAADe+9732u9bpeHbt2/Hk08+ifXr12PRokXYuXMnvvOd7+AXv/gFHnzwQZx99tm+yxUIBAKBQCAQCOYqstR6l59gZrBU5dYuU5UJ4mFi30jH6jpLNMjEMT+3nNdNyJeL+zktY6eQ0xIAkrHZiZaBevm/3z7nScUlaAa1+xe2yv3Mjy1CCFJx/wVfeZ6Cx19joscFSyUsTLIZ2zjxOl+hDb0pKaX2OLNEcct9GlQJUP9svjktASZc3nzzzQCAQ+Mmdg0ZU8zh9KrMpMcwsLSGjRs3IhaLMddlvYcknxiuqqqdGG6VentFzYA2hPyRF1EqXI13vfNGAOx8jcUUxEoGRut62qLelMtB67tNvGhpOf/qwpyrp2Ugyd5vgtF9FaD/vF4iDjaPHAb63gks+QCgsH632iEdVa3kmjdfF7lDKrCg3b2+YZW4tkGW3OLv8XJa2n1s5RCw4ha053cB9TCkA/SShukn62lpUiCdb0yw2jdigs+hLtcAw6QNv5P9WjJ4efmQif95nA2wh1505vdr7XGqcpyGjpuRkRG85z3vgWEY+NjHPoYLLrhg1su69dZb8cQTT6Cnpwd//ud/br9vhfCsXr0aP/vZz9DX1wcAKBaL+MQnPoE777wTN954I5599llfBbtardp9MCyCwaDd0FcgEAgEAoFAIDhRqLITtCE4MQQUtLynJQB0RDnRsi4mSRJBPESQ9XEFtU3itGyPEnRECSaKbD6JAJF55pw7WpIxggNj7vesMuipCKkEpdrkwkNfB8FIFsigtU7L6Qb69LRL+NSGAPYOm3YZOeAI3gBzk3W1ueeraE5Ik9V+wAqg4R1y881p6WUqcRpwyroHBgZg6iXcM/oktmzZgs2bN0NVnB05WWI4MLWoCTj9K+Mhp8VHuZBGpUiQTCbRDF60rFq5THVhzpseDrm5q9QgEeDibwKHfgpUjgChBcCiG2yx0iJXbt71cmmX1ODyDweBKFdG7d3vU7VjaBUuR6ccQkffOcDTbMUOjPM+dkahAugGbdrjdizf+F5Va3yvWAHaZtEueDDt/CJPc+5NIVo6HHfRcnBwEFdddRUGBgbwxje+Ef/yL/8yq+VQSrFp0yZ84xvfQCgUwo9//GNXQM9XvvIV3HLLLUilUujocOzR0WgU3/3ud7F9+3Y8//zz2L59O9avX9+w/K985Sv48pe/7Hrvz/7sz/Cnf/qns1rf+cDExMSJXgXBHEOMCYEXMSYEXsSYEHgRY+L4UMjpqGhAOn1CPAgz4mQdE8WcAVIjUIwW31zWTOSyBoIyQT7rHF+jYiCX9Veqg2oIVa0uOEkl5LKOs2xxUsVEUalPR5HJZFq7vrPgeI4J06Qo5nRXinJYJdM6dypFfVInVkghQE1GuWDaxyavSkgHjl651AyKXHZ6kcOL29g/rcz+AUCQKACY+jg8lkdXyD12RvMEVopyWKkhly0il1VgVkl9ewyoEkE2c/yuMcdiXBTq2zIZDz74IF595UXEYzGoUgR6LYEnn3wS3/nOd3DplR9EjkuKvvnjH8Ojjz+GifQEOpIdWLvmMuSzadx332MYGxlFJBJAJj3KJiYEK5avwMUXnIdclpWQJ2QJ6bQMXWfHNxYk+N737gAAfPzjH2+6jnrVBMAU5EK5hly2AAAYzRH8ZOdqLOygeMdFGlAoASg1XU6QSkiHZaDtOsASsnM1AGnXdEPDOviUbZ6+RBW5bNH1XhQSKkWKoBpEVSNQZYp02llmoUKB2syf8sx0TNR093mjmM4450VBnsGRDNoj/p+N6jKs/T4Zw2MZIOGzDKMCjO8A9CygJIDUapeoPJZxzlOeZDCHdFqel07n6TKZSM9zXP/KefHFF3Hdddfh9ddfx+rVq7F161ZEIjOXo6vVKv7gD/4A/f39iEQiuPvuu7Fu3TrXNKqqYuXKlb7zRyIRvPWtb8V//dd/YefOnb6i5ec//3nceuutrvdOBafldAeO4NRBjAmBFzEmBF7EmBB4EWPi2JMqGqjUKJLJuS9aAifnmKjARDgIdERbK1om2ikO5nUkYxKSSUf8WiKZKFADEgFW9MjYfcSwPUPt0QqGM+xVTzKKtoQzLk5fqOP5g8waFA5ISCYTLV3f2XI8x8Tigu4SLBJhMq1zp6dkwMw0tzQv65KQSsmoSSYyddtiKiUjmTz6MUEpRdvw9ERLPzo7dFh1zYYUQ1tCQb5M8cphA2cvkkFLJgDmQEu2BdGWiGNBt4KAQqDJJsaqBuKh6e2nVtLqcaGGKdpyk+/HclmDJAcQisRB9CyC0SgymQyy2Sw6U0lUCDcGEsC7r3fa0vEuTUopKCg6Orux+o2r0d3TjfXr1rv6afZ0O+OjK62jI0bwWjQKYPJtX0IMAEwoNKiKtgRzRm55oYb9Ywb2jwFXnB9F3xRjLxab5jFVawD8E2bOXBxFW8ItzC/okYGgid6OMvaPUPQlZde1JqbTWfe1nMmYoJQiMaLb18ZwjMJvO1JxgvE8m8qUE2hL+O+3IxP+83shalvDPoFRAfb8wCnfNygwto+5Y+vCZdWsAWDXDkViaeWpOMHi3nYkk+pJLVpOl+N2BfrNb36D9773vcjlctiwYQPuuusuxGKxqWf0MD4+jne/+9347W9/i1QqhXvuuQdr1jTv2dAMK1G8VPJ/CnEqCJQCgUAgEAgEgvmJIrU+BEYwMwIKIB+DoARZIkhEiKu8F2AlvATAOYtldLVJKFQohuqCWnuE2KJlwtPjkk/5DTUaek4JUjEJ6YLjtlOnKZ4EJ9lfBEBfB9u3/H5tVZ9TQgjkus4xG/jE+XyZolKu4B/+J4PxagLdoWG8fbUjBFk9La2SdKuE2dsHcz4ynfJwvqw7AAPVahWEEPT19dlJ4s1wh++wkvBcNofunm5s2LChYXp+rLSFCaJT9LK05wtItqhV1ZxBMZZzfj6SoeibQt+bblsRPlXb29v1tM4aAHeiVzjA9vWH1gWwc4+Bq853S03TOQ6tgBCCgEJQrYffqDK7lhY8uuPilCNaTpYgPlF0dlhjcbmDd/kAWNl+LV2fqz5nLc3e77kCgDsI6HPXAM+/dhgXd+0HGZGBlWsANT7J1p4aHJc/dfr7+3HNNdcgl8vhD//wD/HLX/5yVoLl4cOHcdlll+G3v/0tVqxYgccff7ypYHnrrbfihhtuwOuvv+77+d69ewFMnVwuEAgEAoFAIBDMNRQZUEQQzwlFVcgxE447ogRRT2BONEhw7hImWAIsZMcaAt0J9gMB0Bl3r9TiFLGFkmTs1BwzHZ7tnkqIsghN4nJKxogt6vHi3nR7UU4H+ShSffjE+Ym8jq/d/nWMVdg9+HA5gbvv3W5/Ho+Q+oMQd0/Lk0Hknk4vxfXr1mPVqlXI5/MYGR7E+Pg4Vq9ejY0bN07pDpwqUdwLP1ZiYRayNR0U2RHRK1xPxUyRFzCnViT1aYiWhkld/XNPiw/ZP3eF0ogd/hZzEXKEAwSKTNDbIeEdb1SxuNN9kh3PJGxv6E9HtPG7F6WcEzU7+DQw/FDDNgFuQTPZJAwLaJIgrk2AXZV5SP19hiVaKhKwLPctvKvzB1hMHwFGHwKe/f8AvdD0O08Vjrloec899+DDH/4wdF3Hbbfdhu9+97tQlJkbPDOZDN72trdh165dWL16NR5//HGsWrWq6fRPPvkk7r77btx9990Nnx05cgS//vWvIcsyrrzyyhmvi0AgEAgEAoFAcCKRpdYFfghmR0BpnavOS0dMsoM6eCzBEmDiR2/d6XfV+QouXi7jfZepDWniikzw0SsCeOMKGe9fO89TVWZJLERs9yAwfddXqMnuCqsEZy50FhJUHGmilUJ2MwF0OvJPnDPC7T04ij0HxkAIW2dCZEyYffbnbWFih/AATBwjYELUfEeWyJSJ7qFwCJs2bcKNG2/E+997A2677TZs3rwZsVhsyrHCuzQB+Ibv8PCCWjzU+HCiGRJxktwrnNNyghMtR/NT23KNqYLU9QLKu+9E5chT9ltnJ3YhLDNB7+z2vY5bkCMUcF8PT+QzNa9o6b0mAkBfu9MyIJfNMJFwz783CJclLlyot30S0bLis+/VDjR6M2n9fUa+LlrGg1UQzXJlmuz/8hEWmHSKc0zLw4eHh/HRj34UhmHgC1/4Ar70pS9NOU+pVMKBAyyT/swzz7Tf/8xnPoPXXnsNZ599Nh544AHE45PbZG+55RY89thjuO2227B+/XpcdNFFAIB8Po+PfexjyOfz+PjHP47FixcfxRYKBAKBQCAQCATHH0UmkMks60YFLSGgHLsE97bw1NMAzG15ZMJEZ5uEj7+tuSB5/lIZ5y+V0R6Z/yLUbElECUayVsno9Obxc1qGVIILT3MHZEiSU5LaUqdlk2W1hf2T5Hn48vBilQDBlOtzqW25a3m8I9EqsZ2sPH4+oSoEhjb5/gqFQ9iwYQNO65ZwWrczQKZy5U6VKM4jE3e6dSLqFosnQ5Ed56uVXl2uUZfrki8Vb8akTku9ADz9GZQnyijnnL6d7YE8Pnt2P/bmF+FNXS/A6xZUZbYd/L46Vg90pgM7N5194RUtI0EgpT8P4HwAQLYWZdN7SrcBoFJzlrOgQ8KLB50d2BEltmhcqPqsSOcaIPuc09MSFAgk2ftgIWFWWXk8UEZjATphCe+nOMdUtPz617+OdDoNRVGwZ88efPCDH/Sdbu3atfjUpz4FAHjqqadwxRVskFDKDtjLL7+MO++8EwDQ0dFhT+vHD37wA0iShA9+8IO4//778YMf/ACXXHIJ1q5di2Qyie3bt2N8fBxvfvOb8c///M8t3FqBQCAQCAQCgeD4oMjCaXmikSUC6RgJx9MtpQwoBPEwQWaSnmw8p3If1PYIwUiW/Tzdnpbe8mgC4MJlsq8DMRwAqrpTYt0K/M5xiTARZirRMqCwtgAVDTCkCKRQp+tzQpzBEA+TBnEuqJ4cTkuAidS8uDf5tGTS114sl+a27duQHk8jmUo2hO9YeENVrGV/5jOfmXK9ZMkR4zSDlXBPeNKwpyNaGuYk0xz6KVAeQlnvQsVwHoKE5SqWxo5gacwS0IjLLWidJ/zDgLnktPSWh7eFCdrkUft1VrNaF7rFWAAoT+K0PK1HwsReZl31dVrKIRa6M/Y4oE1AI3Fs3b0YbaMvYt3KPArRN6EueaEtxPW9tKFAaMFkm3pKcExFy1/96lcAAF3XbdGxGZMJkffdd58tYD766KN49NFHm057xx13QJIkEELwn//5n3jrW9+Kb33rW9i5cydM08Tpp5+Oz3/+8/jsZz9r950QCAQCgUAgEAjmE7J0Yp0sAsbx7NPWDCbATU+0PBbBQfMFvsfjdHtaWg7KWj3UIxoiiDQJTrEEpVael6xvrfvYhtTplxTHwwQVjcIgEXT2rUK+6XSN4lxQPZmcltOf1js2pjOv5dKciqPZn7JEXCJ6pebuZwkA6SKFblAokwitJmUOP19xvXIEAEHZCKFsOM02Q6oJJtk3ugUBp08n/70n8gGJtw+p12kZDxPEo1EQmKCQkKtZoqW7dBvwOC3b3Rt1WreEpy3R0q+nJcCEy54rAKOCxx/ZjnsHlgIAFkv/CTV4EMD72Dq1dbD9yrsywwuARTdMf8NPUloiWt5xxx244447Gt5//vnnZ7ysyy+/3BYoLW699Vbceuuts1q3j3zkI/jIRz4yq3kFAoFAIBAIBIK5iAjiEVgEZuC4PZWdlvEw7DTumeyzkArU6u3veOHTS7huTDvW5eEBFYhM0wEZDxOM5lgJ8ZnnrMGzPhm10SATxLwl87HQ1C7D+QLbjmkK+559rsgsTb23gwnYu4amagrZnGbBTrt37wYArFy5ctL5+SCt8QJ19bMEAEqBdIHawVzN0E0g4DdOQwsAUJT0IMq64xQNJ1cCHYuZC1HtYIKlzH3uM/bnutNS6lqDtkAB2VoU2VoMALHF2OGMCUUGUnHJ5bRsi7Bttd5b1s21YPBLD+cZexz7Mwn75cFiD7oMx9XZFlVcrkyoHcCFawBl5gHWJxvH1GkpEAgEAoFAIBAIWo8iEUiS6GkpmJmL7FR25xJCEI8QZIp02uXhQN1BWS/Fjk3icLQEqVYKw37HK6gQRKaZOM2H8Rwc9xfu2up9Tr3j6GTqfzrdHqaAj9NSJrjsDNl2VR9KmyhxrrolnRIOjE2vuW0zp+XWrVsBTC1asuAtJpoOZ8wG0RJgJeLdiYa3XTTtxbvoBmDkAZSNsMtpGe6+CEg2b7RrjX1lzpSHT+W0BCCH0BanyI4DeT0Ks/MKSF1r8Nqwiv97bxUSAf7yeqBcyABoB8DK5NsjBOW6+7KvQ7JbMBT8ysN5tAkMlZbYL8eqCQRlp2dBW5g4rkyLWQRYn4ycwr+2BAKBQCAQCASC+QlzWp7otRDMBbw36JNxKouWgCPEzUTE4ktyJ3Naho6B09KvjD2ksjLcZq49Hn59xwv+okosSLF161Z88/99A9/+9rdRKBTYvCeTaDkD7cfPXcq3gViUdA5wNEiwokdCtEnLAC/enpYzZWHSmX8429jTEphmgngz0VKJwbzwX1BJbkCZdNtvh336c/JYIro7iOfEjR+vOOwVLa3zIhFhx9KkEgptlwNyCPc+rYNSto9eff4RlMtFe77QwW/jDcvZ/j13sYRwgNjHvml5eB2qdGC44oRhjVc6kNMi9uv4NMPXTkWEdHsMMU0ThjF7+/jxRtd1aNo0OxQLTgmajQlJkiCL7v8CgUAgEJwwZOnULvUVOPgJcMF6krWXU120TEQaHWFTYTsoCSuZnmw6ibQ4iMfPaVlfn2hw6nCZuI/IuihJcCRLoddvUw/texXP/e5O6BMvApVRbNmyBZs3b0YsdvKUpc6kzH0q0bm3g2DfCIFmUJzWzbI0FrRL2DM89X2/N9hppvCC6ZEMRdlHKJtWgvgkq1oyY0DPFajIVQBM3QwFmk8PcE5Lbuyf2J6W7tdBlbjKui1BPsEJ8yM5E9kywcCQo+iOFVXbcRqSq5C0cVyz6Am86Yz1aA9VgeGHEJXOwjgSKFYBk1JITfoGZyJvQoXb72PVdqQiTuT4ZA9ETnWEaHkMoJQim82iXC439Oecy1SrVei6fqJXQzCHmGxMqKqKWCyGcFg8FhIIBAKB4HijCqeloI73Bh0AetoJDow13oec6m1Q2yIEMpmZC8wSbKJBMqkgGVJb7y7z61trucgiQdLUPWnhJ1p2t0sIqhR7hpk4k0sfRjweR0jtRK1oYseOHejv78fNN9989BswR5iJWDiVL0OWCBYmCdIFoDvBLsI9CYI9w1Mv+2idlr1JJoybFBjJmNB9HJOjualL1VmCuP+6WKXP1v+KPLnoS+Ds37lSHi5LBIoE1/7piDpl3dZ50Zd0yu1/8oSG7jb3L9XxSjsquiNaWuniyXAN2PMtoJZGjHQDSIBSoFyuIBrxvzc+kgsAcBpkjlU70Rt0ksH9zlUBQ4iWx4ByuYxSqYR4PI5gMDgnUgWnQ7lcFgKUwIXfmKCUwjAMlEolTEyw5sFi3AgEAoFAcHwhhMyo5FFw8sL3Z6yUK/j/27vv8CjK723g92zfTW+EJEAIJDRRigRFSoKCyBcVsGFAARtdQVBRsWDFn4CKICKiIIq8FqqKFKkKKKCIIEoNKqGlQAKpm93n/WOzk63JppBMyP25rlyQmdmZZyYnCzk5z3O2btsKTc4fKA68Bkk9kmBwmNpZm1M2lUCrlhDkV7FnYK8iK68SSqWS5IYk1cXbmpYAfJqS7GnMYf4SggwWOclmzsuCn0YNiGLo9XoIIXDq1KkqjVtpwgNVCPHzvAakIwm+VWXGhKoQ4ld6LoNOQrBJwoW8ss9f1W7seq0K4YESzmULnL1ggUqyAtAgIgDIyrVNafap0tIlrymEkHMW9mpE+5/lxbReW5rMd0pa1vIv1XQaCcUOnb+D/SScOm/73P590bWVGtsOFuNstsA/6QL/pDuXoGYUBqGgpNLSqC6E3F08Y2dJl28BP22efHzu6d/h17wLPDlzwfnrkl+swdmc0nVmWWnpHf+rU82EEMjJyYHRaERAQEBtD6dCiouLodVW8Z2UrihlxYTBYEBWVhYuXbrEpCUREVEtqOoPwHRlsE8PL8gvwMyZM3Hk8CGYT2+BpsH12LN7DyZNmiQnLuv79HAACA+o2EOwf5/5Ugnl69qGvvL09ZIrP8toCmTnaZ28IEMxtqxdCoSmAADyMv5Cfu5pNDIWoriwEJIkITo6uirDVqQ2jdTYddQCs8V7Uk/j4zRyvVZyq5qMDFbhQp73edcalfeEaGhoqE/X1aiAhkEC57IBs1UFe4uSUHUaJP8onMtRISPHDHFmJ6QI5w7fjhxXsBNC4PR5geiS9TLtU87tVYnGcqpDHaeOK6V7OADotECeQ+fvuAYqHDxphUELubu6TiNhaHeBGd9aITy0e0kvCEWh1XaDBnWh3F0cp9fAluIW8NPky8fn5uW7ncPu9Hn3Ctj/Sqrh1Sr43FyrPmLSsppZrVZYrVYmceiKJ0kSTCYTzp8/D4vFwjUuiYiIalhF1mmjK5c9abl121YcOXIEAf4GGGNiUKCx4MiRI9i6bSv69OkDgElLAAgPqNj3jU5jm1LuS2MaXxKJFeG69qZKKm285EuSI8DDeE6m7se/B7fBv2E2VGotpNzjKCwoxPG0w/AzGZCYmIjBgwdXx/AVRa+V0DJahQP/eU8slrVmaXkaBEk4cto2ddvb9b1JSUnx6RpqFdBAewqAc1I5RJsJTXERziEORRYNck7tQlDOPqD5KI+JS8dGPEXFto7o0SXrZeYVCQgh5PVSy1vP0jGpqVLZvlcsovbfa2zfJ6VfjN7tNAjxl9AozNZAxy5O9TN6R1ux/tQNAACVZEGE4TzO5ofLVZYAYDT5lz5PbYh8bsek5SUR4nU8rpWWQGmsBBhRZ2bn1gb+s1XNrFbbO4CqtuuhiWqAPVFpj3siIiIiqlkqlQStWkJWZhaEENCpbT8J6yTb+vpZmVkOx9bWKJXDoKt4csCkl+DnQ5KwKkkvT1yn8+sdlgLQqiWnzz3xlGg1Xzpri5Oi/6DJP46ohlEwGHTo0P4aTJ069YprwuOoQZDKrZO0nVoCWkZXvghDq5YQ5u/9G6w61izUqIGGxky37cG6iwjXlW7PKAi2TV/O2OnxPMUOmdVCM3CpQOBCrr3C0rbN3prDWM73i2tS016t6q0hTU3Ru5Tn6TQSbmipQZNwl6+R+Tz6Nd6OOP+TAIDkhnvQzD/N7XxG/7DSBHB4F1vVJST4awvkY3J1rbyO58wF7z8vcz3LsrHS8jJhppzqA8Y5ERERUe3TqoHQsFBIkoSiwlwYARTlpkPSRiA0rHTqaW1XP9VV4YGST//vre7p4a6Vlq5LQvgZgMJL3l9v0NrO4dgtOirMAEmSYDabodVqYbFYoNeqMXjw4Cuq+Y43QUZJTtA5ahaphqmKX78GQRLSL3reV9aahbt27QIAdO7cuczzq1USIoPdzxOiz4GfpjQ40guD0RyngMJ04OxmwHzeVh0Ybpsy7lhpWWC2PYu0LCsCjSoUmoW8niVQ/pqWrjGvUQOFxQqYHu6a0LcU2JK4Ls8C2hBoVcV4/KrPcK4gFA2NGViX1tXtfEadLTFttgjb65qPAjJ2wi+v9DcVuUWe02uXCgQuFZSex/H5AlzPsjz8Z4uIiIiIiKgO02mApB5JSEhIQHZWOtLS0pCVcQ4tm8cgqUeSfFxtVz/VVRGBvv3YXNXu0K5cEz+u5zeVUwUnSZLTFPFAI3BjcnckJCTg4sWLyMzMxMWLF9GqRfMrckq4J/4eEkTBJgmNwqr+tQsPtE2P9iSojOUFdu/ejd27d5d7frUKiGzS1m17iKEAEYbz8ucZBSXTl3OPAumbgQv7bH8emwdYCpzWtCwsmQaenmNFdr6AQOl6lkDZlZYSgBB/5/325Spqu6rbqWO8pcB27w7PQnN8rm17SdWkRiUQbcqCSpIQbsx1O59JJzn/0kBtACJ7wi+6NNF8qdDzWByrLK9q7F7Ny0rLsrHSkoiIiIiIqA7TqiUYjAZMmjQJ+39ugfzMw4iOjsYNvVNwLre0Esi1co9841/Na1X6qvxKS9+a8ZwvycGEBZTGydZtW5GVmYXQsFDcfkvSFTsl3JWndT7jGqiqZQaZWiUhPFCFs9nOU4FVEnxaXqA8GjXg72eEvyFfrtwDgODm/aA6vVL+PC2vASCpAYt9vcWSJGTJlPHi0JvkYwuLbfusAjh+xjbufLNjZ3Tv4wk0Sm5rK9uruWu70rJBkIS08yVVtQ7dvu3Pwl+cxIWMnUBkT7lqEoXpQO5RhOky3M7nZ5DkhKzrdrvcAocKXofKzjNpLQE0AwA0i1Thz/8sTtWWrLQsG5OWREREREREdZi25Kc6g9GAoffdi6gQW+bgQq7AudRi+bjaTiRQxWhcqtVc17D0lIBzZUuI2JIpYSWd0w1Gg9ycCQACK9hRvS4z6aXSab6wfU+UVQVZUZFBEs5mO28LNEpQVcM3nz0hGBmkwqWC0sRoaEggNCH3QL/XgkKLGvuyWmLb6avRo+FelzNIgPm8y/Tw0r9n59ueSYGPCTXXKkvAvqalqPWlKCRJQtvGto7xRebzsHf7tvPTFOCCuaQ6taRqEmc3AzkHEKY/73Y+kx7QujT3AZzXsc0tFFj3uxnHzhTjnqjPEa7+B4CEM+lhsCcto4IlhAVIOJlZeh4mLctWf96diIiIiIiIrkA6h1IUx2o818YwtZ1IoIpRqSQ4pjNcKy0DjO6JTZNeQtMIFSICVNCqJaepp2FeOqfr6lkFrp/D90WQqXoSinah/u7Vh9U1/VdOWjqsa6nT2NZJ1OmNGHBd6Y19kdoHv2e1kD+3Cgm7M1pjz7l4mB2ylkVm5yQc4Dw9PMik8vrLjjAPyW6NQqaHA7Z1La9qrIKkC4ZjslEFYev6rXXp9l2S3AzU5kKrMjvtCjBIHr9PHNf0/OukFat2F+PAf8CyI9eWXNOKM3lh8jENQ1QID3CNj8rdX32hgFAiqh7h4eFITk6WPx8+fDgkScKlS2WsTu3FsWPHnD6XJAl33XVXVYdIRERERFTtHJMkjg0oNGrJ6XMlJBKoYgwO61gaXNa0lCTJrUN4g0AJzSLVuDpWjQZBEoza0gUMz/x7EAX5BXCl07ptuqI5VrZ56yZeWSqVhAaBzuf01MW9Muzd5CODSs8X4lfaJCrpKg16xx0HAAio8PHhAdid0QbFVhUWHbkdC4/0x8e/JuDP/0qTlgXOuTnbNodKywCDe9wBtvecQA/JNnlNS4UUD4b4qRDU+Aa52zeggl5dDK2ppBmPI61tLVBJAsL1F5x2+RtLK9od6TSliVrHZ3kwuxmKLLYXnMkPBwAYNWYEGoFwlzVyWWlZNk4PpyvWyJEj0atXLxgMhvIPLmE2m9GnTx80adIEixYtkrd/+umniI2NvQyjJCIiIiKqGsdKS53LT3gmHVBUMkPcW5MQUq6YMBWOnrElHl0rLQFbE5msS6VVZKH+pQmR4qI8/L59NRB8O4Sw4ucfvkDGX36YNGkSDMbSn5HKarZyJXKcVh/iV/2Z/OhQFdLOlyYGy0tKNW/e3KfzllZalo7ZNenav+05ZF/Mw66MtigWGiw8MgDfGzPkxBkAnDhnG5sQAoXlVFr6GyQYdUCeS8frYIdkqSNNyZuMWilZSwAGo6l03UrzeegD/KBrmwT855InCO8CZO8DirIQZsjG6fwIeVeAQeVWQQvYfnHgbwAuuPTuMVu1+Cs7Do1M55BVFAQAaBhQAEkK9FBpqZxnpURMWtIVq0uXLujSpUv5BzooLCzE5s2bMWzYMKft9913X3UOjYiIiIio2tgrgFSSc6UlYEtIXcgTUEmo1mmwVDOiQyT8ky6h2CLcEtIAEOynAmBLQmlUQJCpdN/3367CyQNr4d/oAtQogkZ9EUeOnMLWbVud1rT0lAy9ktk7iKsleKwWrKoAo4SYUBXSsqzQaaRyk8K33HKLT+e1LwUQEyrJKzQ2DHY+tyqiC+5vPR+aQ8XYca49ADglLAHgYsnalUXFris02jg2iQkwSjDo3NdyDPWwnqV9jEp7mzFoUbpuJQBtoApavQpAsfOBaoOc3Aw/pQEclrYMNLr/QsjOT1/S8MfFH1ktkHqxkfx527gAAHBLWlZXJe6VihMEiIiIiIiI6jB7BZBrwhKwrXcHKKvyiXynUUuICZWg13qubAs0liaJQvycu2CnnzsNIazQ5R2COi8VWq0WQghkZWY5ncPoYfrvlcykK0nw+lXvepaOmkeqoNdI1Tr1V10yDTksQIVBXbXoHK9G73YumTS1AeqEERhyXQHuan0QUkmy0TF07ElLT1PDAddKS8DkIenqLWmpVivvvcZ1erte4z0BaU9uhkUlOG0OMEoep4cDzutaXt1EBX3Jcfuz22BHRkcAtu/RG1rbKjvD/UqzwipJwKRxX7KBSjFpSdUmOTkZ119/PTZs2IB27drBaDSiVatWmDt3rnzM1KlTIUkS1q5di8aNG8NkMmHKlCkAgKKiIrz00ktISEiAXq9Ho0aN8Nhjj+HChQtO17FarZg+fTpatGgBo9GITp06YdOmTW7j8bSmZUZGBsaOHStf+6qrrsKcOXMghMCJEycQEGD77ccnn3wCSZLkKeKe1rRct24dkpOT4efnB39/fyQlJWHdunVOx9jv959//sGdd96JoKAgBAQEoH///jh69GilnzURERERkZ39B3C9hx+qTSU/UCssj0AV0ChMBZPe8z6VqnRdS9duztENIyBJEsxmW3bKbDZDkiSEhoXKx0iof5WWtim9EoIvY4WbRi2hZbTKp0rOtWvXYu3ateWf0yF706ONBsN76pyWA5CpDZAa9sSN3a7FxNv1uLmdBiN76+Tdlwq9Tw0HXCstVTDonPebdN6rR7Vq5wSpEriOX6+VbOMs4zWu1ZBBJpXHXwoBQFSIbbtGBdx5vRZtGtu+JpeKtLhUZPvGbR+ntnWptxQg9NyHcjI5QHMJquMfABYmLr3h9HCqVqmpqbj99tuRkpKCRx55BF999RXGjh2L9PR0vPjii/JxQ4YMwfjx42EwGHD99dfDarXi9ttvx6ZNm/Dggw+iQ4cO+OuvvzBv3jxs2bIFO3fuhJ+fHwBg7NixmDdvHm677TZMmDABv/32G/r16weLxeJtWACA7OxsdO7cGSdPnsSoUaNw1VVXYf369Xj00Udx7tw5TJ48GQsWLMDDDz+M7t27Y8SIEbjhhhs8nmvBggV45JFH0KZNG7z44osQQmDhwoXo27cvPvroIzzwwANOxyclJaF9+/b4v//7Pxw+fBjvvvsujh49ij///LOKT5yIiIiI6jt78wtPP1Tbk5bsHF536TQSmkZ4b/EdbLJNT3XtDp4y6E6s3nYCR44cgRACkiQhISEBST2S5GMMXio4r3T+BgkhnhJ+1Sg8UIVAk+fEoCPXJrDeSJIEtQRYyj+lrHmkGs0j1cgrLH1RboFAsRUo9FJpWeCQzAw0ua95GlRGslejkhS3dq7etdJSa3uWWo2EomLPD9P1eynQVPo+6+q2TlqE+Elo3qAYDQq24poAI/aindMxPVqXvDhjJzTFGYgP/BdHcmLRPPA/qIoyobnwE4rCesnHqyTv16tvmLSsBXfOuISMnAq809QQIQQigixY9oR/pc9x7tw5vPLKK3juuecAAKNGjUJycjKmTZuG0aNHy8eNGjUKL7zwgvz54sWLsW7dOnz55Ze4++675e233XYbevXqhbfffhvPPfccDh48iA8++ADDhw/HwoUL5eOuvvpqTJgwocyxvfnmm0hNTcWaNWvQt29fALZmPX379sXMmTPx1FNPYdCgQXj44YfRrFkzr+tYnj9/Ho8//jjatGmD3bt3w2SyLRwzduxYXHvttXjssccwYMAAhISEyK/p3bs3PvzwQ/nzvLw8fPDBB9i7dy86dOhQ3mMlIiIiIvJKpZKgUXnuAm2fHs7O4XVbWV2uQ/wknMt2r34LCfbHpEmTsHXbVmRlZiE0LBRJPZKcmvDoda5nqx+C/Tx3v65u3qrzKkutlmBxSbRp1RLM5WQyDTpbBaQQQG6hgMUCFHpJ2DlWWgYaJZhcYqSsNRg1akBS2HuNweV90V6ZrlWXNilz5VhpqVXbkvv26kzXp2bSS7j5agtw7AOgKAttdUaocDWsJRObGwZLSIgqeSjm8wAkjGixDEcuNkGroFRoVBYESWeQ7nBOf8PlW7qgrmHSshZk5AiczVZe0hIAJKlq4zIajZg4caL8uUajwYQJE3D33Xfj+++/l7cnJyc7ve7rr7+Gn58fkpOTkZGRIW9v164dGjVqhNWrV+O5557DmjVrIIRwSoACwOjRo/Hss8+WObbVq1ejRYsWcsLS7sMPP0Rubi6MRiPy8/PLvccNGzbg0qVLmDhxopywBAB/f3888cQTGDFiBNavX49BgwbJ+xz/DkBOVJ45c6bc6xERERERlUenkTxOD1erJOg1ktPUUrqyBJokt8owwJbMMhoNTk13XNW39SztIgLrZoWpRgW4NPJGoBHIvOTxcJlKsiUfcwttH8XW8te0VKtsCT9VyXuIPclZZqWlGoqrtFSrJKfErj2RrNPYnoUnBp2EACNwMR/wM0i2ZKwkQeMtQZyxEyjKAiDgp81D88D/cCQnFgDQvbWmNNa0ISXHFKB96GHbJlUxAgICnZKW7CheiknLWhAeqMwAFEJUeWxxcXFOiTwAaNGiBQDg+PHj8jdrgwYNnI45evQocnNz3bbbFRXZ3ppTU1MBAPHx8U77dTodmjdvXubYUlNT0bNnT7ftjRo18nB02ecBgFatWrnta9OmjdMxdq73pdfb1rYob0o7EREREZEvtGrvVV1GPde0vJKpVRKahHvOSpc1BRZwX++vvqiLCUvA8zIPRp0EnQZlfp0BW8OY3EKBvEIBixUoKmdNS4PWtjYnYHsPKSy2JU39vKyvCtj2K3EpCoMWMJf86G1fw9X2fun9md3SXovv95rR+xqN/Bx0mtLzOCmpoLSfr0/MDhzNaYyG/rm4vkVE6XHhXYDsfSUJTtvxWkMQguJuBk6WHlZWYri+YdKyFlRl+vXllJ+fD6OxajXyWq37nBR7Yk6j0ch/V6vVbsc0atTIacq3p/Pa/3HxVBEpRNlv0sXFxdXyj5P9Op6uZ78/e1LSTsX5OERERER0GWk1kteGKiadBLOXaZB0ZXBdt8/Olszy/jrXzsqkbGoP6xzqNIBRW/bXGQD8DABybEnJQrPVa6VlQUmlpVEnyZ3ATTrbuqkBxrIrVFUqSU7wKYlBK+FigShZK9I2vvLWjOzZVoPkq9QlFZa2bVoNAE/VmSUVlHZtglMxI/EdqBt0hU6XXHqc2gA0H2WrzDSfB7Qh0MZ1QUCAPyQUy2dgpWUpZlKoWp04ccKtevDIkSMAgISEBK+va9q0KTIzM9GjRw/06tXL6SMnJ0eu3rRXUx46dMjp9cXFxThx4kSZY4uNjfXYsXvbtm24//773c7pTVxcHADg77//dttn39a4cWOfzkVEREREVB10Gu+JK6NO4pqW9VR5iZn6WmmpJImJiUhMTPTpWI2Hkmm9VoJRX36Sy+RwzPlceOweLoSQKy2NutKqSfu6kL5UAOoUWBpn/4WO3qEa3Zdx2hO09u8jrUtC1t9Q8nl4F0AXClv1pAqABKPJD7rI691PqjYAkT2BRncAkT2h1ZugUUvy10erluDnw9ezvuA/XVStsrOzsWjRIvnz4uJivP322/D393dbS9LRgAEDkJ+fjxkzZjhtX79+Pe688065ic2AAQOg0Wjw5ptvwmq1ysctWLAAly6VvZDHrbfeir/++gtbtmxx2v7OO+/gyy+/RFRUlFwBWta07d69e8NkMuHtt99GXl6evD03NxdvvfUWjEYjbr755jLHQkRERERUnWzTwz3vM+mVOWWTLr/yGsGw0rL2de7cGZ07d/bpWE/fxzqNe7MZTxwTYecuWD1OjC4qBqwlOwwOv+ywJ0UDfagAVGLS0lDSpMqxGl1bgSZJ9ufueG8GrWRbE9ZSYKucNEQDpqZA0FVARE9bRaXa4PF8juxVnPZnG1ADDaLqEgWGE9VlarUa48aNwx9//IGEhAQsXboUP//8M95//30EBQV5fd1DDz2EJUuWYMqUKdi/fz+SkpJw4sQJvPfee4iKipK7kcfFxWHKlCl46aWX0KtXL9x5553466+/8NFHHyE4OLjMsT3zzDNYtmwZ+vbti3HjxiE+Ph5r167FypUrMX36dAQGBgIAAgICsGnTJsyfPx/du3dH69atnc4TGhqK6dOnY+zYsUhMTMTw4cMhhMCiRYtw+PBhzJs3Tz4XEREREVFN0Gsl72ta6iSoVcpsBEqXl2ulZai/hKxLtliQ4Fuyi5TDU0MtvVaCqYx1Ju38HPJn53IEQvzd3y8cO4cbdaVNdewNm8rqHG5X3R3Tq4M9WamtYKUlYFsP2D5N3vH7yaQHdKo84Ng8pzUqoQsFom/3KWFpO2fpsz19wbfEcH3C37dRtQoODsaqVauwfv16PPnkkygsLMTXX3+NUaNGlfk6nU6H9evXY8qUKdi9ezfGjx+Pzz77DAMHDsT27dvRpEkT+dipU6di/vz5OHv2LCZNmoTNmzfjiy++QGxsbJnXCA8Px86dOzF48GAsXrwYjz/+OE6cOIHFixfjiSeekI975513IITAo48+ihUrVng815gxY7Bq1SqEhIRg6tSpeO211xAVFYXvv/8eI0aMqMATIyIiIiKqOr8yfj426VhpWV+5JmZiQlVyUya9tm520L7SLF26FEuXLvXpWG9rWvpSMetYaZl50VsTntLtRp0kX8+ot61r6UtC0tvaurXJ/nz0Dt8PrlO9vXFco9Px/v30EnQZ6+Wu4YDV9mdRlq3y0kdal0pLJi2dSaK87iVUIWazGenp6YiIiPDYlEbJqtqIJzk5GQcOHEBGRkY1jopqU3kxUZfjnSonKysLoaGhtT0MUhDGBLliTJArxkTNMFtEmT+En7lgRcNgZWQuGRM1Jy3LikOnSpe+6tpSg4MnLTifKxDsJ6FjnHImX9bXuHjvvfcAAGPHji332NRzFqSeK10mTQLQs60WRcUCP/1ddieerX8W44sdtu47w5O16Jzg/rX/O82Cd9fYyi1vulqD2Q8aoSrJch8+ZUGL6HIWSQWQnSeqrft1dcVEoVlg+6FiNI9UIzbC9j6YVyjw85HyO5SZdBKub2F7VmezrfjzP9v3U8toNXBsHg4dOwlbwtJOBQS3s61Z6YO2jdVoEKSCEALbDhajS0uNIqtVa4sy/tUiIiIiIiKiSiuvakhuGEH1imOlpVqyVVeGB9jSAEauZ1nnuDbisSe3dBoJ5RUOOlZjXyyw/Xkqy4qjZ0qT2mlZpTVt0SGSnLAE4PMvPfTKyYPL9FoJKsm5CtTX6eEahzytznV6uCkMcFsdVJR0E/eNtmQckiQhPFDFhKULJi2JiIiIiIiucH4+rHlHVx7HZLa9GUlYgHtTEqobXJd5cGoMoys72eXYPTy3QCAjx4rXlxfirW+K8Mc/tsTlqazSisHGYc4X82U9S9cxKYleIzmNTaOW4KEZuxvHpKXWZXq4vsn/3LqGQxdq6ybugafrOX6Puj5zYiMeIiIiIiKiKx7XLqyfHJM0ppKklkkvwaSXYCwnyUXK45q01DtUy5p0EnILva/+5++YtCwUOHLaKncK//2EBdfEqpFWkrSUAMRUMoGm8iUTWAv0OvcmQVq1hMLisldMdF7TsvR1Oo0Eq97f1iU8YydgPm+rsAzv4rUJj0kvIa9QyM/ddq7Sv/uaGK5PmLSkarNly5baHgIRERERERGV0Dr8xG90qLaNCJDYObwO0rl8zZwrLct+rWOH8dxCIMOhGc+Jc1ZYrQKnz9u2hQdKTo17rgQGreRWXazTAIXlLGvp2LHdnmA06UpfD7UBiOzp0xh0GqDYIqHAXPrsteUvE1qvMWlJREREREREdAXSlkyBtQo4VVaGBag4PVwh+vTp4/OxrolEx6RleZWzfg7r2uYVCmTklO47c0Hgv0wBc8nyltGhElRX2Exlk8597V9fprI7Tg+XJAlatSQ/S5XK9rnZ4lt/a71GgsUiUGDrhwS1pNzKVKVg0pKIiIiIiIjoCmWfAmt0qMQLMtXeeMhZfHy8z8fqNBL0mtIpzY7TnY3lVFoatJAT2LkFAoVm50TbzsOlJYfRIapyG/vUNZ46mtvWqCxnerhL8larcV4fVKuBnOwtj04DWIQE5AuH61NZrrDcORERERERERHZ2aeIO1biSZLEdU7rKMcu4I7VsuVVWkqSJL/WdXo4AOw+Wpp5iwlVXXGVlgFG9+fjW6WlS3WmunR6OFCxbum2pLPjuX1/bX11hYUhEREREREREdnpNLYKO65hqUzvvfce3nvvPZ+P9zc4NoZx6A6vtTXQKYu9QjAnT+BivvO+/KLSv0eHStBcYdOWXZOPgPt0cQCICFQ5bXddc1KrkZwqLV2b+5RFp3FunsT1LMvHpCURERERERHRFUqrlmDQsrLySuG4rqVj1Z5KJTklxDyxdxAvtno/RqMGIgKvvDUtPfFUadk4TAU/h6ZFrh3bDVrnqfi+VGvKx2qdq2M5Pbx89SAMiYiIiIiIiOonnab89Q6p7nCutHTeV97X2bGDuDcNgyWoVZJbsu5K5Pr8Ao0Sgv0kp6ZFrhWagSbnXwCUlyh2vp5tTVI7bT14xlXFR0RERERERER0hdKqy1/vkOoOP71tGrhWLbl1ng70sG6j02sNnqZDO2+LDrGlia6w2eEeuU4PbxRmu3d/p6Sl82uCXRr6VKjSUuNaaen7a+srJi2JiIiIiIiIrlA6jcSk5RVEpZJg1Esek2Xhgd5TPCrJeWq5Xafmzlm5mFDbMfWh0tLfADSNUEElAXqNhAYlCVzH5+S67qRrZaWvSUuVZEuSOk7p97SmJjmrB2FINenMmTPIzc2t1nNKkoS77rqrWs9JRERERERUH2g5PfyK4+claRlo9N4YJiJQhQCD+3bXpGV0qC1NVB+SliqVhGaRaiTGa5AQpZIrV/0dnlN5z8HXRjz241QqSf47u4eXj8WoVG1WrFiB4cOHY9++ffDz86u283766aeIjY2ttvMRERERERHVFzqNxOSIgqWkpFT4Nf4GIL/IPVkmSRLCAyScOi/c9kWHSAhwmT6uVdvWsGwQJOFctu01MXLSsv5UAfrpJafqSk1J86pCs/DYddyRr5WWjhWWeg1QVMzu4b5g0pKqzfbt25GTk1Pt573vvvuq/ZxERERERET1gU7jnDAhZQkNDa3wa/wNEixW98QkgJKkpfM2o1ZCiL8KQS7rMYYH2prKdG2lxopfitEyWoUgk21ffegeXhbbMy7/OJ1GggTA/tXQqDx3Z3esyNRrJVwsKD8hSkxaEhEREREREV2xuJ6lsmVlZQGoWPLSTy+hoMjzvlB/CWoJsDjkNKNCbDHgmrS0N+HpfY0Wic01CDRB7oxdH6aHl8XfAOQV+nasTiOhsFhAp5EQESAh7bx71tKxItPejIeNeMpXz8OQqsvw4cMxc+ZMAEBcXByaNm0KAJg6dSokScLatWvRuHFjmEwmTJkyBQBw4sQJPPLII4iNjYVOp0NQUBBuvPFGbNu2zencrmtaNm3aFMOHD8eyZcvQoUMHGAwGNG7cGM8//zyKi4vLHOeiRYsgSRKWLVuGli1bwmAw4P777wcACCEwe/ZstG3bFgaDAQ0aNMDw4cORlpbmdI60tDQMGjQIMTEx0Ov1SEhIwLPPPov8/Hyn59GwYUP89ttv6NKlC4xGI+Li4vDyyy/DYrE4nS87OxuPP/64/BxiY2MxceJEZGdnuz2HqVOn4v3330erVq3ka7/zzjtOx5nNZjz55JOIj4+HwWBAVFQUhg4din///dftuhMnTkSTJk2g1+vRrFkzPP/88ygs9PGdmYiIiIiIiKpk6dKlWLp0aYVeY9QBBi/rlKpUEkL9S1M9EoCoko7gQX4ulZYBpZ8H+0lQSaWf1/ekpZ9e8vkZ2BOSEYES/L10cNe6TA8HOD3cF8zrUrUYOXIk0tPTsWbNGrz99tty0tJuyJAhGD9+PAwGA66//npkZGTg+uuvh0ajwejRo9GwYUMcOnQI8+bNQ58+fXD48GE0btzY6/U2b96M5cuXY8yYMRg9ejQ+++wzvPrqqwgICMBTTz1V7ngfeOABOWFqv86IESPw0UcfISUlBePGjcO///6LuXPn4ocffsDu3bsRFRWF4uJi9O3bF6dOncL48eMRFRWFnTt3Ytq0aTh58iQWL14sX+PSpUvo3bs3evTogRkzZmD9+vV48cUXkZqaioULFwKwJQ67deuGgwcP4qGHHkKHDh3w66+/YtasWfjhhx+wfft2BAQEyOdctGgR8vLyMG7cOERERGDevHl4/PHHER0djXvuuQcA8Nhjj2H+/PkYN24crrnmGqSmpmLWrFnYsWMHDh48CJ1Oh9zcXCQlJeHo0aMYNWoU4uPjsWfPHrz++uv45Zdf8P3330Ot5jsoERERERGR0kiShJAy2kiEB0pIv2j7e1iASu54HeqStAwL8J6Vq/dJS4Pva8Hap3tHBqkgeSlsduw6bv87k5blY9Kyjrp06RI+//xznDp1CtHR0Rg8eDD8/f1rbTxdunRB69atsWbNGgwYMMAtaTlq1Ci88MIL8ufTp0/H2bNnsWvXLiQmJsrbmzdvjlGjRmHz5s0YOnSo1+v9+++/+Omnn9C1a1cAwP3334+YmBgsWbLEp6TlbbfdJleGAsC2bduwYMECvPnmm3jyySfl7ffffz/at2+P559/HgsWLMDevXuxf/9+TJ8+HU888QQA4OGHH4YQAseOHYPVaoWqZPGP3Nxc3HvvvViwYAEAYOzYsUhJScGiRYswfvx4tG/fHtOnT8eBAwewaNEiDBs2TL7uddddhxEjRuCNN97Aa6+9Jm8/deoUDh06hLi4OADAgAED5Pu2Jy0XL16Mvn37YtasWfLrGjdujLlz5+LYsWNo3bo1Zs6cif379+Onn35Cly5d5OO6dOmChx9+GEuXLuVaokRERERERApV1nqIEYESNGo1tGoJJn3p9hDXSstA7+dQecu+1RN++op0BgcMWgnBfhKKLZ7XGnWcHm7QAlq1JE/FJ+/qee68brp06RKGDBmCqVOn4oMPPsDUqVMxZMgQXLp0qbaH5lVycrLT508++STOnDnjlLAsKipdlKO8e4mJiZETlgBgNBrRsmVLnDlzplLj+frrrwEAt99+OzIyMuSPiIgIdOrUCatXrwYAREdHQ6VSYe7cuVi2bBlyc3MBAB9//DG2bt0qJyztnnvuOafPJ02aBABYtWoVAGD58uVo3LixW4L2oYceQqNGjbB8+XKn7Z07d5YTlgAQFRWFyMhIp/tu1KgRNm/ejFmzZsnbR44ciX379qF169by/SYkJCAhIcHpfvv27QutVivfLxEREREREdUtGrWEiEAVgv0kp8Sbv1GCxuFHVsfp4a7qe6WlJElua4B6o9MAkUG2YzVqCUat++scvw46rfPXgbzjY6qDPv/8c+zevRthYWGIiYlBWFgYdu/ejc8//7y2h+ZVgwYN3LZZrVZMnToV/fr1Q+vWreHv749Ro0bJ+yp6Pr1e77ZepK+vP3r0KACgVatWiIiIcPrYsWMH0tPTkZ+fj5iYGMycORMnT57EXXfdhbCwMPTu3Rtz585FXl6e0zlNJpNbxWmLFi0AAMePHwcApKamomXLlm6/YVGpVGjVqhVSU1MrfN/z58+Hn58fJkyYgOjoaFx77bWYOnUqTp486XS/hw4dcrvXmJgYmM1m/PPPP748RiIiIiIiIqojtGoJfobSz8OYtCyT70lLCZHBpQ/M3+ApaVn6d72GTXh8xcdUB506dQpCCOj1tjpvvV4PIQROnTpVyyPzznV9xO3bt6NPnz7Q6XTo3bs3Bg8ejPbt28NqtWLAgAHlns+1orGq47FYLNBqtVizZo3X12i1thZfEyZMQEpKClauXIm1a9di06ZN+OGHHzB79mzs3r1bnqZvP971OgCg0di+9YQQEMJz+bjFYpG/xna+3HdSUhL++ecffPfdd1izZg3Wr1+Pl156CTNnzsSPP/6I9u3bw2KxoFOnTpg2bZrHcziuo0lERERERER1n1YDNApTITvPisbhUpnTn5m0tHUQ90WIvwQ/femz9DNAXlPUTu+QfdOoJRh0nBruCyYt66Do6GhIkoTCwkLo9XoUFhZCkiRER0fX9tB89vzzzwMA/vzzT0RFRcnba6tatGnTpjCbzWjZsqVbA6C1a9fCaDRCo9EgOzsb+/btwzXXXIORI0di5MiRKCoqwlNPPYVZs2bhm2++QUpKCgBbk53MzEyEhYXJ5zpy5AgAICEhAYCt0/rhw4chhHCqtrRareU2I/LEbDZj3759CAkJwV133SV3Xf/yyy8xaNAgvP/++/jggw/QtGlTnD9/Hr169XJ6vcViwVdffYWGDRtW6LpERERERERUcWPHjq2xa2nUwJDuOuxNteDqJmVnJatYJ3RF8HXNSceEJeBeaalVS1CpXI5xrk8iLxiGddDgwYORmJiIzMxMpKWlITMzE4mJiRg8eHCtjstevejLFO3MzEyEhoYiMjJS3lZYWIi5c+cCAIqLiy/PIL2wV3e+8sorTtv//PNP3HbbbXIznJ9//hlJSUlYtGiRfIxOp0OHDh0AuFdwvvPOO06fT58+HSqVCnfccQcAoH///vjvv/+cuo4DwMKFC5GWlob+/ftX6D7y8/PRrVs3ee1Mu+uuu85pfAMGDMCxY8fcksT27ulc05KIiIiIiOjKolXbmsX0bKtBeGDZ6aAy+vxQOdySlh7KBT1NISd3rLSsg/z9/bFkyRJFdQ8HICcg33jjDdx4441yxaEn/fr1w7Rp0zBw4EDceuutyMrKwuLFi+W1Hi9evOj1tZdD3759cc899+DDDz/Ef//9h1tvvRWZmZmYM2cODAYD/u///g8AcNNNNyExMRFPP/00UlNTcfXVV+O///7D7Nmz0axZM9x6661O550xYwZOnjyJxMREebr25MmT5bUtn376aaxYsQIPPvggduzYgQ4dOuC3337DRx99hFatWuHpp5+u0H0EBgZizJgxePvtt3HnnXfi5ptvRm5uLj788EMYDAaMGDECAPDMM89gxYoVGDp0KLZu3Yprr70W+/fvxwcffICrrrqqRn/bR0REREREVF/Z+yvEx8df9mtp1eUfAwAS4FYZSL4z6mxJX3sjcR2TlpXGpGUd5e/vLyeglGLYsGH4/vvv8emnn2LFihVyNaEnU6dOhdVqxdKlS7Fu3To0bNgQXbp0wapVq9C1a1f88MMP8hTymvL5558jMTERCxcuxKRJkxAcHIyuXbti6tSpaNeuHQDbWpTfffcdXnrpJaxevRoffPABQkJCMHDgQLzyyiswmUxO51y3bh0ef/xxLF26FM2aNcO8efMwcuRIeX9wcDB27NiBqVOnYsWKFVi0aBFiYmIwceJEPP/885VaW/LNN99EVFQUFi1ahLVr10Kr1eKGG27AokWL0L59e/m6O3fuxNSpU7Fq1SosWrQI0dHRGDFiBF544QUEBQVV/kESERERERGRT9atWwdAWUlLDcssq0SSJPgZJOTk27KWntYO5ZqWvpGEty4gVClmsxnp6emIiIjw2IhFyfLz82E0Gmt7GFeE4cOH45NPPsHFixdrvQK2KsqLiboc71Q5WVlZCA0Nre1hkIIwJsgVY4JcMSbIFWOCPKmvcfHee+8BqLm1Lbf+aZYrAL3RayR0bVX7NW51OSb+TrPg1HkrAKBxmAoJUT5mjMkJ17QkIiIiIiIiIqoHfKmiZOfwqnNszlNWl3YqW+2nzomIiIiIiIiI6LLTaoDCcvresnN41QWaJAQYJKgkwN9Q26Opu5i0JCIiIiIiIiKqBzQ+zFLWMGlZZUEmCYnxTLlVFUOR6DJYtGgRhBB1ej1LIiIiIiIiurxCQ0NrdN1GrQ9dwVlpSUrBtC8RERERERERUS1ISUmp0etpfcgCqSWuwUjKwPw5EREREREREVE94Dg9XIJzwxg7VlqSUjAUiYiIiIiIiIhqwa5du7Br164au57WoXu4QSshMsg9acnu4aQU1RKKW7duhUqlwoIFCzzuP3z4MB544AE0adIEOp0OoaGh6NOnD9atW1fha61YsQLdunVDSEgIgoOD0bt3b2zcuNHr8cePH8fQoUMRGxsLo9GI1q1bY9q0aTCbzRW+NhERERERERFRddm9ezd2795dY9dzrLQ06YHwQPe0EJOWpBRVDsVDhw4hJSUFQgiP+7dv346OHTti0aJFMBgM6NevH+Lj47F+/XrccsstmDFjhs/Xev3113HHHXfg999/R/fu3dGpUyds3rwZvXv3xscff+x2/J9//olOnTrh008/RVRUFP73v/8hMzMTzz77LPr27Yvi4uJK3zcRERERERERUV2idUha+hkk+BskGLXO1ZZMWpJSVCkUN23ahB49euD06dMe9xcXF+P+++9Hbm4u3njjDRw6dAgrVqzArl27sH79euh0OkyePBkHDhwo91r79u3DlClTEB0djQMHDmD16tX44YcfsGXLFphMJowbNw6nTp1yes3QoUNx/vx5zJ8/Hz///DOWLVuGo0ePokePHti4cSNmz55dldsnIiIiIiIiIqoznJKWJetZhgc6Jy19aDBOVCMqlbQ8d+4cxowZg969eyMrKwtNmjTxeNyWLVuQmpqKxMRETJ48GZJDB6revXtjxIgRsFqt+OKLL8q95syZMwEAL7zwApo2bSpv79atGyZNmoT8/HzMmzdP3r5p0yb89ttv6NKlCx555BF5e2BgID7++GNIkoR33nnHa4UoEREREREREdGVRKspzcuYvCQt1cxakkJUKmn5+uuv4/3330d8fDw2bdqEnj17ejzu4sWLSExMRN++fT3ub9GiBQC4VUh6smbNGgDAgAED3PYNHDgQAPDtt9/K27777jsAQP/+/d2Ob968Oa655hr8+++/2L9/f7nXJiIiIiIiIiKq6zQOWSA/ve3PYJPk1KCH08NJKSoVis2aNcPcuXNx4MABdO/e3etxAwcOxK5du/DSSy953G/vkNWoUaMyr3fmzBlkZmYiPDwckZGRbvtbt24NSZJw8OBBWCwWALb1LAGgbdu2Hs/Zpk0bAGDSspqdOXMGubm5l/Uax44du6znJyIiIiIiIqoJzZs3R/PmzWvsevZGPHqNBE1JolKSJIQFMGlJylOpUHzssccwevRoaLXaSl94//79WLp0KSRJwh133FHmsfZKzKioKI/79Xo9QkJCUFhYiOzsbJ9eY99+9uzZSo2f3K1YsQItW7ZEenr6ZbvGlClTcPPNN1+28xMRERERERHVlFtuuQW33HJLjV1Po5agkgA/g/P26BAV7GlLFZOWpBC1Eornzp3DnXfeCYvFguHDh6Ndu3ZlHm+v3DOZTF6PMRqNAIBLly759BrX410VFhYiJyfH6aOwsLDMcdZ327dvR05OzmW9xrp16+RqWiIiIiIiIiKqGI1agknnvG5lsJ+EltG2MkxWWpJSaGr6gqdOnULv3r1x5MgRdOrUCXPmzCn3NWq17RvHsZGPN1artUKvsR/vatq0aW7T2idPnoynnnqqzPMVFxejsLAQ+fn5KC4uLne8SpKfn1+l19vvt6CgoMrn8sZqtUIIcdnOT87Ke85msxmFhYU4f/48NJoafzuhWnD+/PnaHgIpDGOCXDEmyBVjglwxJsiT+hoXGzduBADcdNNNNXbN3IvFKNKqkJXlnJ00AAjWWJB9XgUU1X4znvoaE/VBaGiobweKajBs2DABQHz44YdlHrd//34RGxsrAIjExESRlZXl0/n37dsnAIh27dp5PSY0NFQAEOnp6UIIITp06CAAiL1793o8fuLEiQKAmD59usf9BQUFIjs72+mjoKCg3LEWFRWJtLQ0UVRUVO6xSpOXl1fp19pjwP4RGxsr7zt16pR46KGHRGRkpNDpdKJ169birbfeEhaLxekcq1atEtddd50ICAgQ/v7+omvXrmLlypXyfnvs2D+GDRtW5nhiY2PFwoULRXh4uAgICBDvv/++EEKICxcuiMcff1w0btxY6HQ6ERcXJ5577jm3r+/27dtFcnKyCA0NFUajUXTs2FF89NFHTsfExsaKQYMGiSVLloiEhARhMBhE+/btxRdffOE2pr///lvcc889IiwsTOj1etG2bVsxa9YsYbVa5WM2b94sAIjvvvtOTJw4UURFRQm9Xi8SExPFunXrnM538uRJcc8994jo6Gih0+lEfHy8eOaZZ9y+jocOHRJ33323CA0NFQaDQXTo0EEsXrzY67NzVF5M1OV4p8rJzMys7SGQwjAmyBVjglwxJsgVY4I8qa9xMWfOHDFnzpwaveavx8wi65LF6/5ii9XrvppUX2OCStVYadSGDRtw1113IScnB3369MHXX38Nf39/n14bExMDwNbkxRN7pZdOp5OztTExMdi7d6/X15w+fRpA2etk6vV6n8ZHwMiRI5Geno41a9bg7bffRtOmTQHYvmadO3dGfn4+xowZg6ioKGzatAkTJ07E/v378fHHHwMAfvrpJ9x55524/vrr8cYbb0AIgQ8//BADBw7Ehg0bcNNNN+Gdd97BxIkTcfHiRbz99tvlLlZ89uxZPPnkk5g8eTJyc3ORnJyM3NxcJCUl4ejRoxg1ahTi4+OxZ88evP766/jll1/w/fffQ61W48iRI7jlllsQGxuLF154AQaDAUuXLsVDDz0EAHjwwQfl6+zYsQPLly/HmDFjEBcXh4ULF2LQoEHIy8vD8OHDAQC///47evToAY1GgzFjxiAyMhKrV6/G+PHjsXfvXixcuNBp7GPGjEFwcDAmT56MgoICTJ8+HbfeeitSU1MRExOD4uJi9O3bF6dOncL48eMRFRWFnTt3Ytq0aTh58iQWL14MwNaQqmvXrggKCsKkSZMQEBCA1atXY+jQoUhNTcULL7xQHV9+IiIiIiIiqiO0agl+eu+VlGpV7VdZEgGomUrLJUuWCI1GIwCIhx56SJjN5gpfo2HDhgKAyMjIcNv322+/CQCiQ4cO8rann35aABAzZ870eL5rrrlGABD79u2r8FjKUmOVZ+aLQqR+IsRf/2f703yxyqesSqWlEEJMmjRJABCpqanytgceeED4+/uLY8eOOR373HPPCQDixx9/FEIIMWbMGKdKWSGEyMjIEAkJCWLGjBnytmuvvdapitMbe0zOnz/faftLL70kVCqV2LFjh9P2BQsWCADi008/FUII8eabbwoAYvfu3fIxhYWFomPHjmLixInyNnv1p/11QgiRm5srmjVrJiIiIuQ46Nq1q9DpdOLQoUPycVarVQwZMkQAEOvXrxdClFZatmnTRhQWFsrHLl26VAAQb731lhBCiF27dnmsFH7ggQdEjx495CrWnj17ipiYGLeq5vvvv1+o1Wqnr5UnrLQkV/xtJ7liTJArxgS5YkyQK8YEeVJf46I2Ki2PnC6u0etVVn2NCSp12ZdX/eabbzB06FAUFxdj6tSpWLBgQaXWvuvbty8AYNWqVW77VqxYAQD43//+53b8ypUr3Y4/duwY9u/fj8aNG+Pqq6+u8FhqXfEl4LexwD+fAWc32v78baxtu4JYrVYsX74cXbp0QWBgIDIyMuSPgQMHAgBWr14NAGjUqBEAYNy4cfj1118hhEBYWBgOHz6MSZMmVXoMycnJTp9//fXXSEhIQEJCgtN4+vbtC61W6zaep59+Gj/++CMsFgt0Oh1+/fVXzJw50+mcTZs2xZAhQ+TPTSYTRo8ejfT0dOzYsQPnzp3D9u3bcc8996BFixbycZIk4cUXXwQALF++3OmcAwcOhE6nkz/v0KEDgNJq4+joaKhUKsydOxfLli2TG099/PHH2Lp1K1QqFTIzM7Flyxb06dMHFovF6X7tjbDWrFlT6WdLREREREREdU+QiZWUVDdc1qTl2bNnMXz4cFgsFjz33HNygqYseXl5+Pvvv/H33387bR8zZgxUKhWmTJmCQ4cOydt37NiBt956CwaDAY8++qi8vXv37mjfvj1+/PFHvPvuu/L2nJwcPPjggxBC4IknnvCpuY/inFwO5J8GYAVgsf2Zf9q2XUEyMjKQnZ2NDRs2ICIiwunj2muvBQD8888/AIBHH30UN954I7744gt06tQJ0dHReOCBB7Bp06YqjaFBgwZOnx89ehSHDh1yG09MTAzMZrM8nrvvvhspKSnYuHEjevTogYiICNx7771YuXIlhBBO52zTpo1bHNmTk8ePH0dqaioAoFWrVm7ja968OXQ6nXyMt3Hblyqwd06PiYnBzJkzcfLkSdx1110ICwtD7969MXfuXOTl5QGwJeeFEPj444/d7nfAgAEASp8/ERERERER1Q9MWlJdcVnXtHzrrbeQlZUFjUaDY8eO4b777vN4XNeuXTF69GgAwK5du9CzZ08AcEoOderUCVOmTMErr7yC9u3b46abbkJhYSE2b94Mq9WKzz77DJGRkfLxkiRh4cKFSEpKwvjx4/HJJ58gLi4OP/74I86dO4d+/fphzJgxl/HuL6OCMwBc32Skku3KYU+w9evXDxMmTPB4jD055+/vj40bN2LPnj1YtWoV1q1bh8WLF2PRokWYMmUKXn311UqNwd5F3nFMnTp1wrRp0zweHxAQAADQaDT4/PPP8fzzz2PFihVYt24dli9fji+++AJDhgzBZ599Jr9Gq9W6ncd+7xqNRo5j12SnfZsQwm39VJWq/N8nTJgwASkpKVi5ciXWrl2LTZs24YcffsDs2bOxe/dueQwPPfQQ7r33Xo/naNKkSbnXISIiIiIiossjMTGxxq+p0zBpSXXDZU1afv/99wCA4uJiLF26tMxj7UnLsrz88sto1aoVZs2ahc2bN8NkMqFnz56YMmWK2zRgAGjfvj12796NF154ARs3bsRff/2F5s2b48knn8Sjjz5aqWnqimBoCFsDbUeiZLtyREREwM/PD/n5+ejVq5fTvpycHKxbtw5xcXEAgNTUVJw6dQpdu3ZFp06d8Morr+C///7DTTfdhJkzZ+Lll1/2KZFXnqZNm+L8+fNu47FYLPjqq6/QsKHtGZ46dQp///03brzxRrRu3RrPPvssMjMzMWDAACxZsgTTp0+XmzgdO3bM7TpHjhwBACQkJMj36Fo9bH+t2WxG48aNK3Qf2dnZ2LdvH6655hqMHDkSI0eORFFREZ566inMmjUL33zzjfw9IYRwu9+0tDTs2rULfn5+FbouERERERERVZ/OnTvX9hCIFKtapocvWrQIQgg8/PDDTtv/+OMPuZKsrA/HqrXk5GR5uyeDBw/GL7/8gtzcXKSnp2PDhg0eE5Z2LVq0wP/7f/8P6enpyMvLw/79+/HEE0/U7c7gje4AjFGwffnUtj+NUbbttche1ehYZdivXz9s3rwZ27dvdzr2jTfewD333INffvkFAPDss8+iV69ecld3AGjcuDFiYmKgVqvl6ddqtVo+f2UMGDAAx44dw+eff+60/aOPPkJKSoq8puXs2bNx00034ffff5ePCQsLQ3x8vNO9AsCBAwewceNG+fNLly5h7ty5aNKkCTp37ozIyEhcd911+Oqrr3D48GGn69orSPv371+h+/j555+RlJSERYsWydt0Op289qVarUZUVBQ6d+6MpUuXuiVWn3zySdxxxx2cHk5EREREREREilRHSw3rOY0/0PE92xqWBWdsFZaN7rBtr0X26flvvPEGbrzxRqSkpOD//u//sHnzZvTu3RujR49Gy5YtsX37dixevBjJycm4++67AQATJ07E8uXL0aNHD4wYMQLBwcHYtGkTtmzZgsmTJ8tJy8jISOzZswfTp0/HtddeixtvvLFCY3zmmWewYsUKDB06FFu3bsW1116L/fv344MPPsBVV12FsWPHAgBGjhyJDz74AH379sWYMWMQFRWFX3/9FYsXL8agQYOc1pzU6/Xo378/xo8fj4iICHz00UdIS0vDihUr5OrQ2bNnIzk5Gddffz3Gjh2LyMhIfPPNN1i/fj0GDx6M3r17V+g+brrpJiQmJuLpp59Gamoqrr76avz333+YPXs2mjVrhltvvRUAMGfOHCQnJyMxMRFjx45Fo0aNsGbNGqxevRpDhgzBDTfcUKHrEhERERERUfWxz0pNSUmp5ZEQKVBNtiqvD4qKikRaWpooKiqq7aFUWF5eXpVen5GRIXr16iX0er0ICwsTBQUFQgghTpw4IYYNGyYiIyOFXq8X8fHxYsqUKeLixYtOr9+6davo1auXiIiIEHq9XrRt21a88847wmKxyMfs2rVLtGnTRmi1WtG3b1+vYxk2bJgA4HYN+zjHjRsnGjduLHQ6nWjatKkYO3asOHv2rNNx+/btEwMGDBBRUVFCp9OJ+Ph48eKLL4r8/Hz5mNjYWHHttdeKTz/9VMTGxgqTySS6d+8uNm3a5Hbd/fv3i4EDB4qQkBBhMBhEu3btxJw5c4TVapWP2bx5swAgZs+e7fTa1NRUAUBMmjRJ3nbu3DkxduxY0bRpU6HX60XDhg3Fgw8+KNLS0pxe+8cff4g77rhDhIaGCoPBINq0aSOmT5/uU4yWFxN1Od6pcjIzM2t7CKQwjAlyxZggV4wJcsWYIE/qa1zMmTNHzJkzp7aHoUj1NSaolCSEl3nYVClmsxnp6emIiIjw2KBFyfLz82E0Gmt7GHVK06ZNER4ejj179tT2UC6L8mKiLsc7VU5WVhZCQ0NrexikIIwJcsWYIFeMCXLFmCBP6mtcvPfeewAgz/qjUvU1JqhUtaxpSURERERERERERFRdmLQkIiIiIiIiIiIiRWHSkoiIiIiIiIiIiBSF3cOJquDEiRO1PQQiIiIiIiKqo/r06VPbQyBSLCYtiYiIiIiIiIhqQXx8fG0PgUixOD2ciIiIiIiIiIiIFIVJSyIiIiIiIiKiWvDee+/hvffeq+1hECkSk5ZERERERERERESkKExaXiZCiNoeAtFlxzgnIiIiIiIiosuBSctqplLZHqnVaq3lkRBdfhaLBUBp3BMRERERERERVQdmGqqZSqWCSqVCfn5+bQ+F6LISQiAvLw9arRZqtbq2h0NEREREREREVxBNbQ/gSiNJEgIDA3HhwgVoNBro9XpIklTbw/KJ2WyGRsOQoFKeYkIIAYvFgry8PBQWFiIkJKSWRkdEREREREREVypmqC4Do9GIoqIiXLp0CRcvXqzt4fissLAQer2+todBClJWTGi1WoSEhMBoNNbwqIiIiIiIiK4MKSkptT0EIsVi0vIykCQJwcHBCAwMlNf8qwvOnz/Pqjly4i0mVCoVp4QTERERERFVUWhoaG0PgUixmLS8jOzrW9YVGo0GWq22todBCsKYICIiIiIiunyysrIAMHlJ5EndyagREREREREREV1Bli5diqVLl9b2MIgUiUlLIiIiIiIiIiIiUhQmLYmIiIiIiIiIiEhRmLQkIiIiIiIiIiIiRWHSkoiIiIiIiIiIiBSFSUsiIiIiIiIiIiJSFCYtCQBQWFiId999F4WFhbU9FFIIxgS5YkyQK8YEuWJMkCvGBLliTJAn9Tkuxo4di7Fjx9b2MBSnPscElZKEEKK2B0G1LycnB0FBQcjOzkZgYGBtD4cUgDFBrhgT5IoxQa4YE+SKMUGuGBPkCeOCXDEmCGClJRERERERERERESkMk5ZERERERERERESkKExaEhERERERERERkaIwaUkAAL1ejxdffBF6vb62h0IKwZggV4wJcsWYIFeMCXLFmCBXjAnyhHFBrhgTBLARDxERERERERERESkMKy2JiIiIiIiIiIhIUZi0JCIiIiIiIiIiIkVh0pKIiIiIiIiIiIgUhUnLOshqtWL+/Pno0qULAgMDYTAY0LJlS0yePBkXLlxwO/748eMYOnQoYmNjYTQa0bp1a0ybNg1ms9mn6z300EOQJAnFxcVej/n9998xcOBAREdHw2QyoUOHDpg3bx64ZGrNUWJcAIAQAr169UKjRo0qc1tUSUqMh6+++go33ngjQkJCoNPpEBcXhzFjxiAtLa2yt0kVoMSYWLZsGXr06IGAgAD4+/ujU6dOePfdd2GxWCp7m1QBSowJR0II3HLLLZAkCT/88ENFbo0qSWkxsXfvXkiS5PXj+uuvr8rtkg+UFhMAkJWVhSeffBIJCQkwGAwICwvDgAEDsHfv3sreJlWQkuKiadOmZb5P2D8WLVpUxbumsigpJuw2bNiA3r17Izg4GHq9HgkJCZg0aRLOnz9f2duk2iCoTrFYLGLgwIECgDCZTCI5OVn069dPRERECAAiPj5enDlzRj7+wIEDIiQkRAAQ1113nbjjjjvkY2+66SZhNpvLvN6sWbMEAAHA67GbNm0Ser1eqFQqkZSUJPr37y8CAgIEADFs2LDqvH3yQolxYTdx4kQBQMTExFTLvVL5lBgP48ePFwCEVqsVXbt2Fbfffrto1KiRACAiIiLEwYMHq/UZkDMlxsT06dMFAKHRaETPnj3FrbfeKkJDQwUA0bt3b1FUVFStz4CcKTEmXL377rvyazZs2FCl+6XyKTEmFixYIACITp06iSFDhrh9vPjii9X5CMiFEmPixIkTomnTpgKAaNq0qRg4cKC46qqrBABhMBjEnj17qvUZkDulxcWECRM8vj8MGTJE9OrVS/7/5+7du6v9WZCN0mJCCCHef/99IUmSkCRJdOvWTfTv3180bNhQABDNmzcXZ8+erdZnQJcPk5Z1jP0/by1bthSpqany9pycHHHbbbcJAOLuu++Wt3fs2FEAEPPnz5e3ZWdnix49eggA4q233vJ4neLiYjF58mT5zcDbG0JBQYGIiooSarVarFmzRt5+6tQp0apVKwFALF++vBrunMqitLgQQojc3FwxbNgw+TgmLWuO0uLhhx9+kJOTv//+u7y9sLBQjBw5UgAQiYmJ1XDn5I3SYuLAgQNCpVKJwMBAsW/fPnl7VlaWfO25c+dWw52TN0qLCVcHDx4URqORScsapMSYGDt2rAAgvv322+q5SaoQJcZEz549BQAxduxYUVxcLG9//fXXBQDRrl27qt00lUuJceGJxWIRycnJAoB4//33K36j5DOlxUR6erowGo1Cr9eLzZs3y9tzc3PFLbfcIgCI0aNHV/3GqUYwaVnHdO3a1et/3tLT04UkSUKr1Yq8vDyxceNGAUB06dLF7dijR48KSZJEkyZNhNVqddq3bds20alTJwFANGvWrMw3hI8++kgAEPfee6/bvk2bNgkAonv37lW4Y/KF0uJi5cqVIiEhwelYJi1rjtLiYciQIQKAmDNnjtu+wsJCubru6NGjVbhrKovSYuLZZ58VADxWSX3xxRcCgLjtttsqf8NULqXFhKOioiLRsWNHER4eLtq0acOkZQ1RYkx06dJFABCnT5+unpukClFaTPz000/yLzpdzyOEEO3btxfNmzcX6enpVbhrKo/S4sKb1157TQAQt99+e8VvkipEaTGxbNkyAUD07dvXbd+OHTsEANGmTZsq3DHVJK5pWceEhISgVatWHtfwCQ8PR0hICMxmMzIyMvDdd98BAPr37+92bPPmzXHNNdfg33//xf79+5323XbbbdizZw+GDBmC3bt3lzke+zUGDBjgti8pKQkhISH46aefPK5jQdVHSXFx4cIFDBgwAMePH8f48ePx7bffVvHuqKKUFA8AYDKZcNVVV6Fbt25u+3Q6HZo2bQoAOHXqlK+3SBWktJh4+eWXcfjwYTz66KNu+y5dugQA0Gg0Pt8fVZzSYsLRiy++iN9++w3z5s1DREREBe+MKktpMWG1WvHHH38gOjoaDRs2rMKdUWUpLSa+/vprAMCECRMgSZLb/r179+Lo0aMIDw/3+R6p4pQWF578999/ePXVV+Hv74/333+/wq+nilFaTKhUtjRXWlqaW4+N9PR0AEBoaKjvN0i1q7azplR9jh49KgAInU4n8vPzRZ8+fcqcUpOSkiIAiM8++8xp++DBg8XOnTvlz1HGbzFatmwpAIgDBw54vIb9N+Q//fRTFe6MqqKm4yI7O1sMHjxY/Pnnn0IIIVJTU1lpqSC18T5RlpycHHkK6PHjxyt+Q1RlSoqJv/76S16rbOXKlZW7Iaqy2oyJH3/8UahUKnHfffcJIYRISkpipaUC1EZMHDx4UAAQPXr0EK+++qpo27atMBqNIjo6WjzyyCPi5MmT1XuTVCG1ERPdu3cXAMSRI0dEVlaWmDt3rhgxYoR47LHHxDfffFO9N0iVopT/U9x7770CgHj99dcrfzNULWojJjIyMoSfn58AIB555BFx4sQJcenSJbF27VrRuHFjIUkSl7CrQ1jGcAV59tlnAQC33norDAaDXLUUFRXl8Xj79rNnzzptX7Jkic/XrOw1qObUdFwEBgZWKIaoZtXG+0RZXn31VeTn56Njx46Ii4urlnNSxSghJiZMmIBffvkFv/zyC0wmE959912Pv4GnmlFbMXHx4kUMHToU0dHRmD17dkWHTZdRbcTEb7/9BgDYtm0bfvnlFyQlJaFRo0bYs2cPPvzwQ6xevRqbNm1CmzZtKnw/VHW1ERNHjx4FYOs63K1bN6dzvfvuu+jTpw++/vpr+Pv7+34jVK2U8H+Kv/76C1988QXCwsLw2GOPVfo8VD1qIybCwsLw5ZdfYujQofjwww/x4YcfyvtiYmKwbt069O7du0L3QbWH08OvEG+//Ta+/PJLmEwmvPbaawCA3NxcALapmZ4YjUYApVPxKqMmrkGVV1txQcqktHhYtmwZZsyYAZVKhRkzZlT7+al8SomJjz/+GD///DOEEFCpVDh8+DDy8vKq7fzku9qMiUcffRQnTpzAwoULERwcXKVzUfWprZjYu3cvACAxMRHHjx/HunXr8P333+PEiRNISUnB2bNnkZKS4jb1jy6/2oqJ7OxsAMCgQYPQunVr7NmzBzk5OdiyZQvatGmDdevWYcSIEZU+P1WNUv5P8c4770AIgcceewx+fn7Vdl6quNqMifbt2+POO++ERqNBly5d0K9fPzRo0ABpaWmYPn06srKyqnR+qjlMWl4B3nnnHUycOBGSJOGjjz5Cq1atAABqtRoAPK754shqtVb62jVxDaqc2owLUh6lxcNXX32FlJQUWK1WTJs2DT179qzW81P5lBITQggcPHgQubm5+PHHH9GiRQvMmTOHlZa1oDZjYtmyZfjkk08wduxY9OrVq9LnoepVmzExbdo0HDlyBOvWrUN0dLS83c/PDwsWLEBMTAz++OMPbNu2rdLXoIqrzZgoLCwEADRo0ABr167Ftddei4CAACQlJWHdunUwmUxYunQp/v7770pfgypHKf+nuHDhAj799FMYjUaMGzeuWs5JlVObMXH8+HF07twZK1euxI4dO7Bjxw58++23SE1Nxf33348NGzZg4MCBlT4/1SwmLeswIQSeeuopPP7441Cr1Vi4cCHuvfdeeb99akR+fr7H19u3V2UKRU1cgypGCXFByqHEeJg1axbuvfdemM1mvPLKK3jqqaeq7dxUPqXFhCRJaNSoEUwmE7p164YNGzagYcOG+OGHH7Bly5ZquQaVrbZj4vTp0xg5ciRatGiBN998s1LnoOpV2zEBAFqtFvHx8QgJCXHbZzKZcOONNwIA9uzZU+lrkO+UEBP2yqxHHnkEer3eaV+jRo1w6623AgA2b95c6WtQxSghLhx9++23yM/Px2233cZGK7VECTHx3HPPIS0tDW+++SYSExPl7SaTCfPnz0dcXBy2bduGH3/8sdLXoJrDpGUdlZ+fj7vuugvTp0+H0WjEsmXLMGzYMKdjYmJiAABnzpzxeI7Tp08D8L6ehC9q4hrkO6XEBSmD0uLBYrHg0Ucflbt+vv/++3juueeqfF7yndJiwpOQkBD5B0/7mnZ0+SghJl577TVkZmYiKCgIjzzyCO677z7546+//gIAvPHGG7jvvvv4A0YNUEJM+MLeUZxLSVx+SomJiIgIAPC6BnbTpk0BABkZGZW+BvlOKXHhaOXKlQDglCSjmqOUmNi0aRMAoE+fPm77DAaD/Esv+zIkpGxMWtZBOTk5uOmmm7B8+XJERERg8+bNHqfRtW3bFgBw8OBBj+f5888/AQBXX311pcdS1jWsViv+/vtvSJKEq666qtLXIN8oKS6o9iktHsxmM+666y7MmTMHfn5+WLFiBUaNGlWlc1LFKCkmZs6ciXvvvRepqake99sraMxmc6WvQeVTSkzY163avXs3lixZ4vRx7tw5AMDGjRuxZMkSHDt2rFLXIN8oJSYAYOLEibjjjjvwzz//eNx//PhxALYKO7p8lBQT9tempaV53G9PgjRo0KDS1yDfKCku7KxWK9avXw+j0Yj//e9/VT4fVYySYuLChQsAAI3Gc99p+/aioqJKX4NqUK30LKdKKyoqEt26dRMARPPmzcXRo0e9Hrt161YBQHTv3t1t39GjR4UkSaJx48bCarWWeU0AAoAwm81u+z755BMBQNx///1u+zZu3CgAiK5du/pwZ1QVSosLR6mpqQKAiImJ8e1mqMqUGA+DBg0SAERERITYvXt3xW6IqkxpMdG/f38BQLz66qsex9qqVSsBQGzYsMGHu6PKUFpMeJOUlMRYqCFKi4kbbrhBABBvv/22277Tp0+LgIAAoVarxb///lv+zVGlKC0mPvjgAwFAdOnSxW1ffn6+aNy4sQAgDh486MPdUWUpLS7s9u/fLwCIG264wfeboWqhtJho3769ACA++eQTt31ms1m0bNmS/7eoQ5i0rGOeffZZAUA0bNhQnDx5ssxjrVar/A07a9YseXt2drbo0aOH23ZvynpDuHjxooiOjhZqtVosW7ZM3n769GnRunVrAUCsWrWqAndIlaG0uHDEpGXNU1o8zJ8/XwAQJpNJ7Nu3r+I3RFWmtJj4/vvv5ZjYuXOnvD0vL0889NBDAoDo2LGjsFgsFbhLqgilxYQ3TFrWHKXFxOLFiwUAERQUJH777Td5e05OjrjlllsEAPHwww9X4A6popQWEzk5OSImJkYAEC+88IKc1DCbzWLEiBECgLj55psreJdUUUqLC7uFCxcKAGLcuHG+3wxVC6XFxLx58wQAERkZKX7//Xd5e2FhoRg9erQAINq2bcv/Z9YRkhBCeCnCJIXJzMxEkyZNkJeXh3bt2sml1Z7MnDkTkZGR+P3335GUlIScnBx07NgRcXFx+PHHH3Hu3Dn069cPK1eu9Fo2bWfv7GU2mz0eu3btWtx+++0oLi5G165dER4ejk2bNiEnJwcjR47EvHnzqnbjVCalxoXdiRMnEBcXh5iYGJw8ebJyN0k+U1o8FBcXIzY2FqdOnUJ8fDyuu+46r+d45plnuJTEZaC0mLB78sknMWPGDKhUKnTt2hVBQUHYs2cPzpw5g7i4OGzatElen4yql1JjwpPk5GRs3boVGzZsYFfxy0iJMSGEwPDhw7F48WJoNBp07doVoaGh2LZtGzIzM9GtWzesXbsWfn5+VX8A5EaJMQEA27dvx//+9z/k5OQgPj4ebdu2xe+//44TJ04gNjYWW7duRWxsbNVunrxSalwAwIsvvoiXX34Zr7zyCtdMr0FKjAkhBIYNG4ZPP/0UKpUK3bp1Q3BwMH799VekpaUhKioKmzZtkjuak8LVbs6UKmLZsmXybxTK+zhy5Ij8ukOHDolBgwaJ8PBwYTQaRdu2bcX06dNFQUGBT9eFD7/Z2rVrl+jXr58IDg4W/v7+omPHjmLBggX87UUNUHJcCMFKy5qmtHj49ddffR4PK6kuD6XFhKPly5eL5ORkERAQIPR6vWjVqpV49tlnxfnz56t621QGJceEK1Za1gwlx8SiRYtEly5dhJ+fnzAajaJdu3ZixowZoqioqMr3Td4pOSZSU1PFww8/LGJiYoROpxNNmzYV48ePF+fOnavyfVPZlBwX9gq62bNnV/k+yXdKjoklS5aIpKQkERgYKHQ6nWjevLkYP368OHPmTJXvm2oOKy2JiIiIiIiIiIhIUdg9nIiIiIiIiIiIiBSFSUsiIiIiIiIiIiJSFCYtiYiIiIiIiIiISFGYtCQiIiIiIiIiIiJFYdKSiIiIiIiIiIiIFIVJSyIiIiIiIiIiIlIUJi2JiIiIiIiIiIhIUZi0JCIiIiIiIiIiIkVh0pKIiIiIiIiIiIgUhUlLIiIiIiIiIiIiUhQmLYmIiIiIiIiIiEhRmLQkIiIiIiIiIiIiRWHSkoiIiIiIiIiIiBTl/wPjxxnSoRfMIAAAAABJRU5ErkJggg==\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -688,7 +757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "id": "based-bearing",
    "metadata": {
     "ExecuteTime": {
@@ -699,9 +768,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 1000x600 with 2 Axes>"
+       "<Figure size 800x800 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -712,12 +781,14 @@
     "# specify to use orbit style\n",
     "\n",
     "_ = eda_plot.ts_heatmap(df = df, date_col = 'week', value_col='claims', \n",
-    "                        palette = palette.OrbitColorMap.BLUE_GRADIENT.value, use_orbit_style=True)"
+    "                        palette = palette.OrbitColorMap.BLUE_GRADIENT.value, \n",
+    "                        seasonal_interval=52,\n",
+    "                        use_orbit_style=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "id": "db8c7570-a1d7-4d14-8487-12897ae97dcd",
    "metadata": {
     "ExecuteTime": {
@@ -728,7 +799,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2500x600 with 2 Axes>"
       ]
@@ -744,7 +815,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 19,
    "id": "other-heavy",
    "metadata": {
     "ExecuteTime": {
@@ -755,14 +826,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 1800x432 with 2 Axes>"
+       "<Figure size 2500x600 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -771,21 +840,13 @@
     "_ = eda_plot.dual_axis_ts_plot(df=df, var1='trend.unemploy', var2='claims', date_col='week', color1='red', color2='green',\n",
     "                               use_orbit_style=False)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "df2b5f5d-13ea-4830-9d9e-563142a035da",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "orbit39",
    "language": "python",
-   "name": "python3"
+   "name": "orbit39"
   },
   "language_info": {
    "codemirror_mode": {
@@ -797,7 +858,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.9.18"
   },
   "toc": {
    "base_numbering": 1,
@@ -815,4 +876,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/examples/ets_example.ipynb b/examples/ets_example.ipynb
deleted file mode 100644
index 3f3f827b..00000000
--- a/examples/ets_example.ipynb
+++ /dev/null
@@ -1,384 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:29.534059Z",
-     "start_time": "2021-07-13T22:35:26.280935Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import orbit\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from orbit.utils.dataset import load_iclaims\n",
-    "from orbit.diagnostics.plot import plot_predicted_data, plot_predicted_components\n",
-    "from orbit.utils.plot import get_orbit_style\n",
-    "plt.style.use(get_orbit_style())\n",
-    "from orbit.models import ETS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:29.542920Z",
-     "start_time": "2021-07-13T22:35:29.536694Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'1.1.3'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "orbit.__version__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:29.597204Z",
-     "start_time": "2021-07-13T22:35:29.548718Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:29.927306Z",
-     "start_time": "2021-07-13T22:35:29.600727Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "week              datetime64[ns]\n",
-       "claims                   float64\n",
-       "trend.unemploy           float64\n",
-       "trend.filling            float64\n",
-       "trend.job                float64\n",
-       "sp500                    float64\n",
-       "vix                      float64\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# can also consider transform=False\n",
-    "raw_df = load_iclaims(transform=True)\n",
-    "raw_df.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:29.978276Z",
-     "start_time": "2021-07-13T22:35:29.929541Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df = raw_df.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:30.046508Z",
-     "start_time": "2021-07-13T22:35:29.981463Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "      <th>sp500</th>\n",
-       "      <th>vix</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.318452</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.417633</td>\n",
-       "      <td>0.122654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>13.624218</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.168794</td>\n",
-       "      <td>-0.425480</td>\n",
-       "      <td>0.110445</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>13.398741</td>\n",
-       "      <td>0.236143</td>\n",
-       "      <td>-0.292477</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.465229</td>\n",
-       "      <td>0.532339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>13.137549</td>\n",
-       "      <td>0.203353</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.106918</td>\n",
-       "      <td>-0.481751</td>\n",
-       "      <td>0.428645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>13.196760</td>\n",
-       "      <td>0.134360</td>\n",
-       "      <td>-0.242466</td>\n",
-       "      <td>0.074483</td>\n",
-       "      <td>-0.488929</td>\n",
-       "      <td>0.487404</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week     claims  trend.unemploy  trend.filling  trend.job     sp500  \\\n",
-       "0 2010-01-03  13.386595        0.219882      -0.318452   0.117500 -0.417633   \n",
-       "1 2010-01-10  13.624218        0.219882      -0.194838   0.168794 -0.425480   \n",
-       "2 2010-01-17  13.398741        0.236143      -0.292477   0.117500 -0.465229   \n",
-       "3 2010-01-24  13.137549        0.203353      -0.194838   0.106918 -0.481751   \n",
-       "4 2010-01-31  13.196760        0.134360      -0.242466   0.074483 -0.488929   \n",
-       "\n",
-       "        vix  \n",
-       "0  0.122654  \n",
-       "1  0.110445  \n",
-       "2  0.532339  \n",
-       "3  0.428645  \n",
-       "4  0.487404  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:30.095362Z",
-     "start_time": "2021-07-13T22:35:30.049407Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "test_size=52\n",
-    "\n",
-    "train_df=df[:-test_size]\n",
-    "test_df=df[-test_size:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:30.144133Z",
-     "start_time": "2021-07-13T22:35:30.100924Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ets = ETS(\n",
-    "    response_col='claims',\n",
-    "    date_col='week',\n",
-    "    seasonality=52,\n",
-    "    seed=2020,\n",
-    "    estimator='stan-mcmc',\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:36:44.267632Z",
-     "start_time": "2021-07-13T22:35:30.147321Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
-     ]
-    }
-   ],
-   "source": [
-    "ets.fit(train_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:36:45.155898Z",
-     "start_time": "2021-07-13T22:36:44.271106Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "predicted_df = ets.predict(df=df, decompose=True)\n",
-    "predicted_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:36:45.893424Z",
-     "start_time": "2021-07-13T22:36:45.159519Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "_ = plot_predicted_data(training_actual_df=train_df,\n",
-    "                        predicted_df=predicted_df,\n",
-    "                        date_col='week',\n",
-    "                        actual_col='claims',\n",
-    "                        test_actual_df=test_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:36:46.738346Z",
-     "start_time": "2021-07-13T22:36:45.896387Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "_ = plot_predicted_components(predicted_df=predicted_df, date_col='week')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "orbit38",
-   "language": "python",
-   "name": "orbit38"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.12"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/ets_lgt_dlt_missing_response.ipynb b/examples/ets_lgt_dlt_missing_response.ipynb
deleted file mode 100644
index da70adde..00000000
--- a/examples/ets_lgt_dlt_missing_response.ipynb
+++ /dev/null
@@ -1,916 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Handling Missing Response in Exponential Smoothing Models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Because of the generative nature of the exponential smoothing models, they can naturally deal with missing response during the training process. It simply replaces observations by prediction during the 1-step ahead generating process. Below users can find the simple examples of how those exponential smoothing models handle missing responses."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-12-16T20:09:30.288992Z",
-     "start_time": "2021-12-16T20:09:28.292123Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import orbit\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from orbit.utils.dataset import load_iclaims\n",
-    "from orbit.diagnostics.plot import plot_predicted_data, plot_predicted_components\n",
-    "from orbit.utils.plot import get_orbit_style\n",
-    "from orbit.models import ETS, LGT, DLT\n",
-    "from orbit.diagnostics.metrics import smape\n",
-    "\n",
-    "plt.style.use(get_orbit_style())\n",
-    "\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-12-16T20:09:30.867640Z",
-     "start_time": "2021-12-16T20:09:30.834231Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'1.1.4dev'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "orbit.__version__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-12-16T20:09:32.891272Z",
-     "start_time": "2021-12-16T20:09:32.436317Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "      <th>sp500</th>\n",
-       "      <th>vix</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.318452</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.417633</td>\n",
-       "      <td>0.122654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>13.624218</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.168794</td>\n",
-       "      <td>-0.425480</td>\n",
-       "      <td>0.110445</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>13.398741</td>\n",
-       "      <td>0.236143</td>\n",
-       "      <td>-0.292477</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.465229</td>\n",
-       "      <td>0.532339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>13.137549</td>\n",
-       "      <td>0.203353</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.106918</td>\n",
-       "      <td>-0.481751</td>\n",
-       "      <td>0.428645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>13.196760</td>\n",
-       "      <td>0.134360</td>\n",
-       "      <td>-0.242466</td>\n",
-       "      <td>0.074483</td>\n",
-       "      <td>-0.488929</td>\n",
-       "      <td>0.487404</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week     claims  trend.unemploy  trend.filling  trend.job     sp500  \\\n",
-       "0 2010-01-03  13.386595        0.219882      -0.318452   0.117500 -0.417633   \n",
-       "1 2010-01-10  13.624218        0.219882      -0.194838   0.168794 -0.425480   \n",
-       "2 2010-01-17  13.398741        0.236143      -0.292477   0.117500 -0.465229   \n",
-       "3 2010-01-24  13.137549        0.203353      -0.194838   0.106918 -0.481751   \n",
-       "4 2010-01-31  13.196760        0.134360      -0.242466   0.074483 -0.488929   \n",
-       "\n",
-       "        vix  \n",
-       "0  0.122654  \n",
-       "1  0.110445  \n",
-       "2  0.532339  \n",
-       "3  0.428645  \n",
-       "4  0.487404  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# can also consider transform=False\n",
-    "raw_df = load_iclaims(transform=True)\n",
-    "raw_df.dtypes\n",
-    "\n",
-    "df = raw_df.copy()\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-12-16T20:09:34.223732Z",
-     "start_time": "2021-12-16T20:09:34.191302Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "test_size=52\n",
-    "\n",
-    "train_df=df[:-test_size]\n",
-    "test_df=df[-test_size:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Define Missing Data\n",
-    "Now, we manually created a dataset with a few missing values in the response variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-12-16T20:09:54.254440Z",
-     "start_time": "2021-12-16T20:09:54.218923Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,\n",
-       "       70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,\n",
-       "       87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "na_idx = np.arange(53, 100, 1)\n",
-    "na_idx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-12-16T20:09:54.689400Z",
-     "start_time": "2021-12-16T20:09:54.658226Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "train_df_na = train_df.copy()\n",
-    "train_df_na.iloc[na_idx, 1] = np.nan"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exponential Smoothing Examples"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ETS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-12-16T20:09:58.040978Z",
-     "start_time": "2021-12-16T20:09:56.665111Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "19:57:18 - cmdstanpy - INFO - Requested 8 parallel_chains but only 4 required, will run all chains in parallel.\n",
-      "19:57:18 - cmdstanpy - INFO - CmdStan start processing\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "093cf1d35014422eb02029ccd6cdb4e6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 1 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "be1c194c256a4b4e94d5f47631b7ae09",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 2 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3e1a44ced4284bdb884b19374c1e8cf4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 3 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fef741db3b1f48039aa3bb5517d5622d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 4 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                                                                                                                                                                                                "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "19:57:19 - cmdstanpy - INFO - CmdStan done processing.\n",
-      "19:57:19 - cmdstanpy - WARNING - Non-fatal error during sampling:\n",
-      "Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/Users/towinazure/edwinnglabs/orbit/orbit/stan/ets.stan', line 108, column 8 to column 69)\n",
-      "Consider re-running with show_console=True if the above output is unclear!\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "ets = ETS(\n",
-    "    response_col='claims',\n",
-    "    date_col='week',\n",
-    "    seasonality=52,\n",
-    "    seed=2022,\n",
-    "    estimator='stan-mcmc'\n",
-    ")\n",
-    "ets.fit(train_df_na)\n",
-    "ets_predicted = ets.predict(df=train_df_na)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### LGT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-12-16T20:10:05.812782Z",
-     "start_time": "2021-12-16T20:09:58.321187Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "19:57:58 - cmdstanpy - INFO - compiling stan file /Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.stan to exe file /Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt\n",
-      "19:58:09 - cmdstanpy - INFO - compiled model executable: /Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt\n",
-      "19:58:09 - cmdstanpy - WARNING - Stan compiler has produced 11 warnings:\n",
-      "19:58:09 - cmdstanpy - WARNING - \n",
-      "--- Translating Stan model to C++ code ---\n",
-      "bin/stanc  --o=/Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.hpp /Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.stan\n",
-      "Warning in '/Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.stan', line 28, column 2: Declaration\n",
-      "    of arrays by placing brackets after a variable name is deprecated and\n",
-      "    will be removed in Stan 2.32.0. Instead use the array keyword before the\n",
-      "    type. This can be changed automatically using the auto-format flag to\n",
-      "    stanc\n",
-      "Warning in '/Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.stan', line 95, column 2: Declaration\n",
-      "    of arrays by placing brackets after a variable name is deprecated and\n",
-      "    will be removed in Stan 2.32.0. Instead use the array keyword before the\n",
-      "    type. This can be changed automatically using the auto-format flag to\n",
-      "    stanc\n",
-      "Warning in '/Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.stan', line 96, column 2: Declaration\n",
-      "    of arrays by placing brackets after a variable name is deprecated and\n",
-      "    will be removed in Stan 2.32.0. Instead use the array keyword before the\n",
-      "    type. This can be changed automatically using the auto-format flag to\n",
-      "    stanc\n",
-      "Warning in '/Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.stan', line 97, column 2: Declaration\n",
-      "    of arrays by placing brackets after a variable name is deprecated and\n",
-      "    will be removed in Stan 2.32.0. Instead use the array keyword before the\n",
-      "    type. This can be changed automatically using the auto-format flag to\n",
-      "    stanc\n",
-      "Warning in '/Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.stan', line 104, column 2: Declaration\n",
-      "    of arrays by placing brackets after a variable name is deprecated and\n",
-      "    will be removed in Stan 2.32.0. Instead use the array keyword before the\n",
-      "    type. This can be changed automatically using the auto-format flag to\n",
-      "    stanc\n",
-      "Warning in '/Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.stan', line 106, column 2: Declaration\n",
-      "    of arrays by placing brackets after a variable name is deprecated and\n",
-      "    will be removed in Stan 2.32.0. Instead use the array keyword before the\n",
-      "    type. This can be changed automatically using the auto-format flag to\n",
-      "    stanc\n",
-      "Warning in '/Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.stan', line 108, column 2: Declaration\n",
-      "    of arrays by placing brackets after a variable name is deprecated and\n",
-      "    will be removed in Stan 2.32.0. Instead use the array keyword before the\n",
-      "    type. This can be changed automatically using the auto-format flag to\n",
-      "    stanc\n",
-      "Warning in '/Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.stan', line 112, column 2: Declaration\n",
-      "    of arrays by placing brackets after a variable name is deprecated and\n",
-      "    will be removed in Stan 2.32.0. Instead use the array keyword before the\n",
-      "    type. This can be changed automatically using the auto-format flag to\n",
-      "    stanc\n",
-      "Warning in '/Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.stan', line 117, column 2: Declaration\n",
-      "    of arrays by placing brackets after a variable name is deprecated and\n",
-      "    will be removed in Stan 2.32.0. Instead use the array keyword before the\n",
-      "    type. This can be changed automatically using the auto-format flag to\n",
-      "    stanc\n",
-      "Warning in '/Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.stan', line 208, column 36: fabs\n",
-      "    is deprecated and will be removed in Stan 2.33.0. Use abs instead. This\n",
-      "    can be automatically changed using the canonicalize flag for stanc\n",
-      "Warning in '/Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.stan', line 337, column 10: fabs\n",
-      "    is deprecated and will be removed in Stan 2.33.0. Use abs instead. This\n",
-      "    can be automatically changed using the canonicalize flag for stanc\n",
-      "\n",
-      "--- Compiling, linking C++ code ---\n",
-      "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes      -I stan/lib/stan_math/lib/tbb_2020.3/include    -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials    -DBOOST_DISABLE_ASSERTS          -c -include-pch stan/src/stan/model/model_header.hpp.gch -x c++ -o /Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.o /Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.hpp\n",
-      "clang++ -std=c++1y -Wno-unknown-warning-option -Wno-tautological-compare -Wno-sign-compare -D_REENTRANT -Wno-ignored-attributes      -I stan/lib/stan_math/lib/tbb_2020.3/include    -O3 -I src -I stan/src -I stan/lib/rapidjson_1.1.0/ -I lib/CLI11-1.9.1/ -I stan/lib/stan_math/ -I stan/lib/stan_math/lib/eigen_3.3.9 -I stan/lib/stan_math/lib/boost_1.78.0 -I stan/lib/stan_math/lib/sundials_6.1.1/include -I stan/lib/stan_math/lib/sundials_6.1.1/src/sundials    -DBOOST_DISABLE_ASSERTS                -Wl,-L,\"/Users/towinazure/.cmdstan/cmdstan-2.31.0/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/towinazure/.cmdstan/cmdstan-2.31.0/stan/lib/stan_math/lib/tbb\"      /Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.o src/cmdstan/main.o        -Wl,-L,\"/Users/towinazure/.cmdstan/cmdstan-2.31.0/stan/lib/stan_math/lib/tbb\" -Wl,-rpath,\"/Users/towinazure/.cmdstan/cmdstan-2.31.0/stan/lib/stan_math/lib/tbb\"   stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_nvecserial.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_cvodes.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_idas.a stan/lib/stan_math/lib/sundials_6.1.1/lib/libsundials_kinsol.a  stan/lib/stan_math/lib/tbb/libtbb.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc.dylib stan/lib/stan_math/lib/tbb/libtbbmalloc_proxy.dylib -o /Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt\n",
-      "rm -f /Users/towinazure/edwinnglabs/orbit/orbit/stan/lgt.o\n",
-      "\n",
-      "19:58:09 - cmdstanpy - INFO - Requested 8 parallel_chains but only 4 required, will run all chains in parallel.\n",
-      "19:58:09 - cmdstanpy - INFO - CmdStan start processing\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4eb081ae1ec64fc78d3cd5275bac8298",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 1 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e79285fc868c4bf6819e288f2a37ac4f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 2 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "36525206c783415aafbbfb8bfcd157a4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 3 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f2c666d86bc44cab8c4803a73167953c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "chain 4 |          | 00:00 Status"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                                                                                                                                                                                                "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "19:58:12 - cmdstanpy - INFO - CmdStan done processing.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "lgt = LGT(\n",
-    "    response_col='claims', \n",
-    "    date_col='week',\n",
-    "    estimator='stan-mcmc', \n",
-    "    seasonality=52,\n",
-    "    seed=2022\n",
-    ")\n",
-    "lgt.fit(df=train_df_na)\n",
-    "lgt_predicted = lgt.predict(df=train_df_na)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### DLT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-12-16T20:10:10.012793Z",
-     "start_time": "2021-12-16T20:10:05.814942Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
-     ]
-    }
-   ],
-   "source": [
-    "dlt = DLT(\n",
-    "    response_col='claims',\n",
-    "    date_col='week',\n",
-    "    estimator='stan-mcmc',\n",
-    "    seasonality=52,\n",
-    "    seed=2022\n",
-    ")\n",
-    "dlt.fit(df=train_df_na)\n",
-    "dlt_predicted = dlt.predict(df=train_df_na)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Summary\n",
-    "\n",
-    "Users can verify this behavior with a table and visualziation of the actuals and predicted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-12-16T20:10:10.058418Z",
-     "start_time": "2021-12-16T20:10:10.015513Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "train_df_na['ets-predict'] = ets_predicted['prediction']\n",
-    "train_df_na['lgt-predict'] = lgt_predicted['prediction']\n",
-    "train_df_na['dlt-predict'] = dlt_predicted['prediction']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-12-16T20:10:10.107290Z",
-     "start_time": "2021-12-16T20:10:10.061136Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "      <th>sp500</th>\n",
-       "      <th>vix</th>\n",
-       "      <th>ets-predict</th>\n",
-       "      <th>lgt-predict</th>\n",
-       "      <th>dlt-predict</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>2011-01-09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.152060</td>\n",
-       "      <td>-0.127397</td>\n",
-       "      <td>0.085412</td>\n",
-       "      <td>-0.295869</td>\n",
-       "      <td>-0.036658</td>\n",
-       "      <td>13.516891</td>\n",
-       "      <td>13.502820</td>\n",
-       "      <td>13.514451</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>2011-01-16</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.186546</td>\n",
-       "      <td>-0.044015</td>\n",
-       "      <td>0.074483</td>\n",
-       "      <td>-0.303546</td>\n",
-       "      <td>0.141233</td>\n",
-       "      <td>13.269185</td>\n",
-       "      <td>13.287483</td>\n",
-       "      <td>13.281159</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>2011-01-23</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.169451</td>\n",
-       "      <td>-0.004795</td>\n",
-       "      <td>0.074483</td>\n",
-       "      <td>-0.309024</td>\n",
-       "      <td>0.222816</td>\n",
-       "      <td>13.009352</td>\n",
-       "      <td>13.007293</td>\n",
-       "      <td>13.015555</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>2011-01-30</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.079300</td>\n",
-       "      <td>0.032946</td>\n",
-       "      <td>-0.005560</td>\n",
-       "      <td>-0.282329</td>\n",
-       "      <td>-0.006710</td>\n",
-       "      <td>13.044864</td>\n",
-       "      <td>13.072037</td>\n",
-       "      <td>13.070768</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>2011-02-06</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.060252</td>\n",
-       "      <td>-0.024213</td>\n",
-       "      <td>0.006275</td>\n",
-       "      <td>-0.268480</td>\n",
-       "      <td>-0.021891</td>\n",
-       "      <td>12.989848</td>\n",
-       "      <td>13.008208</td>\n",
-       "      <td>13.005283</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         week  claims  trend.unemploy  trend.filling  trend.job     sp500  \\\n",
-       "53 2011-01-09     NaN        0.152060      -0.127397   0.085412 -0.295869   \n",
-       "54 2011-01-16     NaN        0.186546      -0.044015   0.074483 -0.303546   \n",
-       "55 2011-01-23     NaN        0.169451      -0.004795   0.074483 -0.309024   \n",
-       "56 2011-01-30     NaN        0.079300       0.032946  -0.005560 -0.282329   \n",
-       "57 2011-02-06     NaN        0.060252      -0.024213   0.006275 -0.268480   \n",
-       "\n",
-       "         vix  ets-predict  lgt-predict  dlt-predict  \n",
-       "53 -0.036658    13.516891    13.502820    13.514451  \n",
-       "54  0.141233    13.269185    13.287483    13.281159  \n",
-       "55  0.222816    13.009352    13.007293    13.015555  \n",
-       "56 -0.006710    13.044864    13.072037    13.070768  \n",
-       "57 -0.021891    12.989848    13.008208    13.005283  "
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# table summary of prediction during absence of observations\n",
-    "train_df_na.iloc[na_idx, :].head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-12-16T20:10:10.962461Z",
-     "start_time": "2021-12-16T20:10:10.930592Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from orbit.constants.palette import OrbitPalette\n",
-    "\n",
-    "# just to get some color from orbit palette\n",
-    "orbit_palette = [\n",
-    "    OrbitPalette.BLACK.value,\n",
-    "    OrbitPalette.BLUE.value, \n",
-    "    OrbitPalette.GREEN.value, \n",
-    "    OrbitPalette.YELLOW.value,\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-12-16T20:10:14.115976Z",
-     "start_time": "2021-12-16T20:10:13.357609Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x1152 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pred_list = ['ets-predict', 'lgt-predict', 'dlt-predict']\n",
-    "fig, axes = plt.subplots(len(pred_list), 1, figsize=(16, 16))\n",
-    "for idx, p in enumerate(pred_list):\n",
-    "    axes[idx].scatter(train_df_na['week'], train_df_na['claims'].values, \n",
-    "                      label='actuals' if idx == 0 else '', color=orbit_palette[0], alpha=0.5)\n",
-    "    axes[idx].plot(train_df_na['week'], train_df_na[p].values, \n",
-    "                   label=p, color=orbit_palette[idx + 1], lw=2.5)\n",
-    "    fig.legend()\n",
-    "    fig.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# First Observation Exception"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is worth pointing out that the very first value of the response variable cannot be missing since this is the starting point of the time series fitting. **An error message will be raised when the first observation in response is missing.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-12-16T20:11:05.798766Z",
-     "start_time": "2021-12-16T20:11:05.703192Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "DataInputException",
-     "evalue": "The first value of response column claims cannot be missing..",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mDataInputException\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-14-2dbfe269e00e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mtrain_df_na2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtrain_df_na2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mna_idx2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df_na2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/Desktop/uTS-py/orbit/orbit/forecaster/full_bayes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, df, point_method, keep_samples)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpoint_method\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_point_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpoint_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Desktop/uTS-py/orbit/orbit/forecaster/forecaster.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, df)\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_training_meta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m         \u001b[0;31m# customize module\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_dynamic_attributes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_meta\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_training_meta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m         \u001b[0;31m# based on the model and df, set training input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_training_data_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Desktop/uTS-py/orbit/orbit/template/ets.py\u001b[0m in \u001b[0;36mset_dynamic_attributes\u001b[0;34m(self, df, training_meta)\u001b[0m\n\u001b[1;32m    144\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseasonality_sd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraining_meta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTrainingMetaKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRESPONSE_SD\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_valid_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_meta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_valid_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_meta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Desktop/uTS-py/orbit/orbit/template/ets.py\u001b[0m in \u001b[0;36m_set_valid_response\u001b[0;34m(self, training_meta)\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_valid_response\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m             raise DataInputException('The first value of response column {} cannot be missing..'.\\\n\u001b[0;32m--> 154\u001b[0;31m                 format(training_meta[TrainingMetaKeys.RESPONSE_COL.value]))\n\u001b[0m\u001b[1;32m    155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_init_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mDataInputException\u001b[0m: The first value of response column claims cannot be missing.."
-     ]
-    }
-   ],
-   "source": [
-    "na_idx2 = list(na_idx) + [0]\n",
-    "train_df_na2 = train_df.copy()\n",
-    "train_df_na2.iloc[na_idx2, 1] = np.nan\n",
-    "ets.fit(train_df_na2)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "orbit39-cmdstan",
-   "language": "python",
-   "name": "orbit39-cmdstan"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
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-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/exploratory_data_analysis.ipynb b/examples/exploratory_data_analysis.ipynb
deleted file mode 100644
index 59ab108c..00000000
--- a/examples/exploratory_data_analysis.ipynb
+++ /dev/null
@@ -1,1069 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "green-navigation",
-   "metadata": {},
-   "source": [
-    "# EDA Utilities\n",
-    "\n",
-    "In this section, we will introduce a rich set of plotting functions in orbit for the EDA (exploratory data analysis) purpose. The plots include:\n",
-    "\n",
-    "* Time series heatmap\n",
-    "* Correlation heatmap\n",
-    "* Dual axis time series plot\n",
-    "* Wrap plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "specified-cuisine",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:19.737054Z",
-     "start_time": "2021-07-13T22:35:18.374991Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import seaborn as sns \n",
-    "from matplotlib import pyplot as plt \n",
-    "\n",
-    "import orbit\n",
-    "from orbit.utils.dataset import load_iclaims, load_m5daily, load_energy_hourly\n",
-    "from orbit.eda import eda_plot\n",
-    "from orbit.utils.plot import get_orbit_style\n",
-    "from orbit.constants import palette\n",
-    "\n",
-    "orbit_style = get_orbit_style()   \n",
-    "plt.style.use(orbit_style)\n",
-    "\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "%reload_ext autoreload"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "present-format",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:19.960093Z",
-     "start_time": "2021-07-13T22:35:19.750841Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# read in data\n",
-    "df = load_iclaims()\n",
-    "df_monthly = df.copy()\n",
-    "\n",
-    "df_monthly['month'] = df_monthly['week'].dt.to_period('M').astype(str)\n",
-    "df_monthly['month'] = df_monthly['month'] +'-01'\n",
-    "df_monthly['month'] = pd.to_datetime(df_monthly['month'])\n",
-    "df_monthly['year'] = df_monthly['week'].dt.year\n",
-    "df_monthly = df_monthly.groupby(['month', 'year'], as_index=False).mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "4a34df7d-9858-4987-9119-03636f8a035d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "      <th>sp500</th>\n",
-       "      <th>vix</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.318452</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.417633</td>\n",
-       "      <td>0.122654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>13.624218</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.168794</td>\n",
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-       "      <td>0.110445</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>13.398741</td>\n",
-       "      <td>0.236143</td>\n",
-       "      <td>-0.292477</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.465229</td>\n",
-       "      <td>0.532339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>13.137549</td>\n",
-       "      <td>0.203353</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.106918</td>\n",
-       "      <td>-0.481751</td>\n",
-       "      <td>0.428645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>13.196760</td>\n",
-       "      <td>0.134360</td>\n",
-       "      <td>-0.242466</td>\n",
-       "      <td>0.074483</td>\n",
-       "      <td>-0.488929</td>\n",
-       "      <td>0.487404</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week     claims  trend.unemploy  trend.filling  trend.job     sp500  \\\n",
-       "0 2010-01-03  13.386595        0.219882      -0.318452   0.117500 -0.417633   \n",
-       "1 2010-01-10  13.624218        0.219882      -0.194838   0.168794 -0.425480   \n",
-       "2 2010-01-17  13.398741        0.236143      -0.292477   0.117500 -0.465229   \n",
-       "3 2010-01-24  13.137549        0.203353      -0.194838   0.106918 -0.481751   \n",
-       "4 2010-01-31  13.196760        0.134360      -0.242466   0.074483 -0.488929   \n",
-       "\n",
-       "        vix  \n",
-       "0  0.122654  \n",
-       "1  0.110445  \n",
-       "2  0.532339  \n",
-       "3  0.428645  \n",
-       "4  0.487404  "
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "dc800769-083f-4c36-b53d-63f3c2c125fe",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>month</th>\n",
-       "      <th>year</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "      <th>sp500</th>\n",
-       "      <th>vix</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-01</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>13.348773</td>\n",
-       "      <td>0.202724</td>\n",
-       "      <td>-0.248614</td>\n",
-       "      <td>0.117039</td>\n",
-       "      <td>-0.455804</td>\n",
-       "      <td>0.336297</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-02-01</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>13.084544</td>\n",
-       "      <td>0.126464</td>\n",
-       "      <td>-0.218936</td>\n",
-       "      <td>0.051658</td>\n",
-       "      <td>-0.451601</td>\n",
-       "      <td>0.212198</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-03-01</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>12.974762</td>\n",
-       "      <td>0.100341</td>\n",
-       "      <td>-0.177173</td>\n",
-       "      <td>0.060325</td>\n",
-       "      <td>-0.401501</td>\n",
-       "      <td>0.084137</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-04-01</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>13.014468</td>\n",
-       "      <td>0.102068</td>\n",
-       "      <td>-0.237037</td>\n",
-       "      <td>0.079888</td>\n",
-       "      <td>-0.372733</td>\n",
-       "      <td>0.123934</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-05-01</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>12.927627</td>\n",
-       "      <td>-0.007502</td>\n",
-       "      <td>-0.277799</td>\n",
-       "      <td>0.095741</td>\n",
-       "      <td>-0.460032</td>\n",
-       "      <td>0.801549</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       month  year     claims  trend.unemploy  trend.filling  trend.job  \\\n",
-       "0 2010-01-01  2010  13.348773        0.202724      -0.248614   0.117039   \n",
-       "1 2010-02-01  2010  13.084544        0.126464      -0.218936   0.051658   \n",
-       "2 2010-03-01  2010  12.974762        0.100341      -0.177173   0.060325   \n",
-       "3 2010-04-01  2010  13.014468        0.102068      -0.237037   0.079888   \n",
-       "4 2010-05-01  2010  12.927627       -0.007502      -0.277799   0.095741   \n",
-       "\n",
-       "      sp500       vix  \n",
-       "0 -0.455804  0.336297  \n",
-       "1 -0.451601  0.212198  \n",
-       "2 -0.401501  0.084137  \n",
-       "3 -0.372733  0.123934  \n",
-       "4 -0.460032  0.801549  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_monthly.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "decreased-indonesia",
-   "metadata": {},
-   "source": [
-    "## Time series heat map"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e0e24d54-e647-4a1f-88c1-decbb7c66943",
-   "metadata": {},
-   "source": [
-    "### weekly - year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "organic-qatar",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:20.355247Z",
-     "start_time": "2021-07-13T22:35:19.999806Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 800x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = eda_plot.ts_heatmap(df=df, date_col='week', seasonal_interval = 52, value_col='claims', fig_width = 8, fig_height=8, normalization=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "01f88181-432f-4c01-904b-fcd316c358c3",
-   "metadata": {},
-   "source": [
-    "### monthly - year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "least-amsterdam",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:20.654586Z",
-     "start_time": "2021-07-13T22:35:20.357618Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = eda_plot.ts_heatmap(df=df_monthly, date_col='month', seasonal_interval=12, fig_width = 6, fig_height=6, value_col='claims', normalization=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a2bc6e96-584f-4012-afd9-f44ed4477f0e",
-   "metadata": {},
-   "source": [
-    "### daily - week"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "20b6f3af-288a-4570-9f15-461c0595d188",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# daily data \n",
-    "data = load_m5daily()\n",
-    "data['date'] = pd.to_datetime(data['date'])\n",
-    "data = data[['date', 'sales']]\n",
-    "data = data[(data['date'] >= '2015-01-01') & (data['date'] <= '2015-12-01')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "44807a8c-5827-414e-b134-9afe18f42c60",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>sales</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1433</th>\n",
-       "      <td>2015-01-01</td>\n",
-       "      <td>26452</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1434</th>\n",
-       "      <td>2015-01-02</td>\n",
-       "      <td>39633</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1435</th>\n",
-       "      <td>2015-01-03</td>\n",
-       "      <td>41085</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1436</th>\n",
-       "      <td>2015-01-04</td>\n",
-       "      <td>39647</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1437</th>\n",
-       "      <td>2015-01-05</td>\n",
-       "      <td>34691</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           date  sales\n",
-       "1433 2015-01-01  26452\n",
-       "1434 2015-01-02  39633\n",
-       "1435 2015-01-03  41085\n",
-       "1436 2015-01-04  39647\n",
-       "1437 2015-01-05  34691"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "e1c0d795-bce5-46f7-b71d-4218d0d7fce1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x144d148e0>]"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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ocVR9zezmD68PhvH1xw8AAEIJGs10srG5HAGQopELJz2me+W7uroAAOl0Gr29vQAgZM7VMj7OZzKQXDetr/f7/fD7/YjFYrJCm9HfQaG8maQEYc7cQNdUrgiH1QKHzQK/ywaGocIchUKR5/XBMM79f8/gWCh1ot8KZY4QSeXxyK4xsOyp36GUNH/o8L95GXNP7OHnpafrs/nFoyEAwGUrOwAAPUH+3I9HjQtz+8d4YW51dxA+lw35Eotc0bg4t20ogol4Fm9b342g2454hi6MzaJYYpHIFtFcFqV8ZWHOLFE1ky/h968fh6NcNm52p16ziKYLaHJXl3K77BY4rBZETRgrpuJZ3HbfDtitFixq82JqFoS5/ukkDk0kTD8uhb+Oydg5V8Vlino0CXPZbBaf+MQncNNNN8kKYQMDAwD4rq2k8ynpnFoL6ZC6bt06AND8eoZhsGbNGgDAgQMH6l4fDocxMTGB5uZmQZDT+jsolDeT5Cw55pK5IrxOvozbYmEQcNlNbzBBmX2OhVK47b7tgpuDQpktfvXSICbjOewdjZ3ot0KZI/zypUH8n/vfwMO7Rk/0W5l1EtkiHDaL0AkxM8uOOY7j8ORefmM4limY1vDgZGIsloXVwmBBC1+R0yM45oyH8h8Yj6PZY0dnwCk4r8wQeB7bzTeUe9v6HgTcduqYMxEyDyaOOafNAruVEURzozy6awyxTAG3buoDMDe7veaKJWQKJeEcEBiGQdBjN7xWYFkOt9//BqYTOfx/t6zHmfOakMwVTd2IiKTyuPUnL+O2+7abdkxKhZf6Q8gV+fituSouU9SjSZhzuVx4/PHH8dBDD+Gvf/1r3fd3796NnTt3IhgM4rzzzsO1114LAHjooYfqXtvf3489e/Zg3rx5ggi2YsUKLF68GHv37kV/f3/dzzz44IMA+MYTBKXf8dBDD4HjuKrXX3PNNWAYBo899hhKpeqHciwWw3PPPQePx4NLLrmkwdmgUMyHTAxmoysr6WgFAEG38Qc65c3noTdG8cSeCewYjp7ot0I5hUnlinjmAN+ZnI4TJyezIewQ19H3nzmCYkk+h/dUIJEtIOCywePgN7Rmu5T1wHgCQzN8oHuhxL0ppbNzjelEDm0+BywWPgO6q+yYGzPomGNZDgcnEljdEwDDMEJ3S6NCTLHE4ok941jc7sWqbj8CLhvyRRbZwsnx2R2eTODI5Nx1MUXS1cIcwzDwOW2mlLJyHIf/fWkQHocVH7pgIQAgaZITz0zI8zform9+EnTbEU0bq64ZCCXx2rEw3rmxDzdu6EF7uXR/2kTX3H89eRDhVB4zKXMrgSg8pJM1QIW5UwHNpaz/9E//BAC44447cOzYMeHrk5OT+PCHP4xSqYQvfOELcLvduOiii3DGGWfghRdewA9+8APhtfF4HB/5yEfAcRw+//nPVzViuP3228FxHD760Y8ikag8ML7//e/jhRdewJlnnokrr7xS+PpHPvIReDwe3H333XjppZeErx86dAj/9m//BgD4whe+IHx9wYIFuOGGG3Ds2DHceeedwuQ1n8/jk5/8JBKJBD75yU8K3WYplDeLXLGEfHmxY4Y9XUwqV4TXQYW5k52jU+Xu0qfhwzeRLeDq7/4dj+4aO9Fv5ZTn6f2TyBb4sYiOEycfX3l0H9763b+bLp4dLi/iB2fS+MuOU9s1l8gW4XfZBWFuth1zxC23tpfvqHc63nehZA5tvkpes8tuRavXgXGDjrmhcBrpfAmruvhzW8kqM/Yc3TEcRSiZx9vW94BhGATK4snJ4pr72K+24XMP7DrRb0MWIjo1i/LVfC6bKeV6O4Yj2D8ex80be9FZFoDn4rwqRsRJCWGuyW1HzGDp9GCI3ww4dxHfMKC9fP+ZVc762rEw/rDtOIC5eX7fbF7qD+Hyb2/BgEk5ohzHYfOBKSxu86LZY6elrKcAmoW5O++8E1deeSWGhoawZs0aXHXVVbjhhhuwdOlS7NixA7fccgv+9V//FQC/u/HLX/4SgUAA//zP/4xNmzbhlltuwbJly/D3v/8d119/PW677baq43/mM5/B5Zdfjueffx5Lly7FLbfcgk2bNuGOO+5Ac3Mzfvvb31a9vq+vDz/4wQ+QTqdx8cUX461vfSve9ra34YwzzsDExAS+8Y1vYMOGDVU/86Mf/Qi9vb24++67sXr1atx6661YtmwZ/vCHP2DTpk346le/qvW0UCiGEZdVmF3KksyVhHwOgN+BPB0n/ic7R6b4hfFcLLmYbUgw+tYjoRP9Vk55Ht5ZEV1OlkUmhef3rw3jly8O4uhUEmETHQrpfBHD4TQuXdGOJo8d33/2CPLFU9c1l8gW4XPa4Lbzz83Zzph7Ys842nxOofHB6fZ85jgO04kc2v3VjdS6m1yGM+aEfLkeXpgTssoMfqaDM3z+5oY+fiM/4CoLcydBztxkPIvhcBqh5NwN+o8KjrmKMOd12EyZ/zxZznP8h3PmC5vWyTkYnE826WubPwBkHp83tFYYCvPC3PxWvny8I8CLlGY45vJFFnc9uAcOmwVnzGtCocSZkut4shLPFvD5B3ZhIJTCHpMiQvaPxzERz+LylR3wmtjUhnLi0NWV9cknn8R3v/tdrFy5Elu3bsWWLVuwZs0a/OxnP8MDDzwAq9UqvP6MM87A66+/jne/+90YHh7GE088gY6ODnzrW9/Cn//8Z9hstqrj22w2PP744/jqV7+KpqYmPPbYY5iensb73vc+vP7661i9enXde/roRz+KJ598EhdddBFeeeUVbN26FRs3bsSf//xnQSQUM2/ePLz22mv42Mc+hlgshkcffRQulwt33XUXNm/erNiRlUKZLcSTDb4cwrxFTypXFHaJASDg5ndWCqd4OdKpRKHECkH8idNwV+zIJL/DOBE3njdEkSeSyuOFIyGcMa8JgHld3yizz56RGP7jkX3C/w8lzRPmDpfvv03zm/GJixdjNJrBA2UnxKlIPFuAX1zKOovliUcmE+ifTuGatZ1o8fILcLPjLLKF0pvSwEIviVwRuSIrOHYI3UE3JhM5lAw0HDkW4q/dZR1+ABVhzqjAQ8QLIiYG3PxxT4bNjDfKcRhm5bXNBpGyY07sFvOb5JgjguSSdh+sFgYeh3VObnhGFRxzAbfdcNn7UFlcXlAW5gTHnAnzrJ++MICjU0ncftlSrCmL4nPxHL9Z/L/HDwh5mWbNqzYf4MtYL1/ZUe42ffqe31MFW+OXSPyQzYY77rgDd9xxh6rXL1++HL///e9VH9/lcuHLX/4yvvzlL6v+mauvvhpXX3216tf39PTgpz/9qerXUyizTe1kI5YpwO2wyrxaPSWWQ6ZQ7ZgjeRXxTAGtNRNhytxkaCaFQolfnJyOkxviFpykwtys8sTecRRZDu87bwF2Ho+eds6dk5VIKo9/+u12gAPefdY8/GHbcVMdc4fLHfVWdPlx4dI2/PyFY/jR5qO4ZVMfXHbjz6m5BMdxSOaKVcLcbJaykm6s163tFkrIzL7v/s/9b+BYKIWnPzc385ND5b+7rcYx1xN0ocRymEpk0R106zp2rYBmVikrOS4pv6045sz97I5MJtDsdVSV+RrljeMRAPw5YFlOyPWbSxBRqtlbEaX4jDnj859IugCX3SKMXd5ZEDWKJRbPHpzCW5a2VW2Ma6G2AYaYJrdDeI3e4w/NpOGwWdBZ7j7dQTLmDDopOY7DT57vx8JWDz55yWLc/bfDAPjKoNbT0Puy5dAUfv/6ccxrceN4OIO4SXP4zYem4HfacNbCFnidNoRTaVOOSzlxaHbMUSiU2YFMNsgDOJoxZ1FFyjVIV1agsvtGF92zQyxTwLbBsKnHJI4xwHgJzsnIYeqYe1N4eOcYnDYLrlnbBb/LRseIk4SvPLoPo9EMvvr2Nbh0RTsAYCZlXpnaQZEw53Xa8PGLF2MinsWWQ1MNfvLkI5UvgeMAv8subI7NZjOGJ/eOo8XrwDmLWoQusDGTnv8A7w565sAk+qeTYA04z2YTQTyrEZ+EzqxR/eM+cY62+nghgzTCMirwENcVOW4lY86853MiW8CNP3oR/+/xA6YdE6g45gBzSji/+dRB3P30YcPHEUPmwE1VGXN2ZAus4WqPaDpfnV1nkuAn5nvPHMEnf7MdD+3Un8dJcvbkmj/wr9H/jB4Op7GgxSMIs0S8noobe3ZkCiUkskVsnN8Mp80qCIcJExp3nGzEswV86S974HPa8M138tFaZoj3oWQOO49HcfHydjhsFnhn4RqmvPlQYY5CmSOQTlO95YlozKRSFrIL6JVwzNFF9+zw7b8ewrv+52XMmJjfcmSqIszN5fKT2YI0voimC7Pa9W77UBivmyyqniyMRTN4fTCMK1d1wue0IeCym5KXdHgygXfc86Ip5TEUaXYej2JZhw/vOWe+4IKeMbWUNQG33Yp5zXzJ0+puvjQpYnLJ5VwgUS5F5B1zJGNudsacWLqAgxMJXLq8HTarZVaezU/tnQDLASw3d2MQiHhW65jrFoQ5/Tlz08kcmjx22K38ksdX3qQ06pCaTvDHddr4482GY+6l/hlkCiVMJswbO4slFntGKhlXZjjwH9g2gp9s6TdVGCBjS62ABhj/7CLpQnV2ndNq6obnS0dDuGfLUf53GXAuKzrmPMbGimKJxUgkLZSxAoDfaYPLbjHsmCNu7eZyaX7lczv9MtB++8oQxmNZ3HXdKuG5aUa5+8v9M+A4CBtxPqcV6Xxpzm6+UNRBhTkKZY5AWrUTYc6szqxkAiNVymp291cKz6vHZsByMLU9vFiYO912xULJXFVZ3myVs+aLLD72q2344p93z8rx5zpP7BkHxwE3ntEDwLzuzS8cCWHn8Si2D0UMH4sizXQih65yd0GSU2a2Y255p09wVpASz1Mx04ZsfFR3ZZ2dv3OqLLj0NfPP/dkQ5h7fPS7826wNP7OZLp+HOsdc+Zoej+kX5mq7vfqc/Dk2usE1ncxVvd/ZyJjbcmgagLmbcYcmE8gUSrBbGVOOzbIcwqkc8iUWzx00z0FL3GIBV2XuSkRVo+85ks6jWSR2mdVUAuCvt3/+w05YmPL5NTBGEjdc0F3f/KEyVuibZ47HsiiUOMxv8QpfYxgG7X6nYcecUIZcPsdmCapvFkenkvjeliFD2ZaE4Rm+vPS6dV2CW9eMDU/i2J3fwgurpInJ6VhRcypBhTkKZY5AJgV9ZUeCWRNoIvh5a7qyAjTYfTaIZQpC2aWZi6sjkwn0NrnhsluQPAnCpc2ElPES0XoiNjvC3OaDk4ikC3N28TrbHCqXK563qBWAecJcrLzACqfNE6opFVK5IlL5klCG1FYurTMrY24mmUMomcOKLr/wNeIkm83stRMFccwFXDY4bRYwzOw55qZq8s+aTChPqz5+Fq8emxH+/1x1yRPHXLu/WoDoNqOUNZET7gmgEuthhmNO3EXW7K6sHMfh+XKpuJnCHCljPWtBC4BKtYZeIuk8iH7x1L4JQ8cSE00XEHDZYLNWlqpEVDWyOVkosUhki4KbC+DdsWa4uViWw+f/uAvTiRy+dO1K/r0a+OzI/SpZyuoxNlYMlQWjhW2eqq93+F3CuKSXOseci5Synhyi0W9eHsSvXxvDgfG44WOFkjnYrQyCbjusFgZ+p80U8Z5cG6SE3nsauxJPJagwR6HMEYRS1vLOuVkTaKlS1oDJk39KhTeGK64gs4TPYonFQCiFZZ2+cuel0+vBSxo/XLSsDcDs5cz9afsIgNOzVBgAxmIZvoS17PwIuu2IZwuGSyNISZKRkh6KPGTnXOgO6bLDZmFM68pKNhqWd4qFubK4cQoKc3HBMWcDwzDw2K3IzFL5fH1nT3Mdc6SM9fzFvNhuVnat2VQy5lxVX+/0O2Fh9DvmcsUS4tlilWPOXxZ3jDhLsuUMrSphTsiYM+ezOzKVFLo4zoYw95by89RoJp64MuC5g1OmRU1E0oUq8QyoCDxGRNVaNxfAz4/zJRb5orHsur/um8CWQ9N458Y+vP/8Bcbfa6YAj8MKh61+uW40K3oozHdkJY4rQrvPiXDKWCdk0lGXlCF7TzLHHHnmmTGHn07wjl2m7KAMuO2mHJdsAJBxx2dSUxvKiYUKcxTKHKG+lNWcCXRSKGUVN3+odHOimMsOUbmeWRP045EM8kUWyzp4Ye5k2XU0C+KYIwuJ2ShlnU7k8Fy5bChfYpErnnqCQyPGoln0NLlEE0gbOM54ODgpmQ+n6HgzG9QG51ssDJq9DtMyLg9N8K6BKsecc3ZLPE8k4lJWAHA7bLPmmKsIc7wgZbda4HVYTXs2P7Z7HC67BTdv7AUwdzfjQskcHFaLsClAsFkt6PC7MK7TJU1yFsXCnNeEckipZhV+oUzNnHNMGqt4HFbBxWkGbxyPYEGrBwtb+RJGoyWc4pK6dL6ErUdCht8jwJeyNtU4xfxO486raI1oBJgnHJHIkQ9fuBBOmxUOq8WQUBKTOAcEo5E0xDG3oNVb9fWOgJOPYjHw/CCbcOQcC6KRyZueHMehaLARiBRkM9iMuJ9aZ63fZTOlQUytm/JkEz8p0lBhjkKZI1RKWWfJMecQZcwZDI2lyLO9yjFnzgPyyCQ/SVjW6YfPZTNcenKycXgyAb/Thg19TQCAiZh52VmEh3eOosRywgTqdHPNcRyH0WhG2BgARBk2BhfzZCEUoaWss0JtOSQAtHodppWyHioL41KlrKeiY46IIGQx6XFYZ7GUlRecOkSfXZPHYcqzeTKexeuDYVy+ssP07FqzmU7y5aZkU0BMd5NLdylrrZsU4MU+l91iaAE7LXFcu9UCj8NqWlfWLYem4bRZ8JalbcgVjbu5AH4sHphO4Yx5TYKQaPRZR5y5/3DOfAC8a8wMojUNGgBRR10D75k4uJskmkqY1amXXBdep9XQ+Y1mCgh66vPlgMr71+2Ym0nBwqDqmQ9UxiIj5axC4w7v7Lq57npwDy799hZTsuAIfHQDf00bHYc5jkMoma/JojTJMZctwGph4C27181qakM5sVBhjkKZI9R2ZTVrZ1ux+cMc3T0/WSmWWLwxHDU9w4/swhLHnNm7jnOdo1NJLO30oTPAu0rMdsxxHIc/bhtBwGXDDev5xgen2zmeSeWRL7LokRLmDF7HESrMzSq15ZAA0OpzmNaV9dBEHM0ee9Xiwm0njrlTT5hLikpZAV6Ymy1noNRnFzAp2/HJcjOX69f1VDbj5ug9GKpxlYjpaXIjlMzpcjEToUScMQfw8yEjIkEoQY5b/Z75TtbGP7tUrojXB8M4b3Gr0KnWDFFj5/EoAODMeU0VkcvgRh9xVp05vwmruwN4+sCk4GLKFUtC+L0WsoUSMoVSVbkpUHEFGTkXlTLLyrHNFuZaRNlqRo4ZTRdkHXOkKYbejbOhmTR6m911ZbLkPpw2JMzx57hFKGU1XzR66WgI9792HCORjKnzNVLGChhfI8UzReRLbNU4EXDxESEcZ0xMjGf4DEaymWHGvUE58VBhjkKZI5DBNOi2m1rKItX8weuwwmZhqGPOZA5OJJDOl3DJcr59uVmlrEfLwtzSjtMvY24mmcNMKo9lHT44bBa0+RymZ8ztG4vj0GQCN57Rg9byAu50c8yNRvgMJylhzuhCM0oz5mYVsoDqqHLMOZHIFQ2XZHMch8OTSazo8le5mawWhncdnRalrLPpmMvB77LBZRdHTdhN2TR7fM843HYrLlvZPqc344irpFbkIpDOrHqa/oQS9aWsAD8fShp4jko55oBKLqdRXuqfQaHE4dIV7SJnm/Hjkny5M+c3C8KO0WeduFz46jVdiKYLeO1YGAcn4rjhh1tx6befE5yhaolKuNoAc0oiZ7OUNZTgu73ayw0rfE677mOyLId4tiBs9NZis1rgd9p0zeM5jsNwOI0Foo6shI5yWb0xYa768/Ob0LRDTL7I4t8f3iv8f1MbrZXLWM047nSy3G26qkmMDYUSh2zBmAM2likI+XKAqPPtKfhMPp2gwhyFMkdI5krwOqywWBjTSln44/LHEQtzDMN3CKJdWc1lR7mM9bIVHQBMLGWdSqA76ILfZYevHFJ8umSgEbcgCZ7vDLhM78pKmj7csmleZRF0mpULj0V5YU5c1mJWED1ZZNGurLODVHA+cWwYLWcdjWaQzBWxQtT4geCdxey1E4m4KytAHHOzlzEnJ+4YabqSL7LYeTyK85e0wuOwCQvkuVjKSlwlco657qD+zqzTSWlnG++Y038upJyOAJ/Laca8iuTLXbqio5KrZsJm0c7jUThsFqzqDggdTo2XslZcides7QIAfP2JA7jxRy/i8GQSLAeMa/zsSMZyrSjlN6G7Z0U0EosavDBuhmOuutGIfsdcIlsEx0l3ZCUEPXZdedTTyRzS+RLmt3rqvtculLLqn2dFUnn4nDbBjec16fwSfvrCAAamU+gM8O/VrE1wgI9OIRid+0jFTJB5lVGhPZEtVl0bFdfnqfdMPp2gwhxlzpErlkybBG89EsJju8dMOdZsk8wWhNICs0pZgErrbHEpK8A/7Odqh7Y3iyf2jONXr46adrzt5cYPFy9vB8OYs4vHshxfytnhA2BOxsrJBMnXI39/V8CFqUTWcKdQAsdxeGTXGJa0e7GhLyhM/E+X80sYjdY75szoMlgoscJkPEKbP8wK0xLB+aR0z2g5K1mkrOgK1H3P47QifQruztc55uw2pAslw6VHUkwnc1VOR4B/NnOcMfHhyFQChRKHtT3850Zc8nPRMUdcJbKOuSZecNbTmVUQjfz1jjkjznNZYc5lRzxTNHStcByHLYemsaDVg0VtXuE6NCqgcRyHncejWNsTgMNmMTVjzmZhEHDZsbzTh0VtXuwbi6Mr4MJ7z+Vz57RuypBnRbOMY86Is622YyhgXhngdI0wZyRjjszPgzKOOaA8j9dxT5Py4oUSwlyHSaWsYuGT5DqaIcwdD6fxw81HsLjdi09cvASAuY65w5NJQQyPGVwjSUYVkCYxBoW5WKaAgKu6szBAM+ZOdqgwR5lTlFgO//jTV3HTj1805Xjf+tshfPFPu2dlQm02yVxRGFjNKmUhxwUqgg4h6DFP/DsZOTgRxx2/34l7Xjhu2vWxfSiCJe1etHgd8DttpuzijUYzyBZYLOvgHSs+k0sC5jp1jrmgC4USZ5r7KpUvIZzK48z5zWAYxjQXwckGcaOQRTBgTsaceBxLmlBaSamHuK7EpaYtXn4hMGPQMXdwgghzvrrveeynpmMuni3CVi7VBXjHXInlkDe5+1+uWEI0XRA6shKahDw4/ffd/jG+k+7qsjDHMAyaPHbDC83ZQMpVIoZki+oJoych7q3eaoHHbzCrdTqRg9XC1AlHAbe97GjXf62MRDIYjWZwUbkLuVmlrIlcEbFMAYvb+XvZ47DCwpiQMZfKodXngMXCgGEY/MfbVuO2S5fg8f/zFly0jI/1iGp8XpPX1zrmTGn+kKoX5swQ/LKFEhLZYpUI7HPZkSuyKOgYO4RyXrd08wcA5Xta++dHOrLOlyhlbfE6wDAGmz+k8oJrm8DHsBifV/1/Tx1EtsDia29fK2xAmVX9w3EcjkwmsLzLD5/TeKRQSKIrdKUSQf+5KJY3PMWOudkS5h56YxQD08nGL6SYAhXmKHOKX754DNuHIuifTpoilkRSeaTyJYyZXPo2G6RyJWGXpsljvJSlclx+kPaIMmwAftF9ugpzuWIJd/x+J/IlFkWWM0XkmoxnMRLJYNOCZgDlzksmCHMk72JZZ9kxZ3JJwFzn8GQCPqcN3eWcoa6A/rwhKUhwNVm4CY650+T8EsaiGViYyiIYMEuYy9f8/9NzzJlNphO5OkdQq+CYM9bB+Eg5CHuZRCmrx2lF+hQsm0lkC/CLQrU9jtlpdEEWbe21DQRMuO/2j5eFue6g8DW97prZRmrxKsZIGH1IIsMP4Bex+ZL+TqfTyRxavQ5YLdVdZAU3jIHPjsQKLGojz3xznG1EkCLPOn4jymZKxlyrt/LZXbayA3desxJ+l11osKDVLU1Krmsz5rwOM5o/8N0s/aLNajPKAMkmiLjRiBHBLyacA2XHXCJb1NyVdGgmBQBYIOGYs1ktaPU6DXdllcoHNKPM8o3hKFZ3B3Dh0jbTGlQRQsk8IukClnf6EHDZDI+X0o4545UI5PoXu+Rno/PtoYkE7vjDTvz3ln7TjklRhgpzlDnD8XAa3/nbYQBAocQhZcIkmAzWJDx/LpMQlbIKpSwmuHaSuaKQXScm6LYjW2CRLZx6C6tG3P30YRycSAgTHjMWK6SM9awFLQAqJS1GERbGHdWT9NOl1JKU8ZJFcpfJnVkFR0V5Ml1ZBJm7gJ3rrt2xWAadAZcQWg1UJpCGhLnyz5JFkNmdWflS7wQe2HYcX310P/aNxUw9/lyHZTmEkrk6cafVpIy5/ukkOgPOqpIZgsdx6pay+kV/r7sszJntDpwqj2EdgerPTnguGXC37R+Lw++0oa+5UppuZnatmciVhRKIYKdLmJO4N4CK80qvu0QqGxAwp/y/VuAh16LRBTc5brPIyeR32U3JVWv1Sbu6yO/S6piT6pwK8E1nPA6roTLvaDqPJre9ak5shttIqlMv2UjVM5cnz07FjDm3PsfYUJh3zEkJcwB/L+otZSUddVskOuoadWeyLIepRFZw9ptxv4kh0Q3LOvwIuPQ11hAj3XXbuHhP3ld1Kav5nW8fL0dBGXXeU9RDhTnKnIDjONz14B5kCiVs6ON3eI128CuVOxoBc1+Y4ziuLKCVhTkTJuaElKhEVgxpwX66NYB47VgY9/59ABvmNeEjFy4CYI5YQIS5jYJjzpxS1sFyycGiNr7kwGfSJP1kIJzKI5TMC6IkwJeyAjCtM2vFMcdPnMwIl5biv546iGu//8KcFejGopmqxg+AuCur8bIhUj5lVCgSMx7L4NxvPIsr7/477vzTbvzixWP41UuDph3/ZCCaKaDIcnUiQWt5cRgykDHHcRz6p5JY0l5fxgoAnnLzBz3X9Hgsg59vPWZaVqSZJHKFqkxWzywJc5WmHfUZc4B+QZzjOOwfj2NVd6BKfGhy2xHNFEwdgzL5kpADqhdx8wApXHYrAi6bbmFOyolnxF3CcRzvUpU4bmUzw4DAU9OwwqxSVjIWt1QJc8Ycc+l8Eel8SdbtSMpFIxo3P5XKOH1OG5IGzgXv5qopkTXBbUQ+t/YqYU7/fE0o51UQ5ioivkZhbiaNdr8THkf92gDgc+amElldY0VEKEOucTsazHUkxy6UOMHZb7ZjjghzyztNEuaSObjtVngdFcduxTGn/1ojczJxV1Yz3KRiOI7DY3vGAWgX1in6ocIcZU7w0M5RvHAkhFs39eHqclcno2JJIlsAeabMdWEuW2DBcqhyzAHmPGxSuVJd4wfx7zCzS9uWQ1MYDKVMO95s8NXH9sFps+Dud20QJpNaJ41SHBiPw+e0YXFZQOMdc8YXQdOJHOzWSpbNbJSyFkos7n9t2HSXmFGOiCZJBMExZ1IpKxGKWmvcCWZmzCWyBfz6pSEcGI8jMwcdqtlCCaFkvqrxAwA4bBa47cZyVsj4sqR8X5jZAGL3SAzTiRyuXtOJe/5xI6xzNNx+NpFzG5HrOZzSX440lcghlS9hcXt9DhHAC1ZFndlrv31lCF97bD9eHpjR/f5mC94xJxbm+H+bXcpKOobWOuaMPv9HIhkkskUhX044rseOfJFFtmBeVt4X/7wb1/9gq6ExopFjjnxvWmNZdqHEIpIuoM0vLe4A+p6jfFamdBdZwQ1j4FlaW9prVpMG8qxrqclWM3LcmWR9+aYYIhxpnc8LopS3XpTyufR3OiXHrs0GNKP5Q6XRiOj8GnBmTsWlxwcxwjxe4/kdDqexoEXaLQfwwly2wOo6H2EJARgwnusIAJPlc0KEOUHkMqE6BeAbPwAol7JadZUJiwklcmjzO6ryXwMmmCIEx5xImLOU3aRmdWU9OJHAwDS/njvd5lUnEirMUeYEdz99GK1eB/7t+lW6d9hqEQ8k/XNcmEuU7d1Cxpyb2P+ND4ZJGcdcsHyezdppmopn8eH/fR3f+tshU44n5uhUAsdMEvyGZtLYOL8ZS9p9olJW47tBoWQOHQGn4FAIuO1gORguyeazbCrHnY3mDw/uGMWX/rIHj+0eN+2YZkAyTsSCERHmxs3KmBNyd/jJ72yUCj+2e1wQ5MyaQJrJZII/B7XCHGA8i5LcW8TxaVbTDqCSM/jB8xfi+vXd5U7Tp9cEUk7U8DttsFsZQ11ZyXNTyTEH6BOsRiN8jtYrc0yY4zgOydpSVjtxzJl775KFd+1nZ/T5v480fuiuFuaE45rUAGJ4Jo3Hdo8hX2INZX6Gkjk4bRbJDUSCntK6cKpa4BJjRIhREhIrQoERYa7s4q7dLDL4zBcEE1+tY86M9yotHtmtFvid2rO6IukCbBZGmBOL8RtwXnEch6hE/pkZZYBSWYnk/ev57MgcR5z7WkuTDhE/ni0gnMpjvkwZK1C5tvXkzJHPurYMmeQ6GmkARSJMOstiJRHCzVrHHJlMIOi2o93vNCUvclqilN6MjDnys7Vlzl6TGmwAwOPl9YDXYTU9goQiDxXmKCccjuMwFs1i04JmNHkcorBYYwOBeIFGAvTnKkQEIJNFUx1z+aIw6RAj/A6TdkI2H5wCx1UWXGaRyZdw609exr88sNPwsUosh0S20snIzIy5mWQebaIAZLM+Q7LjRjCjK1ktD+8aBWBumaEZVAKgK5OPgNsGl91iWilr7SLIYbPAabOY6h78/evHhX+bcU9vPRLCvz+0Bw/vHBXevxEmygJBb1P9AiDgthmamJINFlLKanRcF0MWLl3l8uYmt9208ezNgOM4447aJH8Oaif/DMOg1es0lA3TX94MWSwrzJUXszqEOdKQ6eX+uSXMZQt8Q6CAS6KUVeR23Xk8avjem5YofQPEJeT6rmWh8UONY87M5x0A/HzrAIiZxEiTEamuwrW0+12IZQqaMnGnJTK/CEac53IlyIA488qICy0HCwORS94kx1xawjFX7hqqtwnGjEzXWzFNXrsux1yTxy55TXid+sXERK6IIsvViUZOmxUOq8WQqCF1vXkNbPRNxrPwOqxVmwS16JlnDpfjURa2SjuhAd4xB+jLdQxLZBkCYvegcWGuoyxWOm1WuOwWU2JjOI7D4ckEVnT6wTCM8AzQu9lXYjnMJOuzKCsZc/qvtUrGXLVwbVbnW47j8PiecXT4nbhgaRtiGXOaEVIaQ4U5ygknXrYKk0lIxTFnUJgr/7zDakEkXTDcnW42IQ8q8uDSmxshfezim1LK+syBKQDmhfITHnxjFJF0wVCHKAKZzJHza9a1VmI5hNP5qgBkM3bOOa4+2N3szktT8SxeKi+OzQrQNYtY+XMR7woyDIOugMu064wsLGpzd8w6vwcn4th1PAqHjX/cmnGOf/rCAH77yjD++fc7cdb/fQa3/PdLhpq4jMeVHXNG3jMRAQTHnInC3ESM3wQgwlzQYzfNDfRm8Jn738C7733F0DGU3DstXgdmDJSyVhxz0gs4r9CtVPu9QhxWu0aic6qBBHlGiEtZ3TVdWVO5It71k5fxr3/ebeh3TcVzsFmYurK6SnmaTmFuLA6bhcHSjmpB1UxhLpLK44FtI0JXUq1lpmJCEovXWohQoEUMrc1qEyM4z3UIJoKgOmuOuTxaRB1fyWaRkVw1AAgnpZo/GJtPkPGltiu0mGaPQ5djTq7pAd/ds6hrUyNajlKoFY0A3jVnRPys3eQDRBupOs7vRDwrPNvkIHnUWoS54+XGD/MVSlnb/fzv1eeYI4076ruyAsZciUIpq7+6e7wZG55TiRzi2SKWdfLjJhG99B47nMqD5erHCXIeDDnmZBqDeJ1WU+au+8fjOBZK4bp13Wj1OsCa1IyQ0hgqzFFOOLVZEuSBabSUlQym68rNJOZyzlxtKavRHXNCrlhCocRJN3/Q8UCXI1soYevRaQD8w82snRWO4/C/Lx0DYM5DgUwOya42mTgYXahE0nlwXPWEzIzOS4lylk2bhDBn1kPykV1jQhbjXHvwCgHQNbvbnQGXodIpMeFUHn6nDS57xVXqd9lNOxd/KLvl3rmxF4A5zVbCqTza/U58853rcca8JmwbighZIHogjjmlUla9zq5oOg+GARa2eYT/bxbjsSyCbrtQUtnktp80WSjJXBF/3TeBbYNhQ6U9RJjrkFgYt/ocwmJcD/3TSbjsFvQE668LAHA79DkgWJYT7t9CiRMa58wFiNNJ7FIh1xdp/jAWzSBfYvHcoWljTrFyY4Lajul+lw0Mo//ZfGA8jqUdvqoxDRC7a4zfg799ZQiZQgnvOXseAP1NRviuwnnZ5gGEdh0OnpBC/pmR0kXFUlZTMubqG0uY8UyKpPOwWZgql43fYHSDcI698p9fk8ehwzFXqBN2CD6XDSwHXXmtlcYE9aKf12lDysAmQSiZQ8Blg9NWue+MiFETMRXCnA4Rf6Rc1SLu2FwLybXT55gjpaw15cImNCeYTFS75IHy5qEJ86rDNZnGRoU5uY0Bm5Uv2zfynsn4EqgV5hzmOOZIGev167uFsm9azvrmQIU5yglHsD2Xb/4mk0pZyWB6VrlL5tHpuSvMyZWyGl3EkgWTZMacieWyL/fPIFtg4bBZUGI5hAy4NMS8eHRGCGPVu0MqJlazy2RWxpywUyqanJrReSmUqN+NNhImLMUju8YE58tc69BbKWWtnuB1BV2IZ4umhLGHkrkqQRUwHohNyBVLePCNUSxp9+LSFR0AzLnfwqk8OgNOvOvsebi5LPgZcYpNSGT5EQJuOwolTnfTimi6gICLF888DivCJgpnk/EsukUT9CaPA7kia8g9+Gbx0tEQCiUOLFdxMOhBqVyv1etAKl/SfZ8MTKewqM1XJxwRiLihtVtpOJ1HvsRifXnTbLZy5kajGXzi19vwxrB64U/KMeepcQaORPmFbYnlDOVyTsezkuKOxcIg4NLnAomm8xiNZurKWAHjTjxCtlDCr14eRG+TGx+8YCEA/aWs0UwBJYmuwrUQ17g2YU7ezWXEKaYuY85YQ4Xa+zlgsHsqwD83mr3VQfTkPOgVEqVcYrU0e+xI50uqx2U+By5f99wnGBETwzJuLoCUAep/doSS+bprTW/jjmSuiGSuqJgvB1TmRlrGipEI/7zpa1ZwzPlIxpz2DVAi4DTXNO4w4h4kTMay5WZoongTl91QF2TCoQlemKt1zOldHyiPEzZDa4PatQzB6DUMVMpYOwNObJrfrLuBC0UfVJijnHBqg0LNK2Xlj7upLMwdmZwdYS6WLhje8ScPKrK7FjSp5CRVc1wxlYw544PtMwcmAQBvW9cNAJiMmSPM/fLFY2AY4JyFLSix+sUBAnmYkRBsl90Kl81i2J0p1ZnMjM5LUlk2HrsVDGNOKevAdBK7R2K4Zm03PA6roYmCHOFUXndXq1imAKuFqWo1D1QaQJiRMzeTytd3DzOplPVv+yYRTRfw7rPnmeaCBfixkYyTZiy2J+K8a1CqdIgsNPUKipFyVhDAj+1mZcxxHIfxGkeBWcLDm8GWw9PCv424HaeTOfidNqHcUgwJZNdTzprJlzAazch2ZAX0N0UYj/L37RUrO+F32mYlZ24ilsU/3PsK/rZ/Ek/vn1T9cwkJxxw5t2LHHOHBN0Z1vT+O4zCdzEk6HQF+00hPzISQL9ddL8yRRbzR+Iq/7BhFKJnHR96ySBAO9ObtKQnLYgTHnJZSVoUsuNlq/mBU6MoWSkjmivWbRSY8k8KpfF0WnNFoDCFjTlGY01aZkCznwEm52gBjAo9cmSVQKZHVi5TTUe91RhzF3Q0cc006HXN2KyM79gCi+y2u/b6OyJay6s91JEwmsujwu6rEZeKYM7pxL+eY0ztnU8qi9LvsSBhxzJWFyEBN/iBpsKE3MxIADk0mMDSTxrVru2GxVETQk2FedSpAhTnKCad2ENfbxakW8vNreoNw2S3oN9kxx3EcHnxjBFfcvQXv/O+XhEFdD4KAVn4Q+J02WC2MYXcNeQASC7kYsxxzHMdh88EpLGrz4rwlrQDMEUwGQylsPjSFK1d1Crv/RneMpXaZgm6beY45n9gxZ0ZJS1nwE02gLBYGPoc5wtEju8YAAG8/owcBlznlAGJ2Ho/ivP/3LO79+4Cun4+lC2hy1wdAk8Wg0XJWluX4xUrNxMlIho2YP20fgc3C4OaNfRVhzuA1nC2UkM6XBDHRjHLs8VhO0i0HiMvq9b1vcQe8Fq/DtIy5aLqAXJEVRFpAnM05t3d2OY7D84emhQwpIx2nSXC+FOQa0XPOyXuS68gKVBadWh1z47FKKdU5i1qweyRmmgMY4HMz/+Gnr2C47ETU8vdXhDmJ5g81wtyGviB2Ho/q+vximQIKJXmnmN7yrP1j0o0fAH2LeCkefGMEPqcN7z57HgIuGxxWi+5SViWRS4y+UlaljDn9zvNQMgeHzSLZMdRmtcDrsOp+lsq9Z7/LZjifNJzK14klQsdXnc+lmVQO/pryzVq0Om7kunoSjIiqkZT8sb1Om+6S3kKJRTRdqBNh9F5nZG7T1cAx53FYYbMwmsrTR6MZ9DS5ZZ3QAH8uvA6rruzISLoAt91aV0pvJNeRMBnPCR1ZCQG3vdztVb8YxbIc/n44hAWtHuHeM1rKqphF6TZ2P8cy/Dkm2cUEMypqDpQ3d85e2AJAvKEzt+dVpwpUmKOccIhbSRzG2uS1G17AkUGkxePAknafqRlz47EM3vuzV/HZP+wSJqRGRIJEjbONdAQyurMtCHMSXVlddiucNovh37F/PI7xWBaXr+wQJhFmBPP/70uD4DjgwxcuFJUDGBRrJYS5gMtmmmNOvBsdMChoAJVJeu1kz2tCqSXHcXhk5xjafA5csKQVAbf+TmdSZAslfO6BnciXWAzqFB6imbzgHhVDXFJGr7N4li+jqs0g8rlspjg0j0wmsKo7gDafU7gejArhlUVLdem/3kkTy3KYTOTQI9GRFTAu4Ecz+Yob2qs9a0iO2o6sgHnCw2zTP53EaDSDa9d2AZg9YY5c1zM6RBOykSXX+AEQC1YaHXMiN8j5S1pRZDlsMylnLpYp4B9/9iqOhVL4yo1rYLUwGoU5/trxiYU5O/9vMh6QzuOfunQpAOAhHa65KYVsQIC/7/RsGCk75oyPQRzH4eB4Amt6AvA5bXz3X59Dt2Ou8oyTd1wB+jPmvA6rpJvUiFNsutyQSa6LbMBt170BE5JxoPmddiRzRd35vYUSi3i2WOcOr7jPdAqJicb5gFqrYCrZsuaXsgqOOYnmDz6nDZlCSZfDX6pqghwTqMzx1UI2txuVsjIMg86AS9iEaATHcRiJZBTz5QgdARem9DjmJKoQAGO5jgBQLLEIJXN1uXtmmAx2j8YwEc/imjVdwtcCLv796p1PhBTcwPxGuP7N33i2IORZijGjORxZK5OSXuH+Tc3tedWpAhXmKCecirW8sgDnuzgZzJhLF+CwWuCyW7C0w4fxWNY04eGHm4/ipf4ZvOfsefja29cAMObmIhMMcclpk8dh2MFUWyJbixndjJ4td2O9YlWHaYJJscTiT9tHsLLLj/MXt4rKQ4yJUeR8ikskzHDMSXUmM0OIqbgJ6ifTRh0me0fjGAil8Lb1PbBZLfxEwcRS1m8+dUgo0dMt6pQdc7V0mlTKKiyCaoKrAwZdBIRIuiBMUI2WRRCIyECOS8qy9U4eZ1J55EtcQ8ecns8wWyghW2CFz7BFY9aQEhNxXhypzZgD5r4wt+UQX8Z605m9CLrtGNApzOWLLCLpgoJjjpSyah/fyL2r5JirbYqgFkGYa3LjvMW8y9qsnLmn90/i6FQSn3vrcnzwgoVo9tg1icHkmSkOyHfXCJBj0SyaPXZcuaoD7X4nHto5qnmB1cgpFnTbkcqXUChpc4HsH4ujt8ktKWr4XfZyUwkDeZTxLBK5olDuBfALTz3iL6DeMdfs4buUanXMyXULrbiutI9FSmI4wD8/9Japzcg45nwuGzgOSOscO8k9IBXbABhzzNWWx9ZCRDC147JSgwagIiZqFbv4Y0s3lAJE7jYdDSDknI5WCwO33apZRCRz6G6Zxjti1vYGcHQqqSpLNJYpIJkroq9JPl+O0OF3Cs0WtCCOrxBjtAPwdDIHjgM6/NXCnBlzqyf38lmhV68VC3Oz6Zgz5vKLZQp1ZaxApTrKSBOTo1NJWC0MFrbym3JmZXFT1EGFOcoJp/IQrjzcmz0OU7qyBj18GdzS8uKi30CWj5iJWBYOmwXfuHkd+sotx42IfrWlrADZMTcnY06q+QPAD7jGhblJ+F02nL2wxbQSw3A6j2SuiLMXtoBhGMPlFgSpUtYmNx/CWtS4ABIzI9GZrFK6aKw7GyAxSTeYhQIArx7jF8JXre4EUC6VMamU9eX+GfzixWPYtKAZfpfNgNuqILnAJAKw0euMLILqXAQmdL7NFkrIFErChoPXYYOFMXY9AOJg5bIw5zU2aSJleXLCnBGBuXZsN7O7V8UxV3nfQcERNLcnkM8fnobDasH5S1qxqM2r2zFHNgTkRIJWwTGn3fVAHHOL2tQ45vSVsnYFXFjVHUDAZV7OHHGzXbGKb7bS7HFoEibJGFjdlbX67xyNZtDb7IbNasGNG3owNJPGzuNRTe+ThKorCXPi96OGXLGEo1NJrJJwywG8SBBwGZtXkID05V1iYc5RXjRrd38olZuKsVoYtHod2jLmJDK/CHarBU6bBUmN4zHpIqsozBkoU5NzyRutGhBKOGuFOaf+uVWpHAXR2DGnsZQ1I93Vk2CkJFJ4JrmlHF36ywCnFRqN6MkHJGNkZ1D53ALA2p4gWK7illVCTUdWQlfQhWi6oHkjTd4xZ0yYmyy792pdhEY3wTmOw1/3TqAz4MQZfU3C170OK6wWRndV0XSCL/OuLekFjEfdxDNFyUxgo65EADgylcSCFo9QJlspRZ/bG56nClSYo5xwyIRB7Ixp9tiRKRhzVkQzFbcNseSaVc4aTfPlWaTkFDC2iK8tZQXMcbMpNX8QfoeBwXY6kcOukRguWd4Ou9WCgMsGl92CSR0t1sXUTiJNK2UtT8rELcaDBsPtAd55ZbMwVdZyr8PKCzGGjpuD3crUPYD9Lv1ZKJVj8+eiuyzIBNx25IosckVjbqZUrogv/GkX3HYrvnPrBjR7HLrdVvkiK+mY6/A7wTDGnZlkwV5XNmTC9VYroFksDF/iZLB7mOCYKy9aSB6l3kkTEeZ6Z8ExF61xJ5DJuhklEVLh2CdDKWs6X8SrA2Gcu7gFHocNi9u8mE7kdF1rjdxGrQYy5gZCSXQHXbKbOoCxUtZmjx3u8sLnnEWt2DMaMyU3UxCay4Jti1dbw5FjM3xJmPi6Ik0uMnm+zG0inhWOf9OZfFdkrU0gKp+dcgm5lkXhsVAKRZbDii55l2OTx5gwR5poLe+o/I5WnxP5IqvLwaS2+QPAX+dqHXMV0UjezaWng2EknW/YRdZImZpsKasJWXAA6txtRhxzkXQeLKfc+AHQnoMabeSYMyDwRNMF+Jy2umwu/rjl5gQ6zoVS2aLfqb3CYSKWg83CVG32yrG23N1631is4WuJMNerUpjj34v6eVauWEIqX5LcUBXcXLqFOVLeW58xB+gXuQ5NJjA4k8Y1a7qqcvcYhjG0DlNy1hqNuuFLWeVdn3qcwADvwh+aSWOJaHwXqjJMzqCmSGNYmMvlcli/fj0YhsHRo0frvj85OYnbb78dS5cuhdvtxuLFi/HFL34RiYR0UD7LsvjFL36Bs846C4FAAG1tbXjHO96B7du3y76HnTt34qabbkJPTw88Hg/OPPNM/OQnP5F9KMbjcfz7v/87Vq5cCbfbjb6+PnzqU5/C1NSUvpNAMUQknYffZYPNWrkczXBW8KHj/MC1tMNsYa4gDFaVCZP+QSuZLcLCVBYAAD8pyRRKhoQSMjiLnXhigm5eNNGbc0BCQs9ZxIeEMgyDroALk0Ydc4L4wJ9bMx1zDIOq0GYiphl56Mykcmj1OaoyZximLMQYuC6mE/yOf22WjddhQzJvrDlBpKYk0qzyzVePzWAkksHtly/Fwjav7okNmcRLTT7sVgu6Ai5hkqkXIsxJlQ0BxnI6KiHTotxBl3GxvSL48eeFYRg0GRDYRxs45ox0k61t7ENESnMdcxKlrHN4AvnKwAzyJRaXLG8HUHGk6XHNKXV9AyrNaLQG83Mch4HplGJHVkDsMNHumBM7Hc9f0ooSyxnubg4AY7EMXHZLlRgczRRU50btG4thYaunyjFnsTBw2S1I50uYjGdRYiul32t6Apjf4sELR0Ka3ud0g4w5PXlwgmgmKjOtO67BDb9DNZ0Lgcr4GdKxITedzMHjsCoKwIQOvxNTCXXOvHCKF42UBD89TqZpGUebGCNlarLNHwy6uOUccz4Dm1CVjqzK4pHguFEpkFc262Uy5gw8n+XKLAFjjq6QTMYcwJ9jrZ/bZDyLzoBLsUEDYW0PL8ztGVEjzPEbD33NjUtZu8vOtHEN83kyb2uZhVLWKZncPaMZc0/tnQBQXcYqPraRRi5y4w+Zb+tZH5BNa2nHnP78RQAYmkmhxHJYJhLmHDYLfE7jkT8UdRgW5u666y7s2bNH8nvj4+M499xzcc8998Dj8eD6669HqVTCN7/5TVx44YWIx+ttt5/61Kfw0Y9+FAMDA7jyyiuxYsUKPPzwwzj//PPxt7/9re71zz33HM477zw88sgjWL58Oa666ir09/fjU5/6FD784Q/XvT6RSOCyyy7D17/+dRSLRbztbW+Dz+fDT37yE2zcuBEjIyNGT8kpC8dx+M3Lgzg40dgurYVIulBnWTfqrOA4DrFMHsHyg31Bqxc2C2OeMFcukwVgSv5ZKl8UgpQJZgSaCqWsEl1Zye8ospzmUiTCgBAQXhnEOwMuw9lfgjBXfqiZ5ZiLZQoIuu1Vk52m8rGNPHRmkvm6nDKgsnOul1BSukxEyJvR+bkBfLkwX9pU7gRsUgZaKMGfx1Xd/MJN78SGNDOQm0TPa/HgeERd4LEcM0I3XWl3ghFXYkQiO9NoJy6gPmMO4Es49TZ/GIvy96rcDrqRcShW65jz6Hdw1TIRy8LrsFaJ7CeDY47ky126oizMtZsgzMnlaDn4Bj/ELaOWiXgW6XxJMV8OqGSvqck2IrAsh8lYDj0iQXVluSxyeMZ41MR4LIueJrfwLG32OsBx6sb3VK6IY6EU1pQXumI8Dhsy+ZLgyCOlYAzDYE1PAEMzKU0O/ykVGXOARmGuPL9Z2iH/uQUN5vcemUyg3e+sEniEJiM67utGeW1i2v28M0/NXEtNiaxXR3dzNZl4RjKviMAjnwWnb2wLl8eAlrqurPrFkhmVjTsqzR9UOuYy1ZtPtRjrylrfmZZQ6WipfV6ldL3puc7GY9k6Z5gc7X4nugIu7B0zu5SVfw3Jc1WDVDQRwUiuI/8+pIU5QeTSOdd+au8Emj12nFPuQlp1bJ2RQo3yX4khQM8YQX4mIGG4MNJtGpB/hgTd2rJaKfoxJMxt3rwZ3/3ud2W//+lPfxpDQ0O46667sHv3bvzpT3/CkSNH8K53vQt79uzBl7/85arXP/LII7j33nuxbt06HDlyBH/5y1/w4osv4k9/+hNYlsWHPvQhpNOVhVgul8N73/teFItFPPbYY9iyZQseeughHDp0CCtXrsSvfvUrPPjgg1W/4z//8z+xY8cOfOADH8DBgwfxxz/+Efv378dnP/tZjI6O4tOf/rSRU3JK8/zhaXz54X343B92GXLq1BJN5+t28bRmUtSSzpdQKHHCgtButWBBqwdHp6SdmlpgWU4oZQXMcXMls8W6clOyyDRSaqqmlBXQ7zAhmX1iZ0VX0IVYRnsuhZgwCSr2VAfnG3VzRdOFul2mYPkBaaS8LpTMSZZzGBFiOI7ju79JZZaY0HkpXJ6gkgUscaYZPcfhGpdU0G1HIqc9w08og5TYFQSAec0eRNMFQ8L1jEzzB6PuBKC+lBUwtvsqHLemlBXgz5GRUlYLA3TOwgSyErRNHHPGxnUxE/EsuoKuqs2MgAF335vF3w9Po7fJLYhexDE3oCP/tJFIwDB8LpdWIbR/qnHjBwDwlB3eWoKm+WYjbJXTsVOHM0MKjuMwHs0IZaZApXRPzTV3cCIBjgNW99RntLntVqQLRUmH6fJOP1hOmyNfKX8IgLCpqOX5f3QqAYZR/tyayk0l8jrcXCzL4fBkEitqHHnk+tPjmAsl84ruM6nfo6acNaSQ+UXQ00RJlTBnoLRuJil9XRidZ4bL85tawc9ps8Jhteg67rSwsaX8+Xkc/O9QKwjXdh6vxWfAFRQRVdLIHtdASbbkfK18naldNxVKLGZS9d1HlVjbG8SRyUTDefdoNAObhWnY7RWolPNrGZelNg4JJNdRfykryZirPsdGNg8HQykcnEjgras7q6q2CHodxkL+a0PHnPZzIZWVTTCa43dURphr9tppV9Y3Cd3CXDQaxYc+9CEsW7YMXV319s+jR4/ioYcewrx58/CVr3xF+LrD4cC9996LQCCAn/70p0gmKxOZb3/72wCAb33rW2htbRW+/s53vhPvfe97MT4+jt///vfC1++77z6Mj4/j1ltvxbXXXit8vbu7Gz/+8Y8BoEo4jMfjuPfee+HxePC9730PNht/AVssFnzrW9/C4sWL8cgjj6C/v1/vaTll4TgO33/2CAA+YPTp/ZOmHTsiErkIRktZoxID17IOP4bDacMZWolcESxXsdmTLDEjbq5ErlhXbhowKJoBlcGZBILWIpTL6FzUD4SS8Dis6ArUL7KM5H/VllmaVcoaz0gIc+Xzrl8ELiKdLym0RNcZ7potIl9kpUsjzBCOUvmqvBkj1vra4wIVsYs4S7VOQKI1ok4t88tNV46H9bvmyOSpdvwR3AmGSlmrBUqg3K0vVwSrsqxOirDEeWn2OBBL6ytJD6fzCNRECYhx262wWxlDzR+aazLmzHLM1XasIw5Qve7B2abEchgOp7GuNygIiqTzmS7HnELXN0Krjo6ZAyF+XtaolNVmtcBhs2hy7pK8IrGwZVYzl3imiFS+VJUP1yy4NBtfv/vLGU1SwpzHYUU6X5IU5laUHX+HJ9Vv/E01cIrpcsxNJjG/xSMr9uk9LmE0mkGmUBIyewlkrA9pbDLC58DJl3vVQha5WoQ5JTeXz2nTPMZPqcjEq5xjfd095VzygP4Ft1xXVkB/Zm1lY0vZMccwDJo8dmHTrhGRdB5Om0X2Otbr8qs0ZJJ+v6SyRF8paw5+p7TQ7nfaUGQ51aXNfLk20BVo7GojrOsNoshyODihPAaNRDLobnLBqqJEtlvHuFybK1uLz6k/H3kynoXHYa0zGhjZPHxqH1/Geu3absnvB936IoUaCfhGNhHJHF0q5sVo8wcizNVu7ujNiqZoR7cwd9ttt2FsbAy//vWv4XTWX3hPPvkkOI7D9ddfLwhghGAwiMsuuwyZTAabN28GAMRiMbz44ovw+Xy44oor6o530003AQAee+wx4WuPP/44AOAd73hH3esvueQSNDc3Y+vWrYhGowCA559/HqlUChdffDGam5urXm+1WnHDDTdUHZdS4cWjM3hjOIpr1nTBabPg+88eMcU1l8mXkC2w8qWsOgWjmITbZn6rBywHjEeNTf6FY4synnxO7RkSYlI5CcecR/uOudRxAfmurBXHnL6F7MB0CovavFWuFTM6s9buulXKhc0pZRVDHHN6HzpKk1MjQoxSaYQZGWgzqXxVqUillNWY+EnKmcix9S4ESWfNoMwEb34rP2k1IsyFkvymQK0oZSR3hxCRmKAGXHZwnHHBz18TXh308JlGekqbY+mCIE5LQQKQ9dx75DMnmxhCSZNBYS6RLSCZK0o6Cpo8jjlbyirkXvkrY4XXaUNXwKVLmJuK52Bh6h2fYlq8DsyktHXM7JeZnEvhdVg1NX8YE3VkJficNvhdNsOOOXLsbpFopkUM3lcuBVsjI8yJS1l7mirvn+StHZ7U5piTy5cDRM9mlddyocTiWChVlQ0kRSW7Tvs9KHRkrXHMketZa5YhuR/Ul7Ly51xNZ1YSqaCYMee0IV9kNbkHh8rl1gta5TO6jGxyzSSlG1YYLWWtfS6L8bn0OfvJxpaSK5HQrGFcjkrE24hx2iywWRjNc24h/0xGSDTSlTWUzMmeB6/GjdQJMkaq6MhKWNvLj1l7R5Vz5kYiafQ1Nc6XA/hNHZuFMc0xB+jLdSRMxXPoDLjqMpeNbDY8s38SPqcNFyxtlfy+3mPLdVcmGBkjyHtRbP6gsSET4chUEr1N7ro1Y5PHgWSuqMtpTdGGLmHu/vvvx/33348vfelLOPfccyVfs2/fPgDA2rVrJb+/evVqABDy6Q4cOACWZbFy5co6IU/q9Y1+h8ViwcqVK8FxnPA6re+JwsO75Q7DZmHw729bhfedtwD7xuJ45oDxZhkRme5L5P9HdS7gpPKpyO4PmbzrRardut9lNySSJLPFuoGwpTyB0roLXXXcXAk2CwOnRAcqwFiwezpfxHgsi8U1izey4DKSMxeumUS67Xz3PiPiZ6HEIpUvSQhzxsrrKp096x/AQbd+IUaxNMJgwGuxxCKWKVQt6I12tiJEUnk4rBbhPeoX5uTt+kDFMTdsQJgLp/KSn5vR8wtIOxQE96CBncdwSqr0X3/Tg2gmLyz65AjoLOcgAhzZxBBCwA0KZ0T075IoxzHadXI2kRPbF7d7cSyU0rzZNZ3MocXrVHQ/tPocyBa0ibYDoRTcdqvk+a3F47Dpcsx1N1UfuztoPJt0PEY6DFeOrUWY2z8eR5vPiQ6JTqnusmNuLJqFw2ap6pa4sNUDh9Wi2jGXK5YQyxRkO7IC2ps/DM3wHVmXdsg3fgC0C35iDk/JCHM+fY45LR1ZAZ2lrEoZczqEmP7pFPwum6JLTK+Dp1hiEU5L58oajfOIpPLwOW1w2iQcXTrFErIpqaZzaJNHfUaVUoMGoLwZ7rIhmdN2fuXWGwQjpawhGUEV0L6ROhEjJZvqS1nX9fK5mErCXCxTQCJbVJUvB/AO9M6AS6Njrr5SQIyevD3CRFw6d8/rsMHC6Ju7Ds6ksaLLL3lfAJVrReu93Ngxp38jnPxMwFV/HRsRl0ssh4HpZFVHVkJTAxNHOl/U5BinyKNZmDt+/Dhuu+02bNy4Ef/xH/8h+7qxsTEAfFmpFOTrk5OTul7/Zv0OKXK5HOLxeNV/uZx+8WSu8/LADF4fjOCWTX3oa/bgk5cshtNmwfeeOWzYNVfbtY8glJ/oFEuIyywoOi4pezLqmCOLX/HD3e+y6d7JLLEcUvlS3eJ4XrlrkpHOk6kcL/jV7jARghon/2JIJtLitupyJ7LLNxXXf0+EayaRDMMYOseAvNDTRDLmdC7m5RoIAMYs9koLC6Pdrcg1LN49D5jgEgMqTjxyzekV5hpmzJFSVgMNIGaSOckFlhml09KlrMbcmUC59L/mPVeaHmgfL3kXaQNhTmc32WimAKuFETL7nDa+DMVoxpxUR1aC3i7AYjiOw/efOaKqy50W5O7pRW1eJHNFVU4gMWqC81t1lA/3TyWxuN2rqiMgKfFUi+BqqylD7gq6MR7LGJpTkEYm4mO3qMyYK5RYHJxISLrlgOrmDz3B6m6JNqsFi9u9gqOsEY266QLax03SkbWxY86h6bhiDpf/vtpS1maPAxZGuzAXUlGKLUaLMDc4kwLDAB0KAfp6nqN8t2Kf7JwK0J8fFU7nwXHScwmf09gzqdYhX31sfRUfoWQOdisjzHOUaPHypXCNuiMXSizGopmGXUO9DpvmJg1y6w1CpfmDtnNRLLGIyAiqgPZAfrJBUTtGKtERcKHd78TeMfln1mh5LSHX6EmKrqBLo2NOuvsvwefUnusI8GXIsUxBUqy0WBhdm4fFcpafmc5lQkNhzoBjrlLKqtT8QXv1xGgkg1yRxVIJpzyJI5E7D/c8dxRv+8FW2rnVBBqPpiI4jsMHP/hBZDIZ/PrXv4bdLr+jkUrxi3aPR3pwdbv5gYFkzKl9PXndbP4Oce6dFN/4xjeqcvMA4Itf/CLuvPNOxZ87mYhEIsK/737qAKwM8I9ntiEcDsMG4OYNHbh/+wQeer0flyyt72SjluFJ/iHiQAHhcFj4OlcOMJ2MJKu+rpbRUBQAYC1mhZ/3MfyA0T8RRjiszsoteewp/tzY2JxwbJcNmIzndb1X4qayo1T18x7w5+DoRETXcQEgls7CbWdkf54p8A/qsVBM8+/YfSwEAOjyoOpnnSz/EB+ciup+39PxNIIua/X5sFsQTeZ0H3N4hv9bnUz1eS5l+MXGVFTftTY0yV8PLq7+87dz/Gc7MjkDL7QJwsPl4zq5+r+ZzfN/y2Q4inBYOd9FimMhXsxyW1jh2GyufMxIQvc5BoBQIoOAs/LZWUv8BGVsOoJwm3z2US2TEX4c5nIphMP1D3srx8Fls6B/Uvu1CwBFlkMkXYDfgbqfJ4uHcCKt+1xMxdJw2SxIJ2Ig0qGV5Sc1o1Nh9Li1T5w4js9kWtLqqnpfdvDHHZ4Mo9ul/ri5IotsgYVLdB1I4bHxmx1az8V0PI2Ay1r1PAm6rJiOZQxdY0fHZgAAPkux7jgeK4dkrojJ6RDsMrl5DY8/ncZ3nzmMbcem8f13rtT9PmsZmuDfqxPVY0Wnh1/k7xqYwKZ50sJQLRzHYSqexZnz/IrnMmjnr+VXDo/Cu1y6XEdMJl/CWCyLdd1eVZ+Rw8JhJqX+2hie4stFnaU0wuGKwNLiZJAtsBgcm5J1yTZiYIK/zjxMZcxkCuXxZyau+B53D04iX2SxuMUh+TorV0K+xGJoJoW13b661yxqduLJAwkMj0/JNlsibO/n32fQLn/fcRwHm4XBdCyl6tzuGeK7/Xa4lO9la4l/Do1ORxHu1HaeD4xF0eV3oJhOoNao3OS2YyKqbbwcLN8PLqibO9nK88LRBp9lrsji74ensb7Hj2wyLvvktZT458rolLrncyJXRCiZw7kLlO85Ls+fnMmw8vusZaDcdMVrrf8Mi2Xxeyau7nqoZSaRRYdf+tp2WTgkswXMzMwIgqN4zBbDchwY8JulE9E0mt122deKcVtZcBwwND4lu9kGAEPhDAolDr1+q+Lf6bYziKW1zQdHynN3Oyt9vRXKokIopm0uOJ3kBVWfnZP8OXKdjU2re+4PCnPKrKb3saLdjVcGY5iYClVFXRAOHueP1exQHiPEtLgs2J7MqX6eTkbLmxO5JMLhelOBw8IimdM+lxgpb7oEZM6xz2FBOKntfE0n+M8tIDEHBPh7wFaes41MRbBY3aMZAHA8xD/n7MUMwuH6z7xQboYWUjm+i5mY4Y/N5OvnURzHwcoAER1z1x3l51KPr37N6AC/lhmemEGbvV6c23s8jHyJxbGxaSxoUS/8nk60tKjTSjQJc3fffTeee+45fPvb38aaNWsUX2u1VpwuSrAsq/n1HMeBYRhYrVawLDsrv0OJL33pS/jc5z5X9TWn0ymZtXcy09LSgkMTCWw7Hsc7N/Zh/eIe4XufvdqDP++awl/2zOCmc5bq/h2lEX6w7WtvqrtoXXYL0iVG9cUspsDwA0xfRwtaWpoAACsdXgB7EM3rO6ZwbEu8/J5bhOO0+NzYP57Sddxc2UHQGvDV/XyH34mJZEn3+82WgIDbIfvz80u8qFNg7Jp/x2SGF+bWLexES0tQ+LovwN8/0bz6gaiWeI5FR8Bd9fNBjxOpXFH3MQeT/DXR1eKvO4bfZUO6qO+6yIJ/gC3sbhWuNUJnS7l9vcOj+dgpli8VX9zTjpaW6h2s7jZ+sc1Znbre8+Eo//O9rQHh54t2/l4swGbo/ohlS1jQVrmWe9v566Go8b1m2UEAwILudtnGBPNaPJhI6LsmphLlXenm+usB4LOzsqz+sSJV4NDirb73ulv5e52zu3UdN50vIlfk0NHkrfr53jb+b2GtLk3HnSrvzLcFlK/PtoAH2WIU3kBQtuRDimSeQ4u3+nNv87swncgZusYSRV6YW9bXVjX2AEBHkw/ADKxuP1pUlsjVMjLIL6y3Dcc1/81KZBEFACzqaq36+9cuKAAYwkzOovq8TMSyyBZZLOkMKv7MLed68N0tQ3hkXxi3nres4XFJKdSqvhZV7yXgcWE0nlf9vmcyLJo9dvR0tld9fUFHCMAUshY3FrVoWAGJiOSGAQCr5ncJJT1uH78oSpeUz+34Pl7Y2rS4Q/J1TT5+sZEpsFjYHqh7zdr5rXjyQAihgh3zu5vrfl7Mw4/0w8IAN5+zGC0t8puETR47MiV1z9GRxBAA4MylPYrC4LwO/n8LFvl5gRQllsOxcBbnL26V/LmOgAuxrLa5Sprjn8mLulvR0qJ8zgCgBbxDM9ZgbrHl0BQyBRbXrOtVHteaogAAm8ur6n0PH+dfv6pX+d6Yx/GungKj7VlamOGvVan5cDPHwcIAOVb9GEHgOA7RbBFreqXHiha/ByUuArc/CI+jcu3UvnYsmsGVdz+PVd0BfObypYhmS2gPqHvmdDVPAZgCHN66+YyY7RN8xdKa+W2Kx23yODE4k9Z2fi28aNTX0Sz5cy4fLz4UNX5u41l+zOxrlT6/Hc3884RROQ+M5vl7efm8TsVGLrVsXNSOrQNRTOftWNcRrPt+tMC/zxV97ar/vgXtAeDQDPJWDzoVxipCqshH5/TWjO+EZr8HuWIU/mCTpo2z/jg/z17QUX9vAECz14VwSv1zCABG0/z5mN8u/wztyfKiaknj/DVROAYAWNzXIft3ehxWZHXczwWGb1jR19kq+fzwuezIc9qPO5nhx+MNizrrfranjb+GSzbp+30qxY9ddrevbk5G0Ybqu2LPnj34t3/7N1x88cX47Gc/2/D1Ph8/8GYy0mV45OvkdWpf7/VWguZn63eQ18nhdDoRCASq/jvVRDkCyWw5e2H1pKkj4MKqLr/q0g05Igpt0Zs9Dt0h4VIZc61eBxw2C8ajxjLmhFbuNcH5+RLbsFW5FCTHyifROXV+i8dQhhYpZZXDSGjqQDmsvLZzn8NmQavXgUmdQd4cx2EmlUeLRKdMQ6WsaelSVqB8rekuZZXPmBNKWXW8bxJeLVXuZCQLBRBn+NV3ZTVSvpkvskhki1X3s3CNabS4k+wzOVEO4O+PkUi6YXmMFGEhG9D8kGKAL0uq7ShrJNMREAUr1xy3kt2m9RyXSyIaZMxV3rf2sO26mAKvQ3dEAWEiLl0OCYjySQ3kzO0d5QX1TKGEbYON3SBqmZYtZeXnHFoaQJDXkq6ucnQFXbhqdSdeOBLCoIrj90+TjqyNGz8AfBe4tIaymfF4RvJz09MBsJaxaAZBt73qmed2WOG2WxuW8h6a5M/Nmh7pRYXHUXk+izuyElaUc9eONMjZGZpJ4blDU7hiVadQji9HwK0+L5GEdjdy61U6sWu7B4dmUsgXWaEDbS1tPqfm5g8hjRlzAF8aNtUgi/Dp/by489bVnYqv01rKOqDy3tCbH0XmElKdZEmTMT3PpFS+hHyRRYtMFpxwHho8+184Mo10voTtQxF86JevYySSkZz3SCE0/mlwPZPxZ0mDjtC6MuYk4iXEuO1WWBjtpazkuhc39RFTyZhT934nY1m0eB2aRDkAWFsuw5crZyUdpdVmzAGijtkq8z8jqbxs4wdAe1kvgTwX5HL3Am6b5nnVZPlvMrs7NgAMzaTRE3Qpio8Bl13XXFCI5VHIStRTLkw6skqXspZzjGWeG6PlSBk9Dcgo1agW5r70pS8hl8vBYrHgAx/4AN73vvcJ/4VCvHPm85//PN73vvfhwIED6O3tBQBMTExIHm98fBxAJddN6+v1/Iye33G6Q+rUfRILt4VtXkwlcrrbMgOicHCJAabJgFhCBjtxgwaGYdCtMS9BCunmD/qDeUkpq9Q5nt/iQTiV1y1IJbP13V7FBAw0PhiYTqI76KraYSV0BFyYTOg7z+nyJLI2oyLg4nNQ9GYQVTLmpERgu+5sBCFjTqYrK6A/Y85htUjmSFQWFPoegoIoJZqou+xWOGwWQ40JyDkUnwsjzR+UAqAB3jFXKHG6QuOVBFWg3NDFgEgZTRXqJqhGhFoAiMjkt5CxSG+OXyNhLqCjczHHcYhKhHi3ePhmBBkDE7jxGB/A3yxxfVSuN/3i376xGIix/vnD07qPU4vQKbJmIdDX7IbNwqB/Wr0wR7pDLmpTXsACwPvOWwAAuO/VoYavJdmhjRbGBLeD35QipTlKsCyHiVhWEOHEaF0ASjEuc+wWr6PhM+7QZApehxULZMQyt+g51yslzJUFq0MTylEov3l5CBwHfPD8hYqvA/jyUDX3XInl0D+dxNIG+XJA5fmntVEMCfaWy7Br9fFd+7RsTk5rzJgD+I0qpSw7juPwzIFJLGrzNryGvRo3uIRc3UaikVNftmxIyKuVfybpmWNGhE6ZMgt5oeu98rFfO8ZvUjx6+1vwwfMXwGGzYF2vOndrZcNE+T4kHaEXtylfyz6nDdkCi6KKcYcg1SldDMMw8Dq1Z9c1Epj9Tm3ztfF4RlPjB8K6Pn5TYY9MA4iRSBpWC6OqqQ9ByOZWuW6KpAt1G5JiiAFB63VMRDSpXFmAf+4nckVNm7RTicZNNvRkcnIch8FQCgsbPJsDbn3dkOOZIhgG8EmsvQB+s0yPgH9kKok2n0MyH1CpcVciWxDGDi0d2inSqBbmSO7ali1bcN9991X9R7LbHn74Ydx3332YnJwUOp/u379f8nikQ+q6desAAKtWrYLFYsHBgwclS0lrXw9A8XewLIuDBw+CYRih7Fbre6JUdnikxB2yUz84o34xUYtSGGuLV30Xp1qi6QIYBnUNFbqDLowZdMyRAbq6+QNxG2kfZImwScJ9xQgB9xJZDY1gy00lvDKDNwDYrRbMb/Hg8KTyYkLq2HwIsvSDpyvgxGQ8p0tEq4hG1deE32VHkeWQLehr163U5TPoceh22ISSfKMKqd3NgAFH4nQyhzafQ7L0vrKg0CvwVHe9JQRc+iYKBOKEapboRKpHNGqUNVW5P7S7SoUgfoWQYr3uwUKJRSJXrFsAGBFqAdH5lXHMaRWXhXtCpWNOy2eYypdQZLm6SXqTwcY+AARxR+reaBJ2dvWdY5blsH8sjrMWNKPd78Tzh0wU5pI5uOwWeB3VYwUZh4+F1I/Dx8rP3UaTfwC4YEkrFrd78cftIw2FE+JYUSP4ARD+FjU75TOpPAolrq4jK6B9AVgLEf2k3GzNXrsgxEvBcRwOTaWwqjsg2/BC7JiTCk/vbXLD47AqdqZL54t4YNtxLO3w4cKljfP+eprcmE7mGjY7OB5OI19kGzZ+APSHmZM5gpJjDlDXmIEQSubgl3l2ytHud2ImlZcVZPaOxjEZz+HKVR0NY2u0uncGQkkwTGOXqs1qQYffiaGwtrmx4LySFeb0VQ1IOeSrj8tfE40W89uGwljU5sW6viC+8va12P+Vq/H5q1aoeg9aHHOtXmlxQIyekPuIxPxE6rgJzY45ZWHOp9KRCPBj0WQ8hy6FpiVydAVcaPM5ZDuzjkQy6Aq4FKsQ6o4pOJnVrUF4x5z8vE3oGqpRwBFENJlO1gEdazASZ6Km+YOWuc90ModUvtRYmHPZ9XVlzRYQcNlln1V6xGWO49A/lcQSGTdws8K8alS0pjay4UrhUX13btmyBRzHSf63YAG/G3vkyBFwHIdLL70U11xzDRiGwWOPPYZSqfqDisVieO655+DxeHDJJZcAgPDvWCyGLVu21P3+Bx98EABw3XXXCV+79tprAQAPPfSQ5PuNRCK44IIL0NTUBAC46KKL4PV6sWXLFsRi1QNXqVTCo48+CoZhcM0116g9Lac8ZGFaK3ABlYn7YEh/qWVUoZS1yeNAIltUtRMvddygu37g6gm6Ec8WDbn8ouk8XHZL1WTSiGOOPKy9MqWsAHSVs6bLCzClUlYAWNMTQP90UnGnI5LK46m944LQNhHPIlMoyQ7iXUEX8kVWl+NRbhLpN9g1NKpYympHplDSVYocSuZkyyH1dmcD+F3YWmcNQShlNdChDah2zAH6JwqEcJLszFfOh6/cyl6zYy5dqHKlSmHk/piReK9i/AZKWeU2HISSUJ2fm5zzQWk3Uwki5PldygvjyvtWf3zBDV1zv5H3rjemAODHH7ld/yadwgPheCSNRK6Itb1BXLSsDYcmE0Kkg1FCyRzafE5JwWBRmxfD4bTqMWgwlIKFqXTvVoJhGLz33AWIpgt4bPe44msHplNlkUldBLG7LFipmZCPy3RkBbQvAGsJpXLIl1gZx5xTcZNvJJJBIleS7cgKNC5ltVgYLOvw4ZCCMPfgG6OIZ4v44PkLGopGAHDlqk5wXKU0U44jZZdRbbdUKRw2XhjW6pgjf5ecK4+IEjMa7ms1XYVrafc7wXHyXYaf3s9XxLx1dVfDY5HnqNp5G7k31AiJq3sCODyZ1DR/rQg85j6T5DY7CZXzIH9NTMWzGJpJ46wFlVgbm9Wi6joGKpuASptHHMehfzolO6+UfM8aNiej6QLsVqZuY0SMV0cZoNBdWEaY07KRGkkXkC+y6NLQkZXAMAzW9zXhwHhccjweiWQ0lbEClYgBNRsmlQ1J80tZiWNOrsuynrgNIvYpCXNNOjaWyZp4UQMBP+C269oIj2UKip2Q9ZSyTiVySOSKsuO70uYv6fYL0FJWM9DXskwFCxYswA033IBjx47hzjvvFBb0+Xwen/zkJ5FIJPDJT34SwWAlz+Mzn/kMAOD222+vKjf9y1/+gt/97nfo7u7G+9//fuHrN998M3p6evC73/0Of/nLX4SvT0xM4PbbbweAqk6pHo8HH/3oR4Xfnc/zFxjHcbjzzjtx7NgxvOMd78Dy5ctn4YycnJBJgJS4Q3YDjDrmnDaLMLkX06g9sxLRTEGy8xPZqTey0IpICAZ+A/lcZHdOSvyc36rfEVRx4ilPItf0BMBxwIFx+QXFPc8dxT/9dgeeO8Q3JBBKOmR2hIg1fFJHWRJx0tTmaPlVllvIoZTLoLQb1IiZVF52wqs3U4zjOISSedkdWKfNAruVMSwc1Tq6/G67oRw/4bMTnQ89rezJBE8uQ4Mw34BjrpIxJ+9OSGosjSBUcihrS1n1OygBkWhdc2/4nDbYLIzm61erY07LdSx3Dsj/1+uGzuRLiKYLkgIMIJpA6jzH+8b4fLk1PUFcspwPsP67SeWsRJiT4oKlbSiUOPx86zFVxxoMpdHX7JHsvifFLRv74LJb8NtX5MtZWZbDQCjZsFRPDHFkq3FAjJU760l9dgGXDR6HVbdjbrx8bCnRrMVjRzovv/Ei/szlEM9R5K695Z1+TCdykqIRx3H41UuD8DttuHljn/wfIuLyVR2wWxk8tU86foVwZIqIZtJutlqaPA7NGXNHJhOY3+KRFWyJmBTS5JjLy24+yUHED7KojmcLyBUrn+vTB6bQ7LFj4/ymhsciTiY17hKW5XAslFKdvbiqO4B8kRXmSmqYSebgsFlk40f8Lruu+Y/cc6Ny3MYbfa+XszbPXqSvaY/glFYQbmdSecQyBSzpaDz++DTmAwL8Bl5vk1tRTNQjzI3FlEUjvwZ3H1mbaCk3FXP2whYUShzeOF6djZrIFhDLFNCnYiNHTLvfCQujLvszIjN3F6NVDCdMxLIIuu2yorieudVUPAebhZG9L4BKxIuWagSS5drYMWdDvqg9mzyeVa4m8TpsSOa1xf4cKTui5YS5gMsOCyM9bxM75tI6zA2UamZNmAOAH/3oR+jt7cXdd9+N1atX49Zbb8WyZcvwhz/8AZs2bcJXv/rVqtffdNNNeP/7348DBw5gxYoVuOmmm3DRRRfhlltugc1mw+9+97uqJgs+nw8///nPYbFYcMstt+Ciiy7CTTfdhBUrVuDAgQP45Cc/iRtvvLHqd3zta1/D2rVr8Yc//AHLly/HrbfeitWrV+Puu+/GwoULcc8998zmKTnpqDQmkHDMlXcDtARW1xJJ1+cwEVoahE0qEUvnJQcuslNPFgh6kMq+ChhwcymVshpxBCmJqmLW9PKLkf0ygbEA8FI/3wXxF1sHAfAlHYB8CDIR5vTkBUm5rgBj5cKAqARZqpRVZ9Yey3IIp/Ky4o7eTLF4poh8iZXdgSVZKEaaP3gd1rpJDl/KasAxJ9OcIOi2I6ZhJzOu8FmJmVduy67LMZdSdif4dJZcAOKFUPX7d9oscFj15/hFJIRPgL8emnTkJJJ7Qmn3Fag4P7VMeqUa8ACVa6NRGL8cE0LWjPTOv95wewIpA1rbG8BFy9rBMMDfD4d0HUsMx3GYSeZlr7f3nTcfC1s9uOe5ow0XQSzLYXCmcYaNmKDHjhs39GDn8ahsqdN4PItsgVXlWCFoccwRN5xUThDDMOgKunQ3fyAL2h6JMlkiBstdc/vHeWFutQrHXJtPPpSdlHlKlbO+eiyMw5NJ3HJWX8NnMiHgsuPCpW146WhIaF4EAMUSi1cHZoRNg6MNFlV1x3XbNQnXRGBaruDII4KzUv6bmEKJRTiVl33GyUEcdtPJHF46GsKF/7UZl3/7ebx4NISRSBoHxuO4bGWHqnI9siBXU3I6Gs0gV2RlNyNrWd3NX0sHyteWGkJJ/nzICUf+8kJeLESqoVGjIzWNn14f5Ltinr1QnzAnVcq69Uioatwg+XJaHHNqqwaKJRZDM42FVb+OedVIJIMOv1N2XPBpqKipZKnpayh4Tlk4fe1YuOrrRDyRKsNXwm61oM3nVLVhQn6n0jikRQwXM5XIKYqVAR2u/qlElhceZUpCCUGNG8vHhPzXxs19AO3rg1i6INyzUnidNnCcNvcaidGQu/csFgZBt12yKkPsmMvQjDnDzKowN2/ePLz22mv42Mc+hlgshkcffRQulwt33XUXNm/eLNn99H//93/xwx/+EAsXLsRTTz2Fo0eP4sYbb8Qrr7yCSy+9tO7111xzDV588UVcd9112Lt3L5555hksXboUP/vZz/DjH/+47vWBQAAvvPAC/uVf/gUA8OijjyKfz+O2227Dyy+/TBs/1EAeUH4J0SjosaPZY1fV6U2OqETnQoKaHTbZ42YKCEoct8cUx1x9oPlslbK2+5xw2iy6hIeUWmGOdHIalZ5ARtN5HJjgv7f1aAiHJhINQ5DJA1RPZ1Y58cHIOQb4MHibhakqSSLodWfGMgWUWE52se22W2GzMJrLQ4XujTJdvgDo7tAG8PdUi8R7Drjsul1i5LhAvUsq6LZrEkrIorFR8wePw4Y2n1PX/RFK8teD3ATHiAs2KlPKyjCM7sBfQDkrKKhxsc2/z7Iw12CM6C67kLRswghB2zXuYnJf7xmR3whQolIOKRcCrS/cnrBvLA6HzYIl7T60eB1Y39eEF45MC5lWJZbTFDhOiGUKKLKcggvWin+/fjXS+RK++dRB4evPHpjEO//7pSqxZyKeRa7IYlGrNvfDLZvmAQA2H5yS/L4QvK7JMcePp2pcJmRx1yMjqnYF9AtzFTde/bFbGwhzB8fjsDLKpaBuO3+PSDV+ICxX6Mz68M4xAMA/njNf9ueluGZNF4osh2cPVspZ7376MN597yv4xK+3IZUr4shUEp0BZ8NMTkKThm6vAF8VUWQ54e+TQmspK/ks9JSyAsBvXx7CB3/5GsDxc4b3/uxVfPI32wEAVzXoxkroDLiwvi+Ip/ZONLx+SRd6tU1RVpWFuf2ahDn5WAxAf4SFXDapcFwytyqfg53Ho/jaU/1V8SavD4bR5nNiocYxhxB028EwlWfjC0em8b6fv4r/3nJUeE2/0HimsTBHztMD246rmq+MRDIolLiGwqrXaUUqp81tNBpJK5aIkm6vakpZJ2L83E9PKSsArOsNwmW3CEIqYSSsvSMroTvoUlX98uiuMTAMcO1a+TJyPfnIfO5eVtaRCFTMEVodc0plrARxE56pRBZvv+dFxaZQQsxEo67bOuaYLMvx1SQKYz2pktLi/BwtPz+Vro9mj6Nqg4gwEqWlrGZiijA3ODgIjuOwdOnSuu/19PTgpz/9KcbGxpDNZnHo0CF8/etfRyAgvTNpsVhw++23Y9euXchkMhgfH8dDDz2EM844Q/b3n3322XjssccQiUSQSCSwfft2fPSjH4XFIv3nNTU14dvf/jYGBweRzWbR39+Pe+65B11djTMpTjcSOXnRCOCtukZKWcOpvGRnPaCSSaE1NylfZJHOl6RLWQ065liW4x1zMqWsehbcSuKnxcJgXotHV6leUqFEVkyH34UOvxP7xqUXyq8dC4PjgHeWy29++eIx9E8n4bJbZBdYhhxzqUaOOf2lrPzksH53jAgdWh1HFdeVvLNNT47EdIMuX0BZmDOQVSZVbkCcU0aOC9Rn2Wjdcay4G5Uz5gDeNaenOcpMModmr0N2t9RIjl9EpowTgOay3urjSue2AfykSW8pa6OurAtbPWjxOrB9KKL4uqpjp6VdgxvmNWFVdwA/23oMj+4a0/R+gUpJjVwnNb3h9gC/ANg3FsOqLj/sZcfNJcvbEc8WsWM4ij+8PowL/utZ3Po/L2s+dqOAcAC4YlUHLl7ejr+8MYrtQ2Hc/fRhfPRX27B9KIK/7BgVXqe2VKaWDfOCcNjqF22EgWn1jhUCKW1UMyEnE3i5znpdQRcSuaIuVzRp6iT1TGpUPn08kkFnwAmnTT72gWzoSJXKEoTOrDXCXLHE4q/7JrC804dlCuKWFG9d3QkLAzy1ly9nPR5O42dbj8Ftt+LZg1N41/+8jKNTSSxTWcYK8Bse8WwBrMoNmEMT/N+jKMyVN5HUNn+oPOMaj/FiOsrh788enEKH34U/33YBnvrni3HuohZeVLdacNGydtXHu/WseUjnS3hiT6PsReUqgVoWtXnhsltUO+Yqjlr58UHvHCgiM6eqHLe64uOHzx7Bg7un8MsXB4WvHxiP4+yFzaoz5WqxCo4b/r38cdsIAL70mKBl/LlmTTcuXNqKB7aN4Lb7tjcsB2xU5UHwOm1gOSCjsiQvky8hlMwrlohqqXCYMFjK6rBZsHF+M7YPRZAvVjaQSFMfPcJcV9CFqUROcUMqni3guUPTOG9RKzoU3rvWDrUAvyZI50uK711r3AbLcgglc4rvVXxsMlf6xdZB7DoexXMym1sAv4HZ0+RWfJ4AoooaDfPBRK4IjkNDxxygrcybOCqVnm9Bj3RDxtFIRuhir6X5w6fv24H/T7QJSeGZVccc5eQnmS3yrh+ZsoBFrV6EknldE+liiUUiW5TdxWvSWcoq1TWV0CN0ftPnmEtk+UGxtpulIcccKWWVWRzPb/FgJJKp2hUcnkk3PC/EKq7UlZWwpieAQxOJqgc54ZUBfhH3qUsX48z5TfjLG6PYNxbHojafrKhBFl6TcfV5MwR5Yc5Y84dYpiCbWdaksmNYLaSLmlzGHFAuD9UoxAhhwgq7eXqDoDmOw0wqLzlJNyIwAxW3RK0LNui2I5UvqQ7Cjik06qhlfosHoWROc5v2sEI2IGDsepMrZQWMNdgIp/gSfanxmJSyatnpj2YKcJczVJRgGAabFjRj31hc9S4suZdq7zmX3Yqff/AsdPid+Jc/7tIk9gEiAUaiZBHQH24P8OUyoWQeq0VZYyRn7oO/eA1f/PMeTMZz2D0S0+wqnU6Qjovy1xzDMPiPt62GzcLgH3/6Kn7w7BGs6QnA77Rhm0hM09KRVYzTZsUZ85qwYygiucjqb+CElsLjVN+Vdd9oDIvbvLIlX93Cc0P7hg5x43VKlIA1Kp8ejaTR3aALohphrsPvRMBlw+GJ6u66rx4LI5zK47p12isyWn1OnLOoBc8fnkYqV8R/PXUQ+SKLn33wLHz2yuXYNxZHplBSXcYK8GMFx6l3lxAHoJIwRxoJqS1lnVbxjJOit9kNm4XB2t4AHrztAizv9GN+qwf3f/w8fPOW9fjWretVlwoDwI0beuC0WQShiMBxXJVw2ahKoBarhcGKrgD2j8VVjcnxLB9foTQ++HXkqgH8c9mq4A4Xb0JF03n8/QjvBvqf5/sRSxewYzgKltNfxkogm0fxbAF/LecmHhiPC2N6/3QSDptFVbml22HFLz50Nm7Y0IO/7pvEB37+muKzWu3n59MoaoxG+U3zRoIXXyKrotyflLLqFOYA/nPKFljsFcXTPL5nHF4HP/5rpTvoRonlhPmuFE/vm0S+yOKGDT2KxxK6smq4hofKxoT5LfKfndaMuXA6jyLLqXLMEWEuni3gvnJG60hEeg3JcRyGZtKquprraQ4XVxE/4tWQaUgYjaQVYxqAyv1bO56NRjNCEyq1jrl4toDH94wL4wClAhXmKIokc0VZwQgQNYDQ0Zm1UbmaMJnWLMzJu0oCbmMB02S3IGhi84e4QikrwAsP+RIrLFbS+SKu/8EL+LcH9yoeV20pKwCs7Q2iUOKEEGkxrwzMoM3nwJJ2Hz5y4SLki3w2jNIEp9ljh8Nq0df8QWYSGTBcyiofmNqkM2OOdPaUy5gDyp2XdApzSrvnejPmMoUSckVW2s2loxxATCSdh99lqxN6tO5mknyyRs0fAHEDCG1ie2N3QnV5jxbkSlkB/Z24ACCSks/kbPI4UGQ5pDTsWErlZcpx1oJmlFgOu45HVb2eOGJqO/8CvLjx8w+eDSvD4BO/3qbJEUw+Z6VupHrC7QFg31glX46woS+IDr8THDjcftlS3HRmL0osJ7hl1SLc0w0WAks7fPjwhQuRK7K4eWMv/vypC7BxQTN2j8QEVwhxzDXq+ibFOQtbkMqXJEvsBkJJeBxWTYtCIlg1ymKMpvMYnEljfZ98g4UuYeNM+3NjLJZBu1/a9aaUMZfIFhDPFhsKc0vafVjc5sXFy+XdWAzDYEWXHwfG41UbBY+X3Vh6hDmAL2fNFVl852+H8fjucVy5qhMXLm3DP1+5DN999wb4nTZcuLRN9fHIApd0c23EockELIyyqOGwWRBw2dQLcypc4VK0eB3Y/C+X4s+fuqDK7WKxMHjXWfPw9jN6NR0v6Lbj6jVdeG0wLDi2soUS3vuzV/Gu/3lZELD13Buru/2YSeVVuQhnyudNaS5RaYCl7fkRKVemyG2kiuevT+6dQKHE4fxFQcSzRdz7Qj9eP2YsX47QVHbcPLF7HLkiK9xLpLS+fzqFRa1eWBtkfhGcNiu+/+4z8KELFuK1wTB++8qw7GvVbjpoFTWOR0iJqHLZos9lQ1LF5zYey8LjsDbMfVXi3JqcuSOTCeweieG6dd2qu22L6RI6s8rPsR7bPQabhcE1CmWsgHbhE+CNCACwQKGMWmvn+Kk46ciqwjHnsaNQ4vCzF44J80FxwwMxk/EcMoUSFqp4Ngc0zouByt+nXMqq/RyPRbOKMQ0Af//mS2yVmzRbKGE6kcOy8saQWmGO5NyORjKaNpNPB6gwR1EklSsK1mMpiDB3TEc5q9LiVfx1rSVJQm6SxMDFMAy6gy5hh04rcmJiZSdTRwfZdB4+p03W9jyvpgHE1iMhJHJFvHosrDigCU48FcIcyZnbV5MzF0sXcGAijnMXt4Jh+IcucTUsUdgRYhgGHQGnrrygSFp6Emm0+UM0LS/MkWtNqyBFFudKuTABHZ3U1JayZgusahcagYiJ0qWsxsqFZ5LSTrygxt3MqJBP1lg0qr0/1JAtlJDIFRU/N3K9mV3KGnTzHSK1fm4Av0khV/oviMsaMjnlmuRIcVZ5UUa68zXi8GQCfqcNnTKCx7q+IL7/njMwk8prKmcYiabhdVgVBUU9eXtAJWdT3J3TZrXg0c+8BS9+8XJ8/uoVQsbUZEynMKdCiPjStavw1zsuxndu3QCX3YqzFzYjX2KFyeyxUBpWC6M5yBuodFWU+hz7p1JY0u7TVK5GFnqNSlh2lTMFNyg4NroDZAGowzEXzaJHpkSWOGOl7g2ywOoOKJdUNnsd2Pz5SwUHpRw3buhBIlfEz17gu+uWWA5/3TuBpR0+RceZEleXF7u/ePEYbBYGd123UvjeTWf2Ydd/XoW3qsxVA/iSZgCqRfYjk0ksVHA6Etr8TuH50gjyjNPqmAP4bvWNysS08K6z+OzFP20fAcdx+M+H9+Gl/hlsG4rgj9t5J93AdAqL2rya7g0tOXPEjaRqs0hrxpyMQ54gXsg/umsMDpsF37hhOZZ1+PCLrYN45sAkvA4rVnXru34JzR4HIukC/rxjBE6bBf918zrYLAw2H5xCtlDC8UhaVUdWMRYLgzuvWQGGgWxTG4Avk/U7bQ2bjWiNsCDOKdKISg61G6lj0Qy6gy7dJcMAcOb8ZtgsjCCo/mkHfw2/c5O6btC1kDm/3Hw+ksrjhSMhvGVZm+J1BugTjYZmiGNOXpjT2qBqKqHcSVcMmSP97IUBdAVc2LSgGaMR6fnmMQ0xEwEdFSqVhl2NS1nVuhLzRRaTiayiGxyQbuBCntVLBGFO3e8kGcO5Iiu4pyk8VJijKJLIKjvmyI69ngYQQji43ELTq32hCYgW9TKCX0+TG+OxrC6VPiKTm+Rz2MAw+gSNcCpfVxorprYzK9ldDCVzQpt2KVIN8gHFkIXovprOrK8N8vly5y1uBcB3aPrA+QsBoGFWTnfQpStjbiaVlxRrK7vF2s9xtuwSkxN69F5raibTQTffUEFLYLzaUlZAW0kAIGquIdP8AdBfysqLqiYKcwpt7Al6OhfPCFl4ysInoO+ejqTysFsZIRhfDHElanVRchzHZwPKOub0dE6VF6trWdsbgMNmwbYh6Xyy2vd6aDKB5V1+xQXGVWu6cNaCZjx7YEp1NslIJIO+Zo/icfmyXu3X8L6xGKwWBiu7qse2zoBLcLIQl45WN7AWYc5i4Z1X5G+sFUUHZ1KY1+wWcvC0sHF+EywMhEUbIZkrYiKe1VTGCqh3zBERSEmY62qwAJSjUOIXFlKNHwCRY04mHweQbyailfecMx+L27z4n+f7MZ3I4dVjM5jRWcZK6A66hRK0D5y/sC4nq1FXwVrW9QbBMMCukWjD12YLJQzOpLBcRYZdm8+p2jGn5X6YbS5Y0oreJjf+vGMEv311GH/YdhxvWdqGNp8Ddz99GKFkDuOxrKryNDGrNQhzZDxRLGV16tssCss8lwlWC/+s6p9O4uWBGVy2oh0Blw3/ctUKZAolHJxIYOOCZlWdbpVo8tiRL7J4fTCCq9Z0oafJjXMWteDFoyEcGI+D47TlWxI8DhsWtXnr5q9iBkIpLG5vLKxqFY5GIqSUtYFjzmlr6MLjOA7jMflxTC1uhxXr+4J4bTCMQonFQ2+Moq/ZjXN0Oh67GmyYPLVvAkWWw9vWK5exAqL8Mw3XMOmaPF+NY05lTEjFMaem+QN/76TzJXz0LYuwoNWDeLYoOU8eVNmRFaiUo2pZd5C/T1XzB5Ui2WQ8C45TbmwESG/+kut/XrMbbrtVtWNuj0hEH5UpCz5docIcRZFkrqjouFpYHnz0CHNyWWIEv9MGm4XRXF4ouNpkBq7uoAvpfElXzlMl+6r6PVssDHwOm+5FvJR7iUDs28fDabAsV9VRb7fCjjeZDDbaBQH4fIyg2459Y9UTyFcGZgAA5y2qPNA/dtEi3POPGxU7LwH8YiKcyjcM5a1FTnwwVC6cUbZ/+502WC2Mjow5Uq6n4Jhza98hnE7k4LBaFEP59QpHwn2nJH7qcBvxwlFB8lwIgfwqj6uUE1nLvJbK/aGWHeVcM6UujEZcsJFyt2mpRYBQvqDxc0vkiiiynIpMTvXhx3ENpaxOmxVn9DXhjeFow3y1qUQO0XRBCMNX4rp13cgUSthySD5MmVBiOYxFMw3dCSTcXmsO3L6xOJa2+xSdQULH6YQ28Yg4iRo5NqTY0NcEu5XBtsEwSiyH4Zm05nw5gt9lx+qeAF4frHZcH9PQEVGMWsfc7pEobBZGECukEJwZGkVPsrDolskdJHMBqVJWwTEnkU2nB7vVgjuvWYlUvoTvP3tYaCpwvQFhDuCfu2cvbMb/uaK+yZpW/C47lrb7VAlz/dNJsBywXGGsJLT5eEeUGjewUOqusfnDbGCxMHjnpj5MxnP48kN7Mb/Fgx/945m448rlmE7kcNdf9gBQ3/iBsLJ8rR8Yr48IqaVfReMDn47c02KJRSwjH4EgPvbBiQQ4DrhxA18OfPWaTmwol56ftcBYGStQXR3zzo3877hiVSdyRRa/KWd36RHmAH5zeXAmLTnPSmQLmE7kVH1+Wt1GxDEnl3lKIJnASg1X4tki0vmSKZsEZy9qQSJbxM+3HsNkPIebN/ZpFvAJRCiUG5cf3TUGh9WCq9Y0du06bBY4bBZNG8rDM2k0e+yKDQ8cNgvcdqt2x5yaUtbyHD7gsuEfzp2PvvKaSkpQEhozqShlJUL/wYnG4wNByJgzsflD5RpuMK/y1s8xyTnobXbD47Cq3mAVu1vl8vpOV6gwR5GF47iGwpzfZUebzzErpawMw6CpbH3XQqNFvdCZVUcDiKiMYw7gH7y6guLLi3g5SJbScDiNvWMxTCVyuHxlBwBgp8zEOlso4dkDUzhjXpNs90IxDMMvmPaPx6sWs68MzKDV66gKlrZbLbh+fXfD3dNG9ncpSiyHqMwk0uvgW87rOcfRBsIcwzBoctu1d2VN5mBhlJ1dggtNpRBcKLHYPhSpcsxIIUwgdTQ9AKQFcb2iEcBPAvIl6ew6ci+qFfxiDT4vMV0BF+xWRpMwR8Tty1Z0yL7GSEOXSLogK7ZXrgftOUGA/EYGOcdqNzISuSJYTt05Jmxa2IxkroiDE8oOENLFsdZ5JsW163iB//EGXREBfjJdKHEN3QlBtwMcp22siKbzGIlkhLJ+Och4OqnR1RVK5mC3Mrqyg9wOK9b0BLFtKIKxaAb5Eqtq4i/H2QtbMJPKY0C0oVbpWqjtuMSRreQG4TgOO4/HsKo7oCh6tngdcFgtmh1zxMkht+Nvs1rQ5LFLCnNkUdColFULV6/pxKYFzbj/teN4ZOcYFrd7VQlbSrxtfQ/++E8XqHIRq2F9XxOOhzNCtpkch0njBxX3MnG/yTXZEBNK5tDksZtakmqEW8tlfi67BT953yY0eRx4z9nzsKTdi7/tnwQAoYxdLT6nDQtaPao6sx6danz/6XkmRTMFcJz8c6NybP454HFYhfklwzD48ttWY16LWxinjUDmzR1+p9A594ry73pkJ9+hW68wt1oQQSWyM4VNh8afn1a30YiKjs78cRvP18i4161iM70RJGfu+88cAVARQvVAyj2lHHNT8SxeHpjBpSvaFcUiMT6N+chD4TTmq8pss2kQ5vhxTy5qQ0xLeVx7//kL4HPahPmHlDB3LJSC1cIIG8ZK+F12LG73VrnHGkFcekqlrD6N4jLZnGoUjUHuX5IBXfWzTR64HVakC41/ZyxTwOBMWqh2ocJcNVSYo8iSLbAosVzDjLKFrd5ZKWUF+IFAq2OOhH7LHZfsbOnpzBpRKLHzu+yaF/GZfAnZAqs4aXI7rGj3OzE0k8az5dbyH79oMbwOq2xGzN8PTyOZK2rapV/bG0A6XxKs2LF0AfvH4zivnC+nlUpgrPpFFt9VUjqbi2EY+Jz6XImC0KOwqGnyaM+lIplqSmHFFbFL3bFfGZhBPFtUH6Kr1zEn2fxBf45fJFWQP67mUtY8nDZLw0wjgC/D6Wv2CNkejSixHJ47NIU1PQHhGpXC59Tv0OQdc9Ljj9ayXoLQ6VVmrBAyOVUeN56RH8vkOHthMwBgW4OcOSLMqcnU6g66sWlBs5AzpMSIELTd2DEHaMsnJbvWqxsKc/wkXWvH6elkHq1ep+7soLMXNiOWKeCZA7xIoLWsTgwpaRKXs/ZPNXbsSOGxlx1zChPysVgWoWROyDaTg2EYdAVdmjPmxgTXm/x10eJxCGOUGLK46tKRdSYHw/A5cCWWQzzLP4eNZEbNBmeUP4vdI8qLwsOT/HWh5l4mwpyactbpRG5OlLES5rV48M13rsfPP3i2MAbYrBb867WrhNcsbtMuGq3qCmBgOtlwbDs6lURvk1sxnJ88n7WIGmpc/UBlPvHW1Z1wiyIYzlrYghfuvFx3PqIY8qy56cxeYc60sM2Lxe1eFMsbwlo3BghkQ2X/mHRTG/7YjT8/rc/9kXC64UYRoM7FRMwCZjjmNi1oAcPwzb7OWtCMBQY2clx2K1q9DkxIrJme2DMOjkPDbqxitAhz6XwR04kcFqgQuoIaGmtNxflNdaVmK4QrV3Xg369fhdsu5d3KRMCSagChNWZifW8QQzNpoRqrEVGhaqtxNY2aLsBA5fnZqJRVKmOu1jGntjs7UNmUHZHJ6ztdocIcRZZEuYRLKWMO4B+skXRB9cBCiDRwzJHvac6YE9w20scVHHNR7flnSm48v8um2Wmk5hwAfI7W8XAamw9OIei24+yFzVjXF8SekZhkudaTe/kW1Fp2OUnOHLEYV/Ll9JUwdAsd9tQLoOR8yE0i/S67cF1qoVKCrCQCO7Q75lJ5xZwy8e9U05kNAJ4qf3ZXr1H+7PR2DVUSeCqlrDqyEtPygp8gRqmdfGgosQSAjfObMRBKqXLNvTEcQTRdEHbq5dDrmCuxnGLpEHFMae6sR86vzFhROcfqruGoinuilo3zy8LckLIwd1CDYw4Arl3bhXS+cTlrJc9HXRaKFqGdZBQ2cqIF3XY4bRbN5ZahRA5tfv1uJ5Iz98dtfJC33lJW8bFeGxQJc6EUGEa74EcW8UqOOSFfrq+p4fG6gi7JBaAS44LTRH5B2+x1CNmSYkaiGXT4nXWdpI2yaUGLEPdw/XpjZayzAcn629mgAcSRyQTsVkaVQ5Ms7IhDSYlQMq+rrHs2edfZ8+q62165qgPnLW6Bw2bBIh2i0aruAFiuslkhRYnlMBBKVVUmSEEW3Frmmf1T5cyrBu+dPO9u1CCwaOXiZe24ZHk7PnDBwqqvk2dxd9AlCFhaIWKqVM4cOQdqRD+ycX9ksnF5YTpfxEwq3/B5BEBooqfkYhovr0nMEOaCbjtWdvHnRG/TBzFyGyaP7R6H227FFauU51NivE6bajcXeS4rdWQlBFx21ZUIU4ksWn1OVR2APQ4bPnbRYuHaJONcrTDHshwGNcZMrCs/E/cq5COK2TcWg93KKIrBWsuxBXGtgTAnRNKIM+aiGQTddvicNrgdNlWlrMQheOnyDjhtFuqYq4EKcxRZiBOnkWNukc7OrNHy7rWiMOe1I5YpKOYy1B23wYLTmGMuL3tsPaWsFfeS8uJ4fosHM6k89ozGcOmKdtisFmyY14RUvoSBcjYJIVcs4Zn9k9jQF1S1k0dY28s/xHePxPDCkWn87IUBAJXGD1rp1uGYIzlMctcEf461i0aNSlmBSmC82qYgqVwRw+G0YiAtAJw5vwkAsPVoqOExSyyHv+6bxNIOn4pJur4gaCXxU6u7T0y43KFWSjjS6hKLpQtC4K4aSEfCp8slR0o8Wy5jvXyVch6Kp1w6rTVjLlYuHZJzomktbSaEyXjZsJRVrfgpP5bJ0eRxYHmnD9sGlRtAHJ5MoDPgVO3GI8H4T+yZUHzdSJg45pTvuYpjTr3QPlJeADQqQWEYBp0Bl6bmDxzHIZQ05hA6awEvipLs0EUGHBDtficWt3nx+mC1Y663ya3KpSrGYbPAbmUUd8rVNH4gdAddiKQLmrJJD5dFD6Vz0uJ1IJLO143vo5GMru62avjWrRvwp386X1ggzyVWdgXgsFqwu0HO3KHJBBa1eVUJl+RZt2NYWbjPFUuIZQpoM9GlOFswDIP/ef9ZePjTF6rqcF8LEYyUGkAcD6eRL7JY1uiZryNj7tCkOvfy2t4gFrZ6hBLT2WB+qwe/+sg5dQLA5Sv5Z7FetxzAuzU7A07J8zwQSoJh1Od+9Ta58YKK+dqoSgc3IP7slEpZGzt/tXD9ui50BpymbAx0B/lnnngtNhrNYNtQBFes6lB0etbic1pVO+aGVXRkJQTddsQzRVVz+Ml4TlXjBynIBlBtKetYLIN8UVvMxLpedc5lgF8fbBuMYENfk+JzWmuDjbFYBp4Gne6Bytyz1jFH7mePyuYPu8vC3NreAHqb3ZLOw9MZKsxRZCEDZ0PHnM7OrJF0HhamslMnRYvXAZbT5nyIZgrwOKyyE0nByaXDMRdNF+C2WyUHRb/LjlyRRb6ovvum4JhrUGYgXixeURYUiPugdsf7hcMhJHJFzV3gFrX54LZb8fOtx/D+n7+GV4+FcemK9oYCkRzk4aUlLyii4LoCeFHDUCmrojDnQJHlVE8Ydh3nQ/DJglmO1d0BdAVceO6gshsI4N1coWQO1zRwywGVbCctZS0ALwZbLYxkHoiRHL+wQimr5q6smQKCGhxzFy9vg9Nmwd/2Kws7APDcwSm0+RxY39u4rE5rFgog37mZoLeUNdJAxG/W2PxBS4MNMZsWtGA8lpWdTJVYDocnE5pKn3qa3DhzfhOePTCpKMioLWUlbmkt53hEVJLRiK6AS8ioUUMyV0SuyBoS5lp9vJgGAHYr0zBsvBFnLWzG8XAGe0Zi+OKfduPQZEK1w7EWj8OGtEJ20q6RKLwOq6oyWT2dWfeMxtDX7FZ8jrZ4HEJpKSFbKCGUzDV0CujF57QJ7sS5hsNmwaqeAHaNxGQXsul8EcfDGdX38qI2L5o9dqG5jhxGGqGcCIJuO1YpNC1RYlU3f+6UcuZIvlyjuZbdaoHLbtH0TDo8kYCFaVyi/sVrVmLzv1xqunNUDWctbMaFS1tx/Tpjbr3V3QEcnkjWNR8ZmE6p3nRgGAZvWdqGgelUwxK7yvPIrFLWxs5fLdx++TK88qUrVGe/KbGqO4BCiRPyFgHg8d18LqCWMlZAWylrxTGnJmPOjnyJRbagvAbjOA7TCf3CnNNmRYffiZGaOdBgiH+vWlzna3oCYBhgz2i04WsPjMeRyBVxziLlZ4rfaYPdymAmpW6OMhrJoKfJ3TBuoTZjrlhiMRHPCnMmtc0f9o7GsLjNC7/Ljr5mD0YiadWGiNMBKsxRZCFqu79Rxly5M6vajCdCpNzCXalTEJkwawl2j6Xzsh1ZAf4BGXDZ9DV/yBRkF9x+HbuZSh0yxZDdIquFwSXlHU3iPqjtrEa6wGkV5qwWBrds6sPq7gA+e+VyPP3Zi/G/Hz5HdzZOm9cJu5XR5ExUEneAiitR6yCuRoQgHYmebODaIWwvLz7OWqgszDEMg8tXdWAglGp4j5AS5Eb5ckBFYNZybwD8NdfssUvedwzDwO+y6ytlLU8CpBbHvnLXWzVCCcfxpaBK93AtHocNFy1rx+uDEcXS95FIGgcnErhsRYeqDmV6ciOjjcRlna7EcIOyd4/DCruVUe0S01PKClRy5n72wgC+9Jc9uPC/NuNf/7xb+P5wOI1ckdUs8ly/rhupfAnPH56Wfc1INA2f09bwPevJmBuJZNDssatyxXQEnAin8sgV1bm6QmUhwmimFhlr5rV4GjbfacTZZcHo7fdsxR+2Hce5i1rw5bet1nUspWyZEsthz0gM6/qCqsqGugPanNapXBFHp5NY36cstJNxSdyYYExl6PWpyhl9QYRTedlSoiMa8uUA/vmxcX4z9o3FFQV2EutgpLT7ZKG3yY2Ay1bX8V7M0Wl1whyg/Zl0eDKBhW1eVaKU3q6dRrFbLbjvY+fhH8+db+g4a3qCyJdYQegE+PLCY6GUpo66Fy3ny5m3HlF2zamNVgDUBfKPxzLwOW2mCGkEs7ItP3D+QrjsFnz/2SOCa+7RXePwO224ZLk2l6XPZUe2wKpyRQ/NqC9lDaqcW8UyBeRLrKqOrHL0NrvrHHOkakxLKavXacPSdp8qx9xr5UzYRsKcxcK7+tU8QzmOw2g0o2pzym23wmG1CPOqyUQOJZYTftbtsCJfYhW7csfSBQzNpLGu/Lzua3YjW2AlYyZOV6gwR5ElodUxp7GUNZJunCNFOvEMaRHmMgXFkH+AF2G0BkwD/KJb7th+IThf/aQp0iDQnUCEubMXNgtOop6gC20+J3YdrwzouWIJTx+YxPq+oKquQLV87R1r8cQ/X4R/vnIZlhkM+9XycCAI5ZAKwlyhxCGnwZUIVILulRb0H7lwIboCLnzl0X0YUnEtbxuKwGG1YG0D5xUAXF7u/rlZwTXHcRye2juB3iZ3w86QAL8rZ7Mwitk1UvDCnEIXWbdNZymrvKjKMAyCbvnmGve9OoRb/vslpHJFJHNFlFhOs2B01epOlFhO8RwT16LaPBS/y6ajuYZyU4WAkONnbldW0sVarbtYEKs1lAwDwFkL+EnhL18cxP2vDWM6mcMft48Ii+1D5Y6tKzSW711b3kh4dNeY7GtGIhn0NTfe2dUjzB2PpFWPmaQz65TKBhCk82Wbz5gQQdxXRspYCRcsbYPDZsG8Fg/+5/2bcP/Hz9MdEM4Lc9L3Sf90Eql8SVUZKyByzMXVbejsH4+D49BwHCZOU7EwR1yffbPkmJvrNMqZO6yyDFLMxgXNKLKc4kKTNCQ4WRxzRmAYBucubsWO4Uhd7AhBrWMO4J9Jap3A2QLfzGuFCY0bTgYqOXMVEXQ0mkGuyApuYzVcuKQNDAO80FCYU++YU5NZOx7LKjakOpG0+514/3kLcGA8jr/tn8SxUAp7RmO4ak2X5vgDMr99qb9xufBQOA2X3aLK3UY2PYkjVw4tHVnl6G1yI5TMVYmLpGpM6/N5XV8QI5FMwzz1146FYWGATQ2qdAC+9FiN63wmlUeuyKranGIYBu1+J3aPxDCTzNWVcnvKebNK5awkS4+U8JKfFW8OaYmuOhWhwhxFFrKzQ7Ks5PA6bejwOzWXskbTygIBUBGk1LqC8kUW04mcrKuNQIQ5rc6raFreyaMnLD6cVnaIEVZ1+7Gyy4/3n7dQ+BrDMNjQF8TBicru9ItHQ0hki7h27dwIm+7W2GGvsWNOn9soqpANSGjyOPCdd21AKl/CHX/YiaLCrg/LctgxHMG6viCctsaTkguX8qWWmw/KZ6DtG4tjNJrBNWu7VO1yOmwWLGn3CRkyagmn8opCsN5y4UbCEZ//If25PbprDNuGIvjvLf2CmKK1xPKKVR2wMMo5c88enILdyuAtKnN0fE7phi4cx2HX8ajkBKJRKavWDrWEcIov/VfaTW9y21U75vSWss5v9eArN67Bf7xtNZ77/KX4zq0bUGI5PFIW1EjjB62Lwd4mNy5Y0oon9oxXOR4IJZbDWDSjyp1AxEZSctGIfJEvyVBzbIAvZQX4AGk1hARhzpgQcd6iVlgYYGW38YV2b5MbW794GZ7+7CW4eo26MUcOvpRVejJORJ8zVDR+AIAuwQmsTpjbUxaA1vcqH7+l3KRHvPgZ1VC+fCqyvvyZyOXMVYQ59W4jsmjcrlDOWnHMnfrCHAB8/KLF4Djg3r8PSH7/yFQSbT6HqkzOha1eDM2kVEWmHJ1KguW0CasnM1KdWQfK65IlGvLrmr181MXWoyHJ5mqEkUgGDANVsQJCJrCMY47jOIxHs6Y0fpgtPnHxEsE1RzbQbtigfa1BmuKoqU4ZnklhfotH1fNpVdml/8Zx5VJ6sqHWHjDmmAMqrmuAF+b0xEwQkYo0RZCC4zi8NhjGmp6gsA5SoivoLotuyq5EtY0fCJ9963KEkjl85v43hDLjijBX7tCuIMztEfLliDDnqXofAHDPc0dx5d3Pa64GOlWgwhxFFiFjTkVpz+J2L45MNW4JT+A4jhe5GkxESItsNQ4mgF+Up/IlXNzAWt0ddCFf1Gaf5fNpCmiWyXjSU8oqOOYanAe/y46n7ri4LsR1w7wmFEocDozHEc8W8MPNRwEA12noxjqbdAXdCKfyqq8LoRxSofkDoL1TZixTgNNmabizd+HSNnz8okV4YzgqnEspDk8lkMgWG+bLEdwOKy5Y0opXB8Ky18dTGspYCSu6/BiJZFTndZRYDtFMQbbrLUC6C2t3zM2k8rBZGMERVkvAbZcUoziOE1x/974wIHQFVts4gNDqc2LTgmY8f3ha8npL54t4qX8G5y5qVR3i7XfZJJs//Pfz/Xj7PS/ir/vqJ5aNXLB2qwUeh1VXB+dGpf+kgYkaiIAX0OhMBIAPXrAQH3nLIixq8+Ktqzvhd9rw4Bt8t9DDk3ym0TINi3nCv1y1AiwH3P30obrvTSWyKJQ4Ve4EIjaq7QI8HsuA49Q5HwC+lBUAJmLqHHPTJpWyzm/14NHPvAW3XbrU0HEIHX6XKZlSSqWsWho/AHwn34DLhkd3janaONsjCpJWQskx19uk3V1+KrC4zQu/01bluhdzeDIJh82iyUm5oa8JVgujKMydTo45gK902Di/CX/ZMYqpmqYxHMehfyqpKn8R4HPUCiVOcvOiliNT5U0SndmRJxvzmj3wOW1VnVmJS1FLKSsAXLSsHbFMQVEsGYmk0el3qdqcFTKBZZ77sUwBmUJpTgtzYtfcf2/pR7PHXtfFWA0LWr1Y1R3A3/ZPKpY9FkssRiIZzG9RN/6QRnUv9c8ovo40btKbMQdUXNbirN0jU0ldMRMkhkHpWjs6lUQ4lW9Yxkog11EjV78Q56BSmLtlUx/ee+58vNQ/g2//9VD5Z/nnZ8UxJz+33TMaA8NURHTye8V5ji/1z2A0khEqE043qDBHkYWIH0rNGQhXrOxEOl9SFXAP8GWyRZZr6Gxr8tjhd9mEnIFG/P71YdgsDN65Ubk9OMkTG9PQDSZe7rYYlCn9IudJy4Kb5EZpda0QyID+zIFJvPt/XsEbw1F84uLFukuSzKZHY5B3uNxcw+2QnujoKRcGyuXNKgWIz1+9Aqu6A/jh5iOCY6CWbYP8okONpZxw+coOFFlOMreEZTk8sWccbT4nNs5Xf0wy4VZbzko6hjZyzPHXujY3aSTNO/HkdjaDMsLcdDKHSLqAlV1+5IssvvbYfuH1WrlqdRcyhRJeFHVUi6bz+PnWY7jxRy8iX2Rx+Up1ZawAX1aXLbB4fPe48LWjUwl875kjAIBXj9V3JyUdqxTLhV3y7kE5ZpLKTkcAQimrms8ulinwzXd0dBoU47Jbce26LuwdjePIZAIHJxJY2Kou06iWTQua8dbVnXhiz4Qg6BDUNn4g78lps6gu6yXurHkaHXNqO7OGTMzUWtMTFILE5woeh1U2O2nn8SjafE7VC06X3YqbN/bhyFQS2xo0EQD4if68FndDIZ/cj+E0dcwRLBYG6+cFsWc0JukQPzyZwNJ2n6psQILbYcWangB2DEdkxyHimGs/TRxzDMPgU5cuRb7E4ucvHqv63mQ8h2SuqHojY42KLq+EQxPaMgJPdiwWBqu7A+Xydv7aG5jmN/W1dnx9yzKSM6eQeRpR5+AGAD9xzMmIFqSyxKyOrLMFcc1lCiVcs7Ybdp1Zp9et7UIsU8ArA/Ii2ngsiyLLqcqXA/h57eruAF7pn1EshySlrEaEOfLMIM+Q4Zk0hsNpnKtSOBOzujsICyPvXAYqc021wlynyqxWIiz2aIhz+I8bVmPDvCZMlOc/vRpKWfeMxLCo3PgBqMy5yPwuWyhh+3AEZy1sPiGNaOYCp+dfTVGFFsfcDRt6wDDAQztHVR2bCGKNJusMw2BBq0ewzCpxPJzG1qMhXLmqs+GEj1iNxzR0Zo02KP0iD16tjrmAy6b74UY6s97zXD8OjMfxhatX4EvXrtR1rNmA5GWoLWeNpPKKZb16XIlAucunSqHHabPiy9evAssBf90rbbUnXee0CHOXlQWhZyXE62cPTmEglMI7N/ZqWgSt1CjMEUeikmMu4LaD5YCUiu5K1cfOKzYxCbrtSOdLdTuk5L2/5+x5uHpNp9CZTI9Y/dbVfMfiR3aN4ZFdY/in32zHOf/vWXztsf0IJXP4+EWL8N7z1AdM//MVy9Hmc+ILf9qFQxMJlFgOd/5pNwolFm67FW8M1wsH0QalrEA5x0+DMJctlDAUTjfMyWly21FiOSEfVIlouoCAW7oJiFZuOpPfCLn/teMYDKUMLQS/cPUKMAzwrb9Wu+YqQdvqJultPqfqjRetx+7UKsyZVMo6V/E4bcgV2bqyr3i2gAPjcZy9sFlTqex7yyHwv3t1WPF1qVwR/dPJhmWsANAqUco6Es0g6FbX8ONUZUNfEzKFEl4frB7Ldh2PYjyW1eW22ji/GeFUXnZDdTqZA8M0jvA4lbhiZQeWdfjwu1eGqxzpQr6cWseckKPWOCz+8GQCDqsFC1UKG6cCq3sCSGSLwkJ/IJSEx2EVNlPUsnF+MzwOK/4ukzOXzhcxk8qrFuZINnQoIV2lQ5qkGe22Pdu0+534wPkLAQDvOEN/F91ry1U9T8rMsQFtjR8IFyxpxUwqj8NT8nNiEkHRYaSUtewSI8LW82UB92KVMSli3A4rlnf6hVgGKUjjh7NVdvnuFtZeynOgUR0NkJw2K3783o1o8Trgd9mEua6blLLKVEglsgUMh9NY21PJg23zOeGwWYQ52I7hCPJFFucvaVX9fk41qDBHkYVYrtXszncFXbhgSSueOzitqnxouDzgqgnbXtDixUQ827Ac8o/bR8BxwLvPmdfwmD1B7Y65RtlResosI+lCQxeMEs1eBxa1eWFhgP+6eR0+fdlS07owmQHZ/VMb5B1WLcxpc8zFM40bjYg5a2ELPA6rbPjvtqEIFrd50aphod3X7MHKLj+2HJqq2s3jOA4/eu4oHDYLPvqWRaqPB4gdc413z4FKhl8jNxegXfzks+vkz3HQzX92ta45Iswt7/Lj365bDUdZpNbjmFvY5sXyTh8e3jmG/3P/G/jr/gmcOa8J3333BrzypSvwb9evVlV2QugKuvDf79uIfJHFJ36zDT/cfAQ7hqP4yIWLcOHSVsnug2qy4IJuu6ZyYSIKrhFNaKQgY4maMVhr51slzl3Ugp6gC799dQgsZ6x0anmnHzef2YetR0NV7lLialO7EFrdE1Adr6DFjQfoE+YsTOPIgpMVj126hGX7UAQsp36Xn7Cs049zFrbg8T3jioHY+8bUNX4AIIxNxC0B8G4HtSU8pyrXreuGw2bBp3+3QxCJjofT+OivXofTZsGHL1yo+ZgbG+TMhRL8Jo7eDcmTEYuFwScuXoxErojfvjIkfP1oWUBY2qFuzCTlmvsVurwSDk0ksLjda7iD88kEES6f2DOOzz2wEy/3z2BFl1/zvNhhs+D8xa3YMRSRjAoZ1dD4AeCf+d1Bl6zTkWxed81xxxzAb5499OkLce5i/eLJ0g4/lnX48Ld9E7I5fkNh3u2opZHdBUvL5axH5Z145BlgpJS+1jH398PTsFoYXKCjtBfgn2FjsaywiSeG4zi8diyM5Z0+1ZsZXSqrlUYjGVgtDDo1ugd7m9x44JPn4xcfOlu4txo55ogRRrxRYLEw6GtyC3OwV8plyBcs0XceTwVOn9GaohnyMFJTygoAbz+jF/kSiyf2jjd8LXHAzVcx4M5v9YDjqmvQaymxHP647Th6gi5VOxY9EvkAjSCLXbkuhsSaqzbvC+B3740u1n7wnjPxh0+ej/ecY6zV/GxAdm3UOhMbCXN6RCOO4zSVsgKiSdlwpO53TSWyGA6nhcWHFi5b2YFQMo9dIsv6S/0z2HU8ined1ad5B6+3yQ2f0yYE7jeiUddbQFSSnVF/HRdLLGKZguBKkUII5E9LC3Mr///27jy+iTr/H/hrcidNk973RVtoKeW+71MXOeTwWgTxwPW+1vULv/X+ynp8F3d1XXUVEXVRcD0AFQVWpQJWAeWmXG0phd73mTZpks/vj3TSps01abFN8n4+Hjwemk5nJul8JjPveX/e7ygNEkJVuHt6MjjOvXODPQ/OGoiZaeF4btEQHPzzbPzn7olYMjLOo6mVgOUJ5TMLM1BYrcOr3+UiIUSFx65Ow8gES/fBU13qgvC1M51lolmmshrdni7Md5nLcNGtlz/G3akzJ3RMOCMScVg0MtZakDy9hzWNHpkzEDKxCOt2n7V+Rvz5P97NG6GhsVqYzMytsXFZYMacUiaGRiFBuZtdWauaDAgJkAvKhvUm/MO7rkWfDwmcftPZzeMTYDCa8fmRIofL8DV5hroRmFPLJUiNUGPP2Qq0tplgNFkafvjrNFZeZqwWb9w8CvUtbbjl3YM4VVyPW987hOpmA/7x+5HWBhFCWBtA2MkoBiwZc76aPerMohGxiNYqsPHHi9ap37kCOrIClnPt4OhAm+ma9jS2tqG4rsVv6svxMqIt35Ev7jyLrUeKMSE5FC8tHebRuqYODIPRzPDd6XLUNhugMxg7fR8Je5gDWMoQ5JY32n1YVNp+jRzTj2vM8aRiEUa4WTPUmWsyo1DVZMAvF7uXBAE67hMTBVwLjk0KgVjEOa0zV9mgR0iArEdTJdVyCYJUUhTVtcBgNOPn/GqMiA/y+JrKWZ25yzUtKGtoFfQ9Gu3mbKXiuhZEaRQeBe9TI9Q2GXx8YK7FwXRtfuprZJdjPDZYieK6FjDG8FN+NdRyCTJdXOv6MgrMEYcaW42QiDjI3Tx5zc2Mgkwiwvajrqez8t1WEtxIUe5oAOE4MLfvfCVK61txw5h4t25+orQKiDjPMuYcTmUVOM2SMYYanfNAlDuGxmndTm/+rUULqDHXYjChpc3U6xlzDa1GtJmY4GYC/EXZgQu2Fw2H26f8uNv4obO5Qyzp+6s/O2FNp38jKw9iEYe7p6UIXh/HcRgUaenM6k6Qx1XXW6CjGYCQjC5rXTWnGXP2u5GeK29EeKDcuk+PXjUIP66Z5XGdxIXDY/De7eOwcmJSj6YqdLZiQiJ+PzYeEhGHl64bCqVMjJHtF6ZHL9XZLGtp0uD84kyjlMJgMkPvRmc9ADhdarlYG+LiYoU/N9W60Zm1TtcGbS9mcC0dGWv970E9vBmMD1HhxrFxOF5Ubw3uFNW2IFAugUbp3oMivhlA18CpPUW1LQhTyxzWtrQnUqNwO2OuukmPMLVvZssBHRfkXae/HyqoQaBCgvQo4RfZczOjEKySYvOhSw7PbacEBOY4jsPy8Qmob2nDjhOlKG/Uw2Rmfp8xB1hKAPz9xuEoa2jFwtd/xIXKZjy9IENQI6LOYrQKRGkU1pIPnfEBo+h+PmXvSpBJRLh/ZiqqmvR4bY+lTmleRRPUcgkiNe4HKofEaG2ma9rDB/z8pb4cb1BkIIbHB2HqwDB8es9EbP7DBI+Dk1Pbm8g98p9jGLn2W2Q8vRtL//UTLlXrBJc/ACzfSUYzs1u7uKR9ymGUFwTmesvcTEszu50n7SdzXKrWQcQJ+4wDFVIMi9Pi4IVqu3UzAcvD9Z7Ul+PFBilRXNuCI5csWZWeTGPl8d9hHx24hA8PFGL70WL8mFuFS9U6/JRvmTkwboD7GYrhajlEnOt7r5K63ssaV0qdZ8yV1dsvYRUXrITOYEJxXQuOXa7D+AEhfpXl25X/vnPiUpO+DWqFxO0UcI1CijmDI3CwoMZlwKuwRgeZRITIQNdfQnzwzllg7uNfLoHjgBvGOG/6wJOKRYjUKAQF5vgsFEcBHo3AxgQ6gwkGo9lnpzcBlvoBEhHnss4B0FGU29nn4Unzh/z2rlwDXNTn6orv7Lu/S/FfviD5mCThgbnh8UF4cv5g5FY04ffrD2DXqTL8lF+NRcNjBKXrd5YWpUGdrs1aUNsZPmDjPCvRg+7C/Hpd1JgDYFNbzdx+kZrW6eaB47h+d7PMcRxeXDoUh56YY02xHxYfBBFnqYnRGd891RlHQUpHckoaEKySuqzJyWcCu+pirTdaguC9NZUVsEw/HBanRYBMjKReaD5zx2TLtO6N7cXSi2pbEBusdPv7iJ/e6F5gTodYARf/gOUGyv2prAafzhCy142txWDCiaI6awaDUAqpGNeNisOFyuZuD0d4J4rqkBCistZvcmXpqDgopCJ8eKCw01S0/nWu6SuLRsRi7aJMAMCdUwbg9snCyip0xnEcRicG41x5Y7fvkV2nymAwmvG7If2jc/xvbdm4BAyJ0eDd/QXIq2hEfmUTUiPUgqZa8llhOU6ms55vzxRO87PAnEwiwhf3T8amVeN7/MA6JVyNtYszcfvkJNw8PgHXZEbh2OU6zH9tP7YfKwEg7PzB19aylxVVVt+KQLnEeo3rDwZHByIpVIVdOWV2mzUUVusQrVUKzmybnBKGRr3R7vgwmxnKG/S90ngmNkiJsoZWa9PD6WmeB+YGR2sQqJDguzPleHL7KTzyn2NY8e5BTFuXhf+39SQACGosIRGLEBGoQKmTaxSdwYhaXVuv1TVUtdeYcxSY47P3unZb5QOv248Ww2hmfl1fDqDAHHGiSW8UXBR58QhL1sSXx0ucLnepRof4YKVbhcf5zBlHDSAqG/X4/kwFpg0MF/RkJSZIKWgqa52LjDm1wGyummY+SOK7X8QiEYdIjcKt5g8V7V8goU4ySzzJmMsrtwTmBro5VYQ3ICwAsUFK7DtvG5g7XFiLIJUUyWHC1se7c2oynlmYgQuVzbjnw8MAgHtnCM+W4/HTBt2Zslfd5Dowx18YCpnK2nEsu87E6xyMulSjQ2ub2Sum23AcZ/P+1HIJBkUG2mTM8Rc6rupG8oE7dzJJTWaGs6WNyIjRuLx54y/8TxU7rz/E/w16ayor742bR+GjP0zolSmbyeFqzEqPwH9Pl6OgqhkldS2Czu8RgQpEBMrt3gR11tpmQnmD3u2OrJ3X32wwuQxgt7aZ0KQ3+njGXPcL8qOXatFmYh51qeMta28C8cFPF7v9rElvxIWqZrey5XhapRTXDo/Bsct12J1jKTpOgbkOKyYk4uhTV+HJBRk9XtfIhCAw1j2jePuxYsjEIsxrz5bxN2IRh7WLM2E0M/zp0xOoajK4PY2Vl+FGZ9Zz7VlZ3vDd2p/dMiERzywcgheWDMW/VozGB7ePg1wqwuHCWnAcBGV+djws6v53K61v9atsOcByTTVvaDTKG/R4+b/nbDKjGWO4VKMT1PiBN6k9sGNvOusvF2vQ0mZyWRbEHbHBSpjMDFuPFiNIJRX0XdSVQipG1mMzsPW+Sdi0ahzeWjEKzy0agjunDMBVGZFYNWVAt4CWK1FahTVLzZ4SDxo/OKO084CuM/5BZtfOw/x38GeHLWUrJvSgdqEvoMAccahZbxIcmJuRFgGtUup0OqvZzFBU0+J2DakojQIyschhFsiOEyUwmhmuG+1ethwvNkiJqiaDW8XBAdddWcUiDgEysdtTAK3NJHy8M1lMkHuBOb7wdIqTtvaedGXNbS+uPFDgk2OO4zBtUBguVuuszUrKG1qRU1KPUQnBPepmefvkAXhu0RAAljobQvetszQBnVkvVDVBJhY5zd7hpwoKmcrKB+acHcv2ssT4YKK33jyMTAhGWUOrNSP08yPFMJkZpg10Xrh2eLzlAs5RbZXOCqqa0dJmctn4AbB0TIsIlCOn1HkwqsHFucxT8SGqXqk9w1s1ZQAYA/666yyMZiY4iDI0Vovz5Y3QGx2f4/mLUyFBPwCI0lrGkKs6cwVVlu+t6H6WBdqb7BV9PtiD+nK8lHA1ZqdHYFdOWbfz2+n2xg9D44TdDK2YkAgA2NRegJ/vrkcshJZ8cGRK+zlwy6GOzrrlDa34Kb8as9Ij3M5y9EWjEoJx05h4HL9cB8D9+nK8gZFqSESc0wYQueWWbqT9Lfvc200bFI5vHp6KmWnhmJIaJqiZVKRGjjC1vFtHXcYYSupafPo7wpG7p6dgRHwQ3vwhH89/fQaMMZjNDDtOlKJJb/QoMDcqMRgyicg6BbSzbe33pktHCrtftIcfW5WNekxJDevxA8kwtRyjEoIxdWA45mZGY+XEJDy5IAPvrByDpzx4WBKtVaCiUY82B1N6+anwvfUd6Kr5Q2l9K2QSUbdSL/x13cVqHbRKqTUj2F9RYI441NhqdLvxA08mEeGazCicLWu01pHrqryxFQaT2e3AnFjEIS5E6TBjbvuxEgTIxLhqcKSgfeWnfbkTNAI6TWV10PwBsGQbCc6Y8+GprICly1RNs+sAKB+YcxakCpBJwHHCMubOlzdBJhF51ExganvNiP15lWCMYfVnJ9BmYri5FxptrJyYhN2PTMPfbxzRo/W4mzHHmKVRQXp0oNOpAUKnZANAdfux7Kz5g73AHF9rxVun24xMCAJgyQoxmxne+7EAWqXU5UOCMe3T+/jghTP8Rbyr+nK8zFgtzpU1Whsx2MOfy3o7Y663TUoJRVpkIHae8iy7aUisFm0mhvNlTQ6X8aSIN9AxHaPCxXTW7DzLzUFPMsf6O1X7Azxdp8ZHhwpqoJSK3eqY6sxDswcCAP7ZXpOLd6K9gY7QLIVhcUEYFqe1jg9/b/5wpaRHaXB1RiR2niqz/q2+PFYCxoDFnepR+qvVc9Os59/UcGGBOblEjNQINU6XOH4Ac668EQMjA3v0AJHYFxGowHu3j8OmVeMF/R7HcciM1eBsaaNNsKRO1wa90ewVjR96m1YpxaZV4zA2KRgbfizAg1uOYt5r+/HglqNQSEVYODxG8DoVUjFGJwTjl4s1NtdBrW0mfH2iFBnRml55GNz5moEvfdOfRGkVYAwOy9zwM8Z6byor3/zBUY25VkRpFN1mfnR+KDohOcTvz1kUmCMONenbBGfMAZabTsB+HQUA1uwjITW1EkJUuFzb0q0OQUFVM45frsPvMqMEFe4GgNj2k1GxkwK6ndXqDAiQiZ0GNQIVErezufwlY46vi+WqHtP58kbIxCKnHZhEIg5quQSNevezufIqmpASrvboadaklFCIOGD/+SpsPnQJe89X4rpRcZiTISwI7EhaVKDg47arIJUMkRo5zpU7n75Y3qBHVZPBZeYVH5hzt/4ZAOuTe2dPN/kMic4dQ8+VNYLjvLdA9aj2wNyRwlrsPV+JC1XNWDYuwTq1zxG1XILMWC0OFdTYra3SGf/ZuvsUMTNGgzYTs2aK2uMtgTmO43DHlCTr/wvNauODNqec3MDyHVmF1niMaK+PWuZGYE4q5nqUOdbfqboUfTYYzThyqRajE4Mh7WER5+HxQZiRFo6vT5Yir/2Y1htN1mBtphuZpF2tGG/JmlNKxS4btRDP/enqNHAcsG73OQCWbBWNQoKZ6f3vJva3FqqW43+vHYKkUJVHHd4zYjQoqW9FbXP3Rj81zQZUNuqRFulZuQ1y5WTGaGEwmW0aQPhj44fOAhVSfHDHOExKCcWOE6W4WN2MP0wdgP2rZ1lr+go1KSUUrW1ma/MoAPjuTDka9UYsHdU7DwY6Z5r1pPHDlWKvM2ubyYzsvCr8ZcdpvJmVD6D3yjm4qjFX1mB/una4Wg5Z+3WCp39vX0KBOWJXm8mM1jYz1B4UIuXbPp8osn8zVMi3wBZQIDwxRAWD0dztJoifMrvEgyewfMacuw0ganUGl9M8LIE5dzPmXHfI9AX8l0NJnfMb2NyKJiSHB7jsxqMRkJXYpDeiuK5FcH05XpBKhmFxQdifW4nnvz6DGK0Cz1zb8/o7vS0tSoPc8iaYnAR5+CL4fLdKR/iiuPbqn12q1nWbUsYYw77zlUgMVTkNbgTZyZg7V96IxBBVj4OTfSU5TA2NQoKjl+vw7o8FEIs4rJyY6NbvThgQgvqWNpeZjqdLG6CQipDsZlZFRnugIsdJnbl661TW/n/uWTQi1nqO9GQqK+D4IRHgecZclPWBg+OprAajGQcLajAyIdhlsNabqeS2tWVOFtdBbzT3WjDyodkDwRjwzz15MJrMeGjLURwurMUtExI9mhK5cHgMNAoJEkNVgoruE2HSogKxZEQs9udW4YOfLuJ0aQPmD4sWNP3Ply0eGYsf/memR9eA/IOaM+115vRGE74/U4639+bjiW2WYvHe+sDLl/HXX52/n0vbr41jtP6bvauSSbDxtrF45abh2Ld6Jp6Yn9GjBg3XDI2GRMRh7Y7T1lIW244UQ8QB13qQhWcPn22dHhXYL4OqUe3HU+dr+TWfncDyDQex4ccCtJnMuG1Sksf1srtyNpW1tc2EOl2b3QZmIhFnzdrz98YPAOC7V4qkR/iB5UnGXEq4GkqpGCeL6+z+nJ/iKmRqYUJ7EK+wWmcNqDHGsP1YMcID5R5F2fmTqrsNIMrqW10GEy1TWV3X+gI6mkn4cldWoCMwV9bg+HNu1htRVNviVtq6kOBnfoVnjR86mzYwDMfaa8FsWDnGmlHWn6RFqrHvfCUKq5sdBnD44ISrqV9KmRhhark1k6izP316DLkVTTj4+GzrzdXFah2K61qwYoLz6b0qmRhSMYfLNTowxmAwmVFQ1YzZ6RHuvMV+SSTiMCIhGNl5VTCZGRYOj7Gen1yZkByKt/ddwMGCaoeFiBljyClpQHqUxu2MT+uFf0k9gHi7y9RdoeYPV4JCKsZDs1Lx3k8XkSJwypelpo8MOW4E5oTWYorU8DXmHD9wOF5UB53BhCmpvv0UuOuTcr6Lam9N37XU3QnDV8dL0NDShqxzlbh2eAyevXaIR+tTysT496rxkPj5lJnfwiNzBuHL4yV49qscAB0NwkjPdG4AkRmnxZ3v/4pDnWqWhqnl1lIcpP/I7JTFfWP79zPfObM/Bnd+SwqpGEt6ofYbYKnb+PDsgfjbt+fxyre5+MPUAdh7vhJTB4YjQmATBUeCVVKsnJjY4+6/V0qUhs+Ys1zjmM0MWecqkBIegNeWjURGtOuGYkIo2zPnW9q635/xwcEoB5/90LggcBzXo3s1X0GBOWJXszUwJ/zJplhkqaNwoqgeZjPrNl+crxUXH+L+jRA/vfFSTbM1on7sch0Kq3VYNWWAR9MUhWTM6Y0mVDUZXHaLCVRI0NJmQpvJ7HIKj7Vgvo9PpeE78DjLmMuvdD+AFqiQoKrKfiOQrnKtdes8P9nPHhyJ1/bk4bZJSZjUT2+w06IsF+nnyhodBuZySuohEXFuPUWPD1Hick33cXG+vAn1LW3Yf77KOp33x1xL11pXNwEcx+HqIVH4+kQpPjp4CSMTgmAyM2uNPG81Mj7I2rl31ZQBbv/emKRgiDjg4IUa3D7Z/u+VN+hR02zA3Mwot9cbG6SEVinFKSeFweuvUPOHK+W2yQNwm4PPyBlLTR8tfsqvdnhOvlyjQ0SgHAqpsO+6cLUcHOc8MPdjrqW+3ORU334KzD/A25hdgPIGPX4trIFMLMLwXmwE8sicgdifW4Wsc5WYMzgSf7txeI+KbfdmkxLiWEKoCsvGJWDTgULEBin77U2stxkSbQnwZOdVYfuxYpwqbsDy8QlYPDIWyWEBCAmQUTZoPxQbpESQSmqdwQAApb1c64tY3DsjBd+dKcf6ffkob2iF0cx6bRorYLm+eG5RZq+tr7d1LSOUX9mEWl0brhsV51YzMaFEIg4Kqchuxhw/ndZR8Hnd9cNgNDM6Z8HDqayMMbzzzjsYM2YMlEoltFotpk6dik2bNtldvry8HA888ABSU1OhVCqRnJyMNWvWoLHRfmaR2WzGxo0bMWbMGGg0GoSFhWHx4sU4fPiww306duwYlixZgpiYGKhUKowcORJvvfWWTfvlzhoaGvDkk08iPT0dSqUScXFxuPfee1FRUSH8A/FBTXo+MOfZjdvQ2CA0thqt01Y7u1SjQ5haLmhqD1+7qrC6Y338NFZPn8BqFFIEyiVuZcyV11umK9lLw+0ssD2bqsmNjK5anQEc5x1ZKz1hzZhz0mTjfLklgDbIjQBasEqGWl0b6nWua6DlttfxSI3wPPgzPD4I3z06HU970BXpt8IHt06XOg7GnCyux6DIQLcCEAkhKlQ16W3antfpDNaAzo4TJdbX9+VWQSzi3EpBf2HJUMSHKPHcV6fx+WHL+B3k5YE5vj7QqIQgQTf7gQophsRocbCg2mGdOaGNH4COAtOnSxocTm2ub8/W9fVzD9Be08doW9Ons6LaFo9qrEjauxs7C8xl51VBLZdgWFyQ4PV7k+SwANw9LRlijsPG7AKcKKrHiPggwcFOZ0YnhmDZuARcOzwGr988sse168hv58FZqQhTy7FyYqLfF/buLVqVFLFBSmSdq8Sp4gb8cc4g/GVxJsYmhSBULacb3H6K4zhkxmhxurQBxvYGENZsIj+eynolSMQi/O3GEZCKRdh2tBgBMjGuznD/Iae3i2jP6ueDYnyzsbFXsN6tSiaxG5jjZ0w5uodWSMUezdDzRR5d2Tz44IO46667cObMGUybNg2TJk3C0aNHsXLlStxxxx02y5aWlmL8+PF44403oFKpMH/+fJhMJvz1r3/F5MmT0dDQ/Uby3nvvxapVq3DhwgXMmTMHaWlp+OKLLzBx4kT897//7bZ8VlYWJkyYgC+//BKDBg3C1Vdfjfz8fNx77724/fbbuy3f2NiImTNn4vnnn4fRaMSCBQugVqvx1ltvYdSoUSgqKvLkY/Ep1ow5gV1ZeR115uq6/exyjU5wC2y+dhUf6GszmbHjRClSwgNc1sxyJjZY6VbGHF+cNdrFF6em/fNyZ6plTbMBWqXUZU01bxemlkMi4qzp1PbwherdCaBdPSQKJjPD50dcj9PciiZIxRySPGi53llqhLpf31AMigxEmFqGzw8X2e3GWdHYivIGvdtjJb69yH5Rp8YoFzsFxb89XY7W9szQA/nVGBEf5NYUX61SijdvHg3Akl0DwOsz5sYPCMHiETF4Yr7wwO2E5BDU6tqsmZ1d5bRnvQl9upkZo0VLmwkFDjJL671oKmtP8VOHcoobwBjDF8eK8cwXp1CnM6DFYEJVk15w4wdelEbhsMZck96IY5frMCE5xOeDSCIRhz/PG4zs/zcLXz80BWvmpuOpK/Ag48WlQ/HaspG9GvAjV16ERoFfnpiNu6en9PWu+JTh8ZZz29MLMvDwnIEUjPMSQ2I1aG0z40JVMwqrm/FTfjWCVFIKTFwBqRFq/M/v0gAAczOjvbaesSfkEjHC1DJr4PeX9qnuVzJrWSkV2+3KWtae3BLZS9OIfZngq8WdO3fijTfeQHx8PM6ePYvdu3dj586dOHPmDOLj4/Hee+9h586d1uXvv/9+FBYW4vHHH8eJEyfw2WefITc3FzfeeCNOnjyJp556ymb9X375JdavX4+hQ4ciNzcXW7duRXZ2Nj777DOYzWbcdttt0Ok6bhD1ej2WL18Oo9GIHTt24IcffsD27dtx7tw5pKen44MPPsC2bdtstvHMM8/gyJEjWLlyJc6ePYtPP/0Up0+fxh//+EcUFxfj/vvvF/qx+Jzm9oy5QA+/KIa2B+ZOdmkA0aQ3oqrJIKi+HGCJpkdpFLhUbalP9cq351HdbMDiEbE9uhiJCVKipL7VZWdE/sTmOmPO8nk1uNGZtba5DSE+Xl8OsNy0RWoUNp2Busotdz+AtmBYNIJUUnx4sNBhRqx1vRWNSA5T+3zwUyYR4c6pySipb7UbsOQLDbuqL8fjp5lf7pTxWlhtCfKMSghCs8GErLMVOH65Do16o6AaWkPjtHhqoeWmXSYWIUlAE5j+SCEV49Xfj8RoDzrrjR9gyTI8WFBt9+enSxog4oA0gUW8+fpDOQ66kda1tEEhFflFgIMPRu/LrcS9Hx7Bwx8fwwc/F2LBP3/E7hxLZ09Pu5JFauSoaLT//XGooBpGM/OrLmMcx2FIjBb3zkixXgMQAoCCRlfAswuHYOt9k3CHgBIKpO/x12Gf/noZ1/3rZ1Q0tuKJeYP7eK981x2TB+Cv1w/Dmrlpfb0rv7koreXeizGGQwU1GBihvqINB1Uysc1MG16Zm8ktxIPA3IcffggAeO655xAf31FYOj4+Hg888AAAWANzeXl52L59O+Lj4/G///u/1mVlMhnWr18PjUaDd955B01NHdkCL7/8MgBg3bp1CA3tmBp13XXXYfny5SgtLcXHH39sff2jjz5CaWkpbrjhBlxzzTXW16Ojo/Hmm28CAF555RXr6w0NDVi/fj1UKhVeffVVSCSWQIpIJMK6deuQnJyML7/8Evn5+UI/Gp/S04y5AaEBCJRLcKJL0e3L1vpywjMUEkJVKKxuxmvf5+HNH/IxPD4It/fwgiQmSAGD0YyqZsed9YBOGXMuCoTzU1ndypjTGRDs4x1ZedFahdOprEICaAqpGDeMjsOFymb8nG8/oAFYOgQW1bYgtQf15bzJigmJCFJJ8UZWHtpMtllzfD2TIW4H5izj0zYwZ/nv+2akguOAr06UYH97Da1pg4QFH1aMT8CqKQNw8/gEnw+aOjN2QAg4DjhwoftxXK9rw4miOkszHYFPea1ZYg7qzNXp2vwiWw6w1PQJVkmx40QpduWUYf6waDy7MAMVjXo88p9jAIC4YM8y5pLD1WgzMbz5Q163n2XnWf6mUwb6T2COEPLbidAoMCpB+AMh0rcy2zPg39lfgIaWNry5fBRuGGO/URPpOZGIw41j4nut6YM3idIoUd7Qiss1LSitb72i01gBPjBnv8aciAPC1P5xz9sTgu+I3n//feTk5OCGG27o9jM+wMYHu3bu3AnGGObPn299jafVajFz5ky0tLRgz549AID6+npkZ2dDrVZj9uzZ3da/ZMkSAMCOHTusr3399dcAgMWLF3dbfvr06QgODsaPP/6Iuro6AMDevXvR3NyMadOmITjY9gtNLBZj4cKFNuv1V8096MoKWE6EmbFa5BTX29Q5uuRBR1ZeYogKDa1GvPLdeWTGavDv28f1OPU7NsiyH84aEwDCM+aa9M4Dc4wx1DYbfL4jKy8lXI3qZoM166ozncGIyzUtgho03Dw+EQDw4cFCh8tcqGwGYz3ryOpN1HIJVk0egKLaFmv9Rd7J4nqIRRwyooVNZb3UqQEEH5gbOyAE45JC8P2ZCuzOKUOgXILhAmtocRyHpxZkeNxV0VdolVJkRGtwqKAGjDE0643Ye74SD205irEvfIeS+lZM9qDhyIDQAATIxDYFpjuratL7TWCO4zjMTItAkEqKfy4biTduHoXbJg/AZ/dMtHZiFVpagffArFQMjtbg5f+ex+aDl2x+lp1XhfBAud+cfwghhLiWEKJCcPvU1ffvGIu5mdF9vUvER0VrFTCaGXaeKgXQe53SHVE6CMyVN7QiIlDh1w/i3SX4E5JKpcjIyEBAgO30o59//hlvvPEGxGIxli9fDgDIybG0R8/MtN+1JCPDMp3p5MmTAIAzZ87AbDYjPT29WyDP3vKutiESiZCeng7GmHU5ofvkr/iprJ5mzAGWOnPNBhMKqjoyIi/3JDDXfvOUHhWITXeMh7YXOgryXZBc1ZkrqWuFRMQhTC13uhxfZ6u6yXkGXqPeCKOZISTAP26Orx5i6eC581RZt5/lV1iCdQMFNGgYEBaAqQPD8N+cclQ4KL7OF3sXsl5vd+vkJAQqJHjzh3ybgHhOSQNSw9VuT12M1iogFnG4XGs7lTVYJYVWKcXC4THQG804W9aIiSmh9GXbA+MHhKKqyYCrXtmHoc/uxq0bD+HL4yUYER+El28Yjj/PSxe8TpGIw+BoDXJKGrpN984pqUdRbYtHU2+91V+vH4bDT16FhcNjrK8NiwvCjgen4LVlI61TioXSKKT44I6xSAxV4cntJ/HFsWKcKq7HJ79cxtmyRkxOCaUpfIQQQqxEIg6bVo3H1w9N8atSB+S3x3dB/eKYpWHble6KHSCT2K0xV1rf6rAjK7HV47upm2++GSNHjsSkSZPAcRy2bNmC0aMtxb1LSiwHQnS0/acB/Ovl5eUeLf9bbaMrvV6PhoYGm396vfNAjLdpMvSsxhzQUWfu+OWOrA0+Y86TDIVFI2Jxx+QB+PDO8b02BZTPmCiudR6YK2toQaTGEqxwhm968bOdqWmd1TZbuiL6y1TWKQPDoJZL7Abm+ACaOx1ZO1s+PhFGM8PHv1y2+3O+oL7Q9XozjUKK2ycPQEFVs7Vzak2zAcV1Ldbpje6QiEWIDVLaTmWt0SGhvR7cNZlR1rEwdVB4L74D//O79qB1Y2sbrs6Iwv+7Jh1Zj83AJ3dPxPWj4yCXeFYHLjNWi/qWNpsGHgDw6a+WGoTXj/afqTMSscjuuTs4QIZrh8e4PK87ExGowKY7xiNULcfDHx/Dgn/+iNWfnwAAXOVHHeAIIYS4JzNWi0Qvr69L+j9+ltfp0gbEBikR46IcU08pZWIYTGZrx2HA0qyxskmPKD+cSuyJHs0DrK6uxpYtW6z/z3EcTp48iaVLl0IsFqO52ZIJo1LZD8IolZYDhJ8C6+7y/HJCfkfoNjrXvevqxRdftKmZBwBr1qzB6tWrHf6Ot6lpsHxOxtYm1NS4bmRgT0J7POSX/HLMHGD5vPPK6iGXcBC3NaOm002/OwIAPDA5CjA0o6bGfrdBoVSwBFQvlNeipqbG4XLFtTokBiudLgNYBlR6ZAD2nqtAZVW1wxu+wjJLMEoBo8t1+oqpyUHYeaYKORdLEa3pyDw8eakSABCuMAn6LEZFSRGhluHDny9iSUYQAuS2AYzTRTWQiDhoRHqnx3Btba3Ad9K/LcnQ4t39Yjy+9STMhhbI2rPZkoMlgj7fqEApTpU0orq6Gi1tZlQ26jEmLhA1NTXgAIxN0ODAxXoMDRe2XmJrYBCHnx8dD7mk83MyPWpqevawJ1FrGQ8HzhcjYJAlI6zNZMb2o0VIClEgSW22/t18bQz81tQc8OYN6fjPkTKEBkiREqZCapgKSaFyGhtegsYAIR1oPBB/5wtjIIDruPcZERtwxa9HxMxSxqm4osqa2FPWoAdjQLACfn09FBLiXrZijwJzarUa5eXlUCgUyM7OxkMPPYS1a9eitLQU77zzDsRiy42Bq6kcZrMlsipkecYYOI6DWCyG2Wy+Ittw5M9//jMeffRRm9fkcjnkcufTHL2JkbMcGnGRYdaGBkIFBzNoladwvqrVekCWNbYhPiQAYaGeTR3qbdogBrGIQ3ULczhoWttMqNUZMTlV7dbAmpMRjdez8nBZJ3JYmNdUYTl5xYYHuT1Yvd2i0YnYeaYKB4pasWpKR8bq5fp8SMUchifHQCpwSuSd05LxwjdncevmHLy1YjQGdepeWVirR1JYACLDXU8V8KW/QQiADbeOxX0fHcaj285Z68qNHxgt6H0mR2hwqLAenCIQjQbLdOGBMcHWdbxw3QicLK7HiJTYXn8PpOemDpYCO/PxZU4Nrh+fCo7jsPNkKepajLh7eqpNcyXAt8ZAXwgJCcHYQXF9vRukB2gMENKBxgPxd94+BgaZ5QBOAwCmpAm7B/BEcGAAgEooAjQIac+QK2i0BDiTIvznfrcnejSVVS6XIyIiAhqNBtdccw127doFlUqFjRs34sKFC1CrLSlTLS32pwnyr/PLubt8QECANbB2pbbBL2ePXC6HRqOx+edLQTmgo/lDgMzz2C3HcRgWp0VOSQOMJjNMZoai2haP6stdKWIRhyiNAsVOasyVt9cwczcFeGa6ZWrfD2crHC5T0z6VNcRPmj8AwPRB4VBKxdh5stTm9dyKJgwICxAclAOAP0xNxpPzB6OwWodFr2fjs8NFyK9sQm55Iy7V6Py28PrElFB8+cAUpEdZ6oxxHDDYzcYPvM6dWfmmHYmdxm5yuBqLRlBQrr9KjQjEjWPisD+3Cp/8apnu/cmvlyHigKWj6O9GCCGEEOKrOk8fvdL15QDLVFYANg0g+HtoqjHnnl6t2J2SkoJJkybBbDbj2LFjiI21XPyXlXWvKwUApaWWG3S+rpvQ5T35HU+24Y+a9CYEyMQQ9aD2DgAMjdVCbzTjjg9+xQvfnIHBZO5XgTnAUmfOWfOH0vaOrO7Ojx8RH4wglRRZ5yodLlOr42vM+UfzB8Bywp6VHoFfC2utJ+oWgwmXa3UYGOlZgwaO43Dn1GR8fNcEBCokeOzT45j9t7246pV9MDN4vF5fEB+iwuf3TsTN4xOwckIiAgTWi+QDc5dqdLjY3pE1Kax/jV3i3BPzMxCpkeMvO87g2OU67D1fiRlpEYikWh+EEEIIIT5LKRNDq5QiNECGlPArX9NQJeUDc0bra0Lvof2d4HSoxx9/HHl5eXjvvfe6dWYFYM0ca2trs3Y+PX36tN118R1Shw4dCgAYPHgwRCIRzp49C7PZDJFI5HR5wNJd9dSpUzh9+jTS022715nNZpw9exYcx2HIkCHW5YXsk79qNph61JGVd+2IGPxwrhI/5VVh33lLoCqln2UxxQYrcehiDXQGI1R2MgRL6y1BO76DqytiEYdpA8Px5fESVDRaWkR3xWfMBftRxhwAzM2MwtcnS7E7pwwrJyYhr6IJjAGDetg5dWxSCHY8NAWf/lqEFoMJZsYgEYuwYkJCL+25d1LJJHhhiWfnsvhgS4bo5VodLtdYxkBCCBUr9iZapRQvLh2KO97/FSs2HISZATeMpumWhBBCCCG+7pE5A6GSiX+TDvF8xlznzqxl7ffQ0dor23jCVwiOvHzzzTc4fvw4Fi1ahOXLl9v8rK6uDgcOHAAAjB49GlKpFBzHYceOHXjllVes9d0AoL6+HllZWVCpVJg+fToAWP87KysLP/zwA2bNmmWz/m3btgEA5s2bZ33tmmuuwccff4zt27dj6dKlNsv/8MMPqK2txeTJkxEUFAQAmDp1KgICAvDDDz+gvr4eWm1Hp0KTyYSvvvoKHMdh7ty5Qj8an9KsN0Hdg46svPQoDb55eCr0RhNyy5tQVKvDjLSIXtjD3sMH3ErqWpBqJ0BkjfYLOKnMSLME5vadr8L1dm6E+Yy5ED/pysqbmR4BuUSEzw8Xobi2Bf9pn2KXGStsmqU9EYEK3D8ztcfrIRYJ1qmsLSisbkaATIwwtX8dr75gVnoklo6KxdYjxQhWSTF7cGRf7xIhhBBCCLnCbp884DfbFp/c0tw5MNdgaWQWofGtkl9XiuCprPfccw8A4LHHHkNubq719draWqxYsQLV1dVYvHgxUlNTkZiYiIULF6KgoACrV68GYwwAYDAYcPfdd6OxsRF33323TXDswQcfBAA88MADNtNNt27dis2bNyM6Ohq33HKL9fWlS5ciJiYGmzdvxtatW62vl5WV4YEHHgAAm26pKpUKq1atsm7bYLAESBhjWL16NQoKCrB48WIMGjRI6EfjU3QGE9QeNn2wRy4RIzNWi7mZ0VBIxa5/4TfE144rrLbfJba0rr3GnID58dMGhYPjgKxz9uvM1TQbIOIATS9+xt5ALZdg+qBwHC+qx9v7LiBEJcPaxZmYld6/grXEEjRWycTtNeZ0SAgN+E2euJHe98yCIUiPCsSdU5Mhk/RqBQtCCCGEEOLnVNaMuY6prGX1LQgJkPW7e//+SnBK1F133YWsrCx88sknGDp0KKZMmQKpVIqDBw+itrYWo0aNwsaNG63Lv/766zh8+DD+/ve/45tvvkFmZiYOHTqES5cuYfTo0Xjuueds1r9kyRLccsst2LRpE9LS0jBr1ixUVVUhOzsbMpkMmzdvtmm0oFar8e677+Laa6/F9ddfj8mTJyMsLAx79uxBQ0MD7r77blx77bU221i7di327NmD//znPzhw4ADGjh2LU6dO4ezZs0hKSsIbb7wh9GPxOU0GE1J7IWPOGySFWqbn3b3pMKYPCseikbGYlxkFSXszgtL6VkhEHELV7kf7w9RyDIsLwr7zlTCazJCIRSitb8GJonrkljfi+OV6BKtkPa7h540emj0QwSoZFgyPxuSUML/8DLwBx3GID1Yhv7IJZQ2tGBandf1LpF/SqqTY9ci0vt4NQgghhBDig+w1fyitb6X6cgIIfnQuEonw8ccfY8OGDRg+fDh++ukn7N27FwkJCXjppZeQnZ2N4OBg6/Lx8fE4dOgQ7rzzTtTX1+Orr76CQqHA448/jj179tjtfvr+++/jn//8J5KSkrBr1y7k5eXh2muvxYEDBzBjxoxuy8+dOxfZ2dmYN28eTp06he+++w6pqanYsGED3nzzzW7LazQa7N+/H3/6058AAF999RUMBgPuu+8+/Pzzz37f+IEx1mtTWb3BxORQ/OP3IzApNQxZ5yrw0Jaj+Mf3HdmgpfUtiNQoIBYYQJqZFo7GViOe/jIH177+Iya+uAd3bzqMl/97HmUNrZg2KLy334pXyIzV4v+uH4apA8MpKNfPxYeoUFrfCsaAxFCqL0cIIYQQQgixFdA+lZUPzJnNDBUNeurIKoBHkReO47Bq1SqsWrXKreVjYmLwzjvvuL1+kUiEBx54wDoV1R1jx47Fjh073F4+KCgIL7/8Ml5++WW3f8df6AwmMKBXmj94A5GIw6IRsVg0IhaVjXosev1HfHGsBI9eNQgcx6GsvhUDwoQHJWamReDV73Kx+eAlBCokWDIyFhOTQzEoKhCpEWq/CXwS7xUf0lFXMTGUOrISQgghhBBCbHVt/lCjM8BgMlNgTgCKDJBumvSWueH+GDgKD5TjqoxIfPBzIc6XNyExVIXqZgMmpYYJXtewOC1euWk4glUyTEoJo9pOxOvEB3cE4ygwRwghhBBCCOlK1WUqaxnfPJGmsrqNIgWkm8ZW/w3MAcBVGVEAgP/mlKG8wXJSifYg2s9xHJaMjMOMtAgKyhGvxHdmBWgqKyGEEEIIIaQ7a2CuzRJHyK9sAkAP9oWgaAHpxpox5ydTWbsanxyCQIUE354pR0md54E5QrxdfHtgTiYRIZqeeBFCCCGEEEK66DqV9UxpIwBgcLSmz/bJ21BgjnTT5OcZc1KxCLPSI3CiqB5HL9cCoMAc8U9xwZYac/HBSmrUQQghhBBCCOlG1aX5w9myBsjEIiR7UKfdX1FgjnTTpG8DAAT6acYcAFyVEQkA+OjAJQBAtFbpbHFCfFKAXILRicGYOtA/OwgTQgghhBBCnFNK+RpzlgSfM6UNGBiphkRM4SZ3+W/khTjUpLdEuv01Yw4Apg8Kh1TMobiuBQBlzBH/9fm9k/p6FwghhBBCCCH9lFjEQS4RQWcwoabZgPIGPT3YF4hCmKSbQIUEmdFqhAfK+3pX+kygQopJKZZOrBIRhzC1/34WhBBCCCGEEEKIIyqZGDqDCWfLGgAA6VGBfbxH3sV/U6KIQ78bEoWx0TKEhAT19a70qasyIrH3fCUiNQqqr0UIIYQQQgghhNihkknQYjBZGz9kUOMHQShjjhAH+DpzMUE0jZUQQgghhBBCCLHHkjFnxNnS9ow5CswJQhlzhDgQqVHg6QUZGBBO3WQIIYQQQgghhBB7VDIxKhv1OFvWiEiNHCEBsr7eJa9CgTlCnLhjyoC+3gVCCCGEEEIIIaTfUsrEaGw1orrZgAnJoX29O16HAnOEEEIIIYQQQgghxCMqmQSNeiMAID2aGj8IRTXmCCGEEEIIIYQQQohHlDKx9b+p8YNwFJgjhBBCCCGEEEIIIR5RSTsCc+lRFJgTigJzhBBCCCGEEEIIIcQjqvaMOZlYhGRqnigYBeYIIYQQQgghhBBCiEeUMkv7gtQINaRiCjMJRZ8YIYQQQgghhBBCCPEInzE3mOrLeYQCc4QQQgghhBBCCCHEIx2BOerI6gkKzBFCCCGEEEIIIYQQj8QFqwAAoxOD+3hPvJOkr3eAEEIIIYQQQgghhHinqzMisX/1TMSHqPp6V7wSZcwRQgghhBBCCCGEEI+IRBwF5XqAAnOEEEIIIYQQQgghhPQBCswRQgghhBBCCCGEENIHKDBHCCGEEEIIIYQQQkgfoMAcIYQQQgghhBBCCCF9gAJzpBu9Xo/XXnsNer2+r3eFkCuKjnXi72gMEH9HY4CQDjQeiL+jMUD6CscYY329E6R/aWhogFarRX19PTQaTV/vDiFXDB3rxN/RGCD+jsYAIR1oPBB/R2OA9BXKmCOEEEIIIYQQQgghpA9QYI4QQgghhBBCCCGEkD5AgTlCCCGEEEIIIYQQQvoABeZIN3K5HM888wzkcnlf7wohVxQd68Tf0Rgg/o7GACEdaDwQf0djgPQVav5ACCGEEEIIIYQQQkgfoIw5QgghhBBCCCGEEEL6AAXmCCGEEEIIIYQQQgjpAxSYI4QQQgghhBBCCCGkD1BgjhBCCCGEEEIIIYSQPkCBuT5mNpuxfv16TJw4ERqNBgqFAmlpaVizZg3q6uq6LX/hwgWsXLkSiYmJUCqVGDx4MF588UW0tbW5tb1Vq1aB4zgYjUa7Pz969Cg4jnP4b8KECYLf47Zt2zBlyhQEBwcjKCgIV111Fb7//nu3freqqgqRkZFYsWKF4O2S/oWO9e6ysrIwd+5cBAUFQaVSITMzE2vXrkVLS4vgbZP+j8aArdraWqfbj4qKErx90r/RGOgwY8YMp9vm/z377LOC94F4BxoP3e3YsQOzZ8+GVquFRqPB+PHjsWHDBphMJsHbJt7BH8ZBZ5s2bQLHcfjuu+9cLrt27VpwHIe8vLwebZN4B0lf74A/M5vNuP7667Ft2zaoVCqMGzcOAQEBOHToEP76179i69at+PHHHxEZGQkAyMnJwdSpU1FbW4vx48djzJgx2L9/Px5//HF8//332LVrFyQSx3/S1157DRs3bnS6T0eOHAEAjBkzBmlpad1+npqaKug9vvDCC3jiiScQEBCAWbNmQafTISsrC99//z02bNiAO+64w+HvNjc3Y+nSpaioqBC0TdL/0LHe/Vj/9NNPcdNNNwEAJk6ciPDwcBw6dAhPP/00Pv/8c+zbtw8ajUbQPpD+i8ZA9zHAbz89PR2jR4/utr6goCBB2yf9G40B2zFw1VVXIS4uzu56mpqa8MUXXwAARo4cKWgfiHeg8dD9O+Gpp57CX/7yFwDAkCFDkJKSguPHj+MPf/gDtm/fjk8//RRKpVLQPpD+zR/GQWc//fQT7rvvPreW3bp1K5577jmPt0W8ECN9ZsOGDQwAS0tLYwUFBdbXGxoa2MKFCxkAdsMNN1hfHzVqFAPA1q9fb32tvr6eTZs2jQFgf//73+1ux2g0sjVr1jAA1n9tbW12l73//vsZALZjx44ev79jx44xACwmJsbm/e3fv58FBAQwpVLJiouL7f5uQUEBGzNmjHV/ly9f3uP9IX2HjnXbY72qqoqpVComkUjYt99+a31dp9OxefPmMQBs9erVPd4v0n/QGOh+vl+3bh0DwF5//fUeb5/0fzQGHF/zdHXLLbcwAGzNmjU93i/SP9F4sB0P33//PQPAxGIx27Rpk/X1trY29sgjjzAA7H/+5396vF+kf/H1cdDZxx9/zNRqtXX7na//u3r11VeZVCq1Lpubm9ur+0L6JwrM9aHJkyc7HPiVlZWM4zgmlUqZTqezfmFNnDix27J5eXmM4ziWkJDAzGazzc/27dtnDXAlJye7PBlNnDiRAWClpaU9fn/8heVbb73V7WdPP/00A8Ceeuopm9f1ej17+eWXmVartdlnCsx5NzrWbY/19evXMwDs1ltv7bb8wYMHGQA2dOjQHu8X6T9oDHQ/3y9btowBYD///HOPt0/6PxoD3ceAPR999BEDwEaNGuVwv4n3o/FgOx5uuukmBoA98sgj3ZY3Go0sIyODSaVSVlFR0eN9I/2Hr48Dxhi7cOGC9fhWqVQsMjLSYWDu+PHjbM6cOQwACwsLY4GBgRSY8yMUmOtDCxYsYOnp6ayqqsruz0NCQhgAdunSJfboo48yAOyll16yu+zw4cMZAHb8+HGb1/kA1/Lly1l1dbXTk5HJZGIBAQEsJiam52+OMRYaGsoAsLKysm4/O3r0KAPARo4cafP6tm3bGACm1WrZ22+/zd577z0KzPkAOtZtj3Wz2cwKCgrsZk/wFx5dxwbxbjQGuh/T6enpTCwWM51O1yv7QPo3GgOuz+uNjY0sKiqKiUQiduTIkV7ZL9I/0XiwHQ8ZGRkMANuzZ4/d9d1zzz0MANuyZUuv7B/pH3x9HDDG2KJFixgANnbsWHby5Ek2ffp0h4E5/j387ne/Y4WFhSwxMZECc36Emj/0oa+++gpnzpxBaGhot5/l5+ejpqYGMpkM4eHhyMnJAQBkZmbaXVdGRgYA4OTJkzavz58/Hz///DM+/PBDhISEON2fc+fOobm5GampqXj++ecxdOhQqFQqxMbG4q677kJxcbHb762srAzV1dUICwuz1gXobPDgweA4DqdPn7Yp6KrVarFmzRrk5+fjrrvucnt7pH+jY932WOc4DklJSYiJibFZtqSkBKtXrwYA3HrrrW7vA+n/aAzYjoHm5macP38eycnJ2LhxI0aPHg21Wo2IiAgsW7YM586dc3v7xDvQGOh+zdPV888/j7KyMtx5551UW87H0XiwHQ9msxkAHNbW5euGnTlzxu39IP2fL48D3rBhw7B582YcPHjQ4b7zpkyZgm+++Qa7du1CQkKC4G0R70aBuX7q8ccfBwAsWLAACoUCJSUlAIDo6Gi7y/Ovl5eX27z+0Ucfud09hi92uW/fPqxduxYxMTGYPn06DAYD3nnnHYwePRqnT592a12u9lculyM4OBh6vR719fXW12fOnImXXnrJ7gma+CZ/PdY7e+GFFzB9+nQkJSXhxIkT+POf/4yHHnrIre0T7+ePY+DYsWMwm83Izc3Fww8/DI1Gg5kzZ0Imk+Hjjz/GmDFjsHfvXre2T7yfP46BrmpqavDaa69BKpXiySefdGu7xDf543hIT08HALvnfcYYsrOzAQCVlZVu7QPxft4+DnjPPfccli1bBo7jXC77+uuv45prrhG0fuI7KDDXD73yyiv45JNPoFKp8PzzzwOwZBcAgEqlsvs7fJeipqYmj7d79OhRAMDYsWNx4cIF7N69Gzt37sTFixexbNkylJeXY9myZWCMuVyXq/3trX0m3o2OdYstW7Zg3759aGtrg0gkQlFREaqqqoS+LeKF/HUM8NtPSUnBqVOnkJWVha+++goFBQV49NFH0dTUhJtuusm6buK7/HUMdPX2229Dp9NhxYoViI+PF/p2iI/w1/Fw2223AQCeffZZ7Nu3z7qc2WzGM888Y90/vV4v/M0Rr+ML44AQoSgw18+8+uqrePTRR8FxHN59913rEySxWAwALqPtfCq4J1588UXk5uZi9+7dNlPsAgICsGHDBsTGxuLEiRM2X5iOuLu/Pd1n4r3oWO+wa9cutLS04PDhw5g2bRo2bdqEGTNmwGAwuPmOiDfy5zFw33334eLFi8jOzra+bwCQSqVYt24dRo8ejfLycnz22WdC3xrxIv48Brq+9uabbwKAtZwB8T/+PB4WLVqEe+65B42NjZgxYwbGjRuHJUuWYODAgXjppZes5W2kUqknb494EV8ZB4QIJenrHSAWjDGsWbMG69atg1gsxrvvvovf//731p+r1WoAQEtLi93f51/nl/OEVCpFamqq3Z+pVCrMmjULmzZtwq+//orp06fjj3/8o92U8ldffdXl/vbWPhPvQ8d6d7GxsQCAUaNGYceOHRgzZgxOnjyJzZs3W58iE99BYwAQiURITEy0u6xIJMK8efNw+PBh/Prrr1Rv0QfRGLD1008/oaioCOPGjbMJVBP/QOPB4l//+hcmTJiA119/HSdPnkR+fj6mTZuGzz77DGfOnMH69esRFBTk8Xsk/ZuvjYOwsDCP94P4JwrM9QMtLS1YsWIFtm7dCqVSiS1btmDRokU2y8TGxuLo0aMoKyuzu47S0lIAjufd94aoqCgAgE6nAwBs27YNhYWF3Zb7y1/+Yg00ONpfvV6P2tpayGQyl4U4ie+gY931sS6TyXDTTTfh5MmTOHLkCAXmfAyNAffO9123T3wHjYHuY2D79u0AYHMTSvwDjQfb8XDrrbfafRizbds2AHD4QId4N18cBxSYI0LRVNY+1tDQgNmzZ2Pr1q0IDw9HVlZWtxMR0NGBxlHBSb5TzdChQz3el0cffRRLly61e4IBgAsXLgAA4uLiAAAXL14EY6zbv6SkJISGhiIqKgrl5eWorq7utq7Tp0+DMYYhQ4ZAJKLD0B/Qsd5xrL///vu45ZZbcODAAbvbl8vlAIC2tjaP3yPpf2gMdIyBF154ATfccAMOHTrk1vaJb6AxYP+aZ+fOnQCA6667zuP3Q7wPjYeO8VBUVIRvv/0Wly9ftrv9PXv2ALDU/iK+xVfHASGCMdJnDAYDmzJlCgPAUlJSWF5ensNl9+7dywCwqVOndvtZXl4e4ziOxcfHM7PZ7HSbABgA1tbW1u1nkyZNYgDYK6+80u1npaWlLDAwkInFYnbp0iXXb44xdvvttzMA7N133+32s6eeeooBYE888YTTdbz33nsMAFu+fLlb2yT9Ex3rtsf6ww8/zACwO++80+76Zs+ezQCwd955x63tk/6PxoDtGLj55psZAPbwww93W16n07GEhAQGgGVnZ7u1fdL/0Riwf83T0NDAOI5jMTExbm2H+AYaD7bj4e2332YA2GOPPdZt+ePHjzOO41hqaqrL90i8i6+PA3umT5/OALBvv/3W5bKJiYkMAMvNzfV4e8R7UGCuDz3++OMMAIuKimJFRUVOlzWbzWzEiBEMAPvHP/5hfb2+vp5Nmzat2+uOODsZ/fvf/2YAmFarZUeOHLG+3tDQwObOnes0kGDPL7/8wkQiEYuKimJnz561vp6dnc0CAgKYQqFgZWVlTtdBgTnfQMe67bF+6tQpJhaLmUgkYtu3b7e+bjQa2dNPP80AsLi4ONbU1OT2PpD+jcaA7RjYt28fA8CkUinbvXu39XW9Xm+9oZszZ47b2yf9H40B+9c8WVlZDABbsGCB29si3o/Gg+14KCwsZDKZjAUGBrLTp09bXy8qKmJDhgxhANiHH37o9vaJd/D1cWAPBeaIIxSY6yNVVVVMpVIxAGz48OFs+fLlDv/xX1xHjx5lGo2GAWCjRo1i1113HYuIiGAA2Pz58+2eYLpydjIym81s5cqVDACTSCRs+vTpbMmSJSw0NJQBYFOmTBEcKOCfiikUCjZ//nw2Z84cJhaLGcdx7KOPPnL5+xSY8350rNs/1l977TXGcRwDwMaOHcsWLVpk/QIOCwtjv/76q6Dtk/6LxoD9McAvD4BNmDCBXXfddSw2NpYBYOnp6S4f3BDvQWPA8TXPxo0bGQC2atUqQdsi3ovGg/3x8Le//c26/Ny5c9m8efOYUqlkANgjjzwiaNuk//OXcdAVBeaIIxSY6yOff/659cTg6l/nwXju3Dl20003sbCwMKZUKllmZiZbt24da21tdWu7zk5GvPfff59NnDiRBQQEMKVSyYYPH85efvllZjAYPHqvH330ERs3bhxTqVQsLCyMzZkzh2VlZbn1uxSY8350rGc5XD4rK4vNmzePBQUFMalUygYMGMAefPBBVlxc7NH2Sf9EYyDL4fJffvklmz17NtNoNEwul7O0tDT25JNPssbGRo+2T/onGgNZDpf/v//7PwaA/elPf/Joe8T70HjIcrj8v//9bzZ69Ggml8tZWFgYmzlzJtu2bZtH2yb9mz+Ng84oMEcc4RhjDIQQQgghhBBCCCGEkN8UtcMkhBBCCCGEEEIIIaQPUGCOEEIIIYQQQgghhJA+QIE5QgghhBBCCCGEEEL6AAXmCCGEEEIIIYQQQgjpAxSYI4QQQgghhBBCCCGkD1BgjhBCCCGEEEIIIYSQPkCBOUIIIYQQQgghhBBC+gAF5gghhBBCCCGEEEII6QMUmCOEEEIIIYQQQgghpA9QYI4QQgghhBBCCCGEkD5AgTlCCCGEEEIIIYQQQvoABeYIIYQQQgghhBBCCOkD/x+NsTlqG31zswAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 1500x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(15,2))\n",
-    "plt.plot(data['date'], data['sales'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "66ee6610-dcef-4223-9a30-683044e24451",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = eda_plot.ts_heatmap(df=data, date_col='date', seasonal_interval=7, fig_width = 6, fig_height=6, value_col='sales', normalization=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a855862-a8d9-4b16-a507-75cc2e142c3d",
-   "metadata": {},
-   "source": [
-    "### hourly - day"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b1d5c197-5f81-4e7e-aab2-4ee1c43042cd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 720 entries, 0 to 719\n",
-      "Data columns (total 8 columns):\n",
-      " #   Column  Non-Null Count  Dtype         \n",
-      "---  ------  --------------  -----         \n",
-      " 0   Date    720 non-null    datetime64[ns]\n",
-      " 1   HR      720 non-null    int64         \n",
-      " 2   PGE     720 non-null    float64       \n",
-      " 3   SCE     720 non-null    float64       \n",
-      " 4   SDGE    720 non-null    float64       \n",
-      " 5   VEA     720 non-null    float64       \n",
-      " 6   CAISO   720 non-null    float64       \n",
-      " 7   hour    720 non-null    object        \n",
-      "dtypes: datetime64[ns](1), float64(5), int64(1), object(1)\n",
-      "memory usage: 45.1+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "energy_hourly.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "8f83164a-66f3-4e32-83b0-ba31739f0516",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "energy_hourly = load_energy_hourly()\n",
-    "\n",
-    "# energy_hourly['hour'] = pd.to_datetime(energy_hourly['Date'] + ' ' + energy_hourly['hour'])\n",
-    "# energy_hourly.drop(['Date', 'HR'], axis=1, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "853e0c72-667c-42e4-bb5a-0b12958a9002",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>PGE</th>\n",
-       "      <th>SCE</th>\n",
-       "      <th>SDGE</th>\n",
-       "      <th>VEA</th>\n",
-       "      <th>CAISO</th>\n",
-       "      <th>hour</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>11451.34</td>\n",
-       "      <td>11391.68</td>\n",
-       "      <td>2199.00</td>\n",
-       "      <td>68.16</td>\n",
-       "      <td>25110.18</td>\n",
-       "      <td>2021-09-01 00:00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10927.52</td>\n",
-       "      <td>10891.04</td>\n",
-       "      <td>2102.47</td>\n",
-       "      <td>64.95</td>\n",
-       "      <td>23985.98</td>\n",
-       "      <td>2021-09-01 01:00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>10494.04</td>\n",
-       "      <td>10519.43</td>\n",
-       "      <td>2038.21</td>\n",
-       "      <td>62.93</td>\n",
-       "      <td>23114.62</td>\n",
-       "      <td>2021-09-01 02:00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>10498.10</td>\n",
-       "      <td>10363.86</td>\n",
-       "      <td>2006.95</td>\n",
-       "      <td>62.79</td>\n",
-       "      <td>22931.70</td>\n",
-       "      <td>2021-09-01 03:00:00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>10428.09</td>\n",
-       "      <td>10469.98</td>\n",
-       "      <td>2026.70</td>\n",
-       "      <td>63.94</td>\n",
-       "      <td>22988.70</td>\n",
-       "      <td>2021-09-01 04:00:00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        PGE       SCE     SDGE    VEA     CAISO                hour\n",
-       "0  11451.34  11391.68  2199.00  68.16  25110.18 2021-09-01 00:00:00\n",
-       "1  10927.52  10891.04  2102.47  64.95  23985.98 2021-09-01 01:00:00\n",
-       "2  10494.04  10519.43  2038.21  62.93  23114.62 2021-09-01 02:00:00\n",
-       "3  10498.10  10363.86  2006.95  62.79  22931.70 2021-09-01 03:00:00\n",
-       "4  10428.09  10469.98  2026.70  63.94  22988.70 2021-09-01 04:00:00"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "energy_hourly.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "ecc3bc14-7e4c-4bde-9244-b4586c052fb8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x136289e50>]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 2000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(20,4))\n",
-    "plt.plot(energy_hourly['hour'], energy_hourly['PGE'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "260a726d-d52b-48b6-a5ae-c54f9777ee30",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 600x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = eda_plot.ts_heatmap(df=energy_hourly, date_col='hour', seasonal_interval=24, fig_width = 6, fig_height=6, value_col='PGE', normalization=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "sufficient-commerce",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Correlation heatmap"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "driving-edward",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:21.041831Z",
-     "start_time": "2021-07-13T22:35:20.657231Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "var_list = ['trend.unemploy', 'trend.filling', 'trend.job', 'sp500', 'vix']\n",
-    "\n",
-    "_ = eda_plot.correlation_heatmap(df, var_list=var_list, fig_height=6, fig_width=8, use_orbit_style=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "great-marks",
-   "metadata": {},
-   "source": [
-    "## Year over Year Outcome vs Events Time Series Plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "hungarian-johns",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:21.053184Z",
-     "start_time": "2021-07-13T22:35:21.046297Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "holiday = [['12/25/15', 'h_christmas'], ['12/25/16', 'h_christmas'], \n",
-    "           ['12/25/17', 'h_christmas'], ['11/22/15', 'h_thanksgiving'], \n",
-    "           ['11/28/16', 'h_thanksgiving'], ['11/26/17', 'h_thanksgiving'],\n",
-    "           ['7/4/15', 'h_independence'], ['7/4/16', 'h_independence'], \n",
-    "           ['7/4/17', 'h_independence']]\n",
-    "\n",
-    "\n",
-    "holiday_df = pd.DataFrame(holiday,columns=['week', 'holiday_name'])\n",
-    "\n",
-    "holiday_df['week'] = pd.to_datetime(holiday_df['week'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "improved-begin",
-   "metadata": {},
-   "source": [
-    "## Dual axis time series plot "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "moving-version",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:21.677731Z",
-     "start_time": "2021-07-13T22:35:21.055907Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2500x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = eda_plot.dual_axis_ts_plot(df=df, var1='trend.unemploy', var2='claims', date_col='week')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "static-headset",
-   "metadata": {},
-   "source": [
-    "## Wrap plots for quick glance of data patterns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "intended-veteran",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:21.704971Z",
-     "start_time": "2021-07-13T22:35:21.680769Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>variable</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>trend.unemploy</td>\n",
-       "      <td>0.219882</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>trend.unemploy</td>\n",
-       "      <td>0.219882</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>trend.unemploy</td>\n",
-       "      <td>0.236143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>trend.unemploy</td>\n",
-       "      <td>0.203353</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>trend.unemploy</td>\n",
-       "      <td>0.134360</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2210</th>\n",
-       "      <td>2018-05-27</td>\n",
-       "      <td>vix</td>\n",
-       "      <td>-0.175192</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2211</th>\n",
-       "      <td>2018-06-03</td>\n",
-       "      <td>vix</td>\n",
-       "      <td>-0.275119</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2212</th>\n",
-       "      <td>2018-06-10</td>\n",
-       "      <td>vix</td>\n",
-       "      <td>-0.291676</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2213</th>\n",
-       "      <td>2018-06-17</td>\n",
-       "      <td>vix</td>\n",
-       "      <td>-0.152422</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2214</th>\n",
-       "      <td>2018-06-24</td>\n",
-       "      <td>vix</td>\n",
-       "      <td>0.003284</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2215 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           week        variable     value\n",
-       "0    2010-01-03  trend.unemploy  0.219882\n",
-       "1    2010-01-10  trend.unemploy  0.219882\n",
-       "2    2010-01-17  trend.unemploy  0.236143\n",
-       "3    2010-01-24  trend.unemploy  0.203353\n",
-       "4    2010-01-31  trend.unemploy  0.134360\n",
-       "...         ...             ...       ...\n",
-       "2210 2018-05-27             vix -0.175192\n",
-       "2211 2018-06-03             vix -0.275119\n",
-       "2212 2018-06-10             vix -0.291676\n",
-       "2213 2018-06-17             vix -0.152422\n",
-       "2214 2018-06-24             vix  0.003284\n",
-       "\n",
-       "[2215 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "var_list=['week', 'trend.unemploy', 'trend.filling', 'trend.job', 'sp500', 'vix']\n",
-    "\n",
-    "df[var_list].melt(id_vars = ['week'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "suited-percentage",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:35:22.908828Z",
-     "start_time": "2021-07-13T22:35:21.707681Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 2400x800 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = eda_plot.wrap_plot_ts(df, 'week', var_list)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "env_orbit",
-   "language": "python",
-   "name": "env_orbit"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "toc-autonumbering": true
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/ktr.ipynb b/examples/ktr.ipynb
deleted file mode 100644
index 4f04b156..00000000
--- a/examples/ktr.ipynb
+++ /dev/null
@@ -1,8577 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# KTR Example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:05:15.298074Z",
-     "start_time": "2022-01-26T02:05:15.276445Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:05:18.115426Z",
-     "start_time": "2022-01-26T02:05:16.159919Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "pd.set_option('display.float_format', lambda x: '%.5f' % x)\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import orbit\n",
-    "from orbit.models import KTRLite, KTR\n",
-    "\n",
-    "from orbit.utils.features import make_fourier_series_df, make_fourier_series\n",
-    "from orbit.diagnostics.plot import plot_predicted_data, plot_predicted_components\n",
-    "from orbit.diagnostics.metrics import smape\n",
-    "from orbit.utils.dataset import load_iclaims, load_electricity_demand"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:05:18.148065Z",
-     "start_time": "2022-01-26T02:05:18.117382Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'1.1.1dev'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "orbit.__version__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:05:20.474418Z",
-     "start_time": "2022-01-26T02:05:20.105283Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(443, 7)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "      <th>sp500</th>\n",
-       "      <th>vix</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.38660</td>\n",
-       "      <td>0.21988</td>\n",
-       "      <td>-0.31845</td>\n",
-       "      <td>0.11750</td>\n",
-       "      <td>-0.41763</td>\n",
-       "      <td>0.12265</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>13.62422</td>\n",
-       "      <td>0.21988</td>\n",
-       "      <td>-0.19484</td>\n",
-       "      <td>0.16879</td>\n",
-       "      <td>-0.42548</td>\n",
-       "      <td>0.11044</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>13.39874</td>\n",
-       "      <td>0.23614</td>\n",
-       "      <td>-0.29248</td>\n",
-       "      <td>0.11750</td>\n",
-       "      <td>-0.46523</td>\n",
-       "      <td>0.53234</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>13.13755</td>\n",
-       "      <td>0.20335</td>\n",
-       "      <td>-0.19484</td>\n",
-       "      <td>0.10692</td>\n",
-       "      <td>-0.48175</td>\n",
-       "      <td>0.42864</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>13.19676</td>\n",
-       "      <td>0.13436</td>\n",
-       "      <td>-0.24247</td>\n",
-       "      <td>0.07448</td>\n",
-       "      <td>-0.48893</td>\n",
-       "      <td>0.48740</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week   claims  trend.unemploy  trend.filling  trend.job    sp500  \\\n",
-       "0 2010-01-03 13.38660         0.21988       -0.31845    0.11750 -0.41763   \n",
-       "1 2010-01-10 13.62422         0.21988       -0.19484    0.16879 -0.42548   \n",
-       "2 2010-01-17 13.39874         0.23614       -0.29248    0.11750 -0.46523   \n",
-       "3 2010-01-24 13.13755         0.20335       -0.19484    0.10692 -0.48175   \n",
-       "4 2010-01-31 13.19676         0.13436       -0.24247    0.07448 -0.48893   \n",
-       "\n",
-       "      vix  \n",
-       "0 0.12265  \n",
-       "1 0.11044  \n",
-       "2 0.53234  \n",
-       "3 0.42864  \n",
-       "4 0.48740  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = load_iclaims()\n",
-    "\n",
-    "DATE_COL = 'week'\n",
-    "RESPONSE_COL = 'claims'\n",
-    "\n",
-    "print(df.shape)\n",
-    "\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:05:22.098450Z",
-     "start_time": "2022-01-26T02:05:22.067260Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "starts with 2010-01-03 00:00:00\n",
-      "ends with 2018-06-24 00:00:00\n",
-      "shape: (443, 7)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'starts with {df[DATE_COL].min()}\\nends with {df[DATE_COL].max()}\\nshape: {df.shape}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:05:22.682940Z",
-     "start_time": "2022-01-26T02:05:22.652047Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "test_size = 52\n",
-    "\n",
-    "train_df = df[:-test_size]\n",
-    "test_df = df[-test_size:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## KTR"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### KTR - Full"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### zero regression_segments"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:05:25.576880Z",
-     "start_time": "2022-01-26T02:05:25.544271Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ktr = KTR(\n",
-    "    date_col=DATE_COL,\n",
-    "    response_col=RESPONSE_COL,\n",
-    "#     regressor_col=['trend.unemploy', 'trend.filling', 'trend.job'],\n",
-    "        regressor_col=['trend.unemploy'],\n",
-    "    seasonality=[52],\n",
-    "    seasonality_fs_order=[3],\n",
-    "    level_knot_scale=.1,\n",
-    "    level_segments=10,\n",
-    "    regression_segments=0,\n",
-    "    regression_rho=0.15,\n",
-    "    # pyro optimization parameters\n",
-    "    seed=8888,\n",
-    "    num_steps=1000,\n",
-    "    num_sample=1000,\n",
-    "    learning_rate=0.1,\n",
-    "    estimator='pyro-svi',\n",
-    "    n_bootstrap_draws=-1,\n",
-    "    ktrlite_optim_args = dict()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:05:44.783729Z",
-     "start_time": "2022-01-26T02:05:32.503187Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:Optimizing(PyStan) with algorithm:LBFGS .\n",
-      "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_ce583eb84b35032d795f7056cd3c761e NOW.\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:771:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1969:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: \"Using deprecated NumPy API, disable it with \"          \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-W#warnings]\n",
-      "#warning \"Using deprecated NumPy API, disable it with \" \\\n",
-      " ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:52:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/err/is_cholesky_factor_corr.hpp:5:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/err/is_cholesky_factor.hpp:6:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/err/is_lower_triangular.hpp:25:27: warning: 'ptr_fun<double, double>' is deprecated [-Wdeprecated-declarations]\n",
-      "  return y.unaryExpr(std::ptr_fun(internal::notNan))\n",
-      "                          ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/functional:1107:1: note: 'ptr_fun<double, double>' has been explicitly marked deprecated here\n",
-      "_LIBCPP_DEPRECATED_IN_CXX11 inline _LIBCPP_INLINE_VISIBILITY\n",
-      "^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/__config:1046:39: note: expanded from macro '_LIBCPP_DEPRECATED_IN_CXX11'\n",
-      "#  define _LIBCPP_DEPRECATED_IN_CXX11 _LIBCPP_DEPRECATED\n",
-      "                                      ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/__config:1035:48: note: expanded from macro '_LIBCPP_DEPRECATED'\n",
-      "#    define _LIBCPP_DEPRECATED __attribute__ ((deprecated))\n",
-      "                                               ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:781:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:841:12: warning: 'auto_ptr<stan::io::var_context>' is deprecated [-Wdeprecated-declarations]\n",
-      "      std::auto_ptr<stan::io::var_context> init_context_ptr;\n",
-      "           ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/memory:1851:28: note: 'auto_ptr<stan::io::var_context>' has been explicitly marked deprecated here\n",
-      "class _LIBCPP_TEMPLATE_VIS _LIBCPP_DEPRECATED_IN_CXX11 auto_ptr\n",
-      "                           ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/__config:1046:39: note: expanded from macro '_LIBCPP_DEPRECATED_IN_CXX11'\n",
-      "#  define _LIBCPP_DEPRECATED_IN_CXX11 _LIBCPP_DEPRECATED\n",
-      "                                      ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/__config:1035:48: note: expanded from macro '_LIBCPP_DEPRECATED'\n",
-      "#    define _LIBCPP_DEPRECATED __attribute__ ((deprecated))\n",
-      "                                               ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9546:30: warning: comparison of integers of different signs: 'Py_ssize_t' (aka 'long') and 'std::__1::vector<std::__1::basic_string<char>>::size_type' (aka 'unsigned long') [-Wsign-compare]\n",
-      "    __pyx_t_12 = ((__pyx_t_9 != __pyx_v_fitptr->param_names_oi().size()) != 0);\n",
-      "                   ~~~~~~~~~ ^  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:473:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixVector.h:186:39: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "  const Index offset1 = (FirstAligned && alignmentStep==1)?3:1;\n",
-      "                                      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/GeneralProduct.h:239:131: note: in instantiation of member function 'Eigen::internal::general_matrix_vector_product<long, double, Eigen::internal::const_blas_data_mapper<double, long, 0>, 0, false, double, Eigen::internal::const_blas_data_mapper<double, long, 1>, false, 0>::run' requested here\n",
-      "          <Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(\n",
-      "                                                                                                                                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:377:31: note: in instantiation of function template specialization 'Eigen::internal::gemv_dense_selector<2, 0, true>::run<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>' requested here\n",
-      "                           >::run(actual_lhs, actual_rhs, dst, alpha);\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:355:14: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::DenseShape, Eigen::DenseShape, 7>::scaleAndAddTo<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>' requested here\n",
-      "  { Derived::scaleAndAddTo(dst,lhs,rhs,alpha); }\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:351:5: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl_base<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::internal::generic_product_impl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::DenseShape, Eigen::DenseShape, 7>>::scaleAndAddTo<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>' requested here\n",
-      "  { scaleAndAddTo(dst, lhs, rhs, Scalar(-1)); }\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:178:37: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl_base<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::internal::generic_product_impl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::DenseShape, Eigen::DenseShape, 7>>::subTo<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>' requested here\n",
-      "    generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());\n",
-      "                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:473:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixVector.h:187:39: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "  const Index offset3 = (FirstAligned && alignmentStep==1)?1:3;\n",
-      "                                      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 12 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 10 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 12 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:45:13: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:322:21: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>::operator/=' requested here\n",
-      "      if (rs>0) A21 /= x;\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 10 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 12 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:45:13: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:322:21: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>::operator/=' requested here\n",
-      "      if (rs>0) A21 /= x;\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 6 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SolveTriangular.h:182:11: note: in instantiation of member function 'Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>::operator=' requested here\n",
-      "    other = otherCopy;\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:353:72: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2, Eigen::Dense>::solveInPlace<2, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);\n",
-      "                                                                       ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, 4, 4, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 4, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, 4, 4, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 4, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, 4, 4, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 4, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, 4, 4, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 4, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, 4, 4, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 4, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/SelfadjointProduct.h:126:59: note: in instantiation of member function 'Eigen::selfadjoint_product_selector<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, false>::run' requested here\n",
-      "  selfadjoint_product_selector<MatrixType,DerivedU,UpLo>::run(_expression().const_cast_derived(), u.derived(), alpha);\n",
-      "                                                          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:354:54: note: in instantiation of function template specialization 'Eigen::SelfAdjointView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>::rankUpdate<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,typename NumTraits<RealScalar>::Literal(-1)); // bottleneck\n",
-      "                                                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:736:51: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>\n",
-      "                                                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:799:10: note: in instantiation of template class 'Eigen::internal::triangular_dense_assignment_kernel<1, 0, 1, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:838:5: note: in instantiation of function template specialization 'Eigen::internal::call_triangular_assignment_loop<1, true, Eigen::Matrix<double, -1, -1, 0>, Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_triangular_assignment_loop<SrcXprType::Mode, (SrcXprType::Mode&SelfAdjoint)==0>(dst, src, func);  \n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Triangular2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Assign.h:75:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>' requested here\n",
-      "  internal::call_assignment(derived(), other.derived());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:571:13: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::operator=<Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>' requested here\n",
-      "      Base::operator=(other.derived());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:548:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::operator=<Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>' requested here\n",
-      "      *this = other.derived();\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::PlainObjectBase<Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:86:10: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>' requested here\n",
-      "  return llt.matrixL();\n",
-      "         ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:278:15: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "        Base::_set_noalias(other);\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:86:3: note: in instantiation of member function 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix' requested here\n",
-      "  return llt.matrixL();\n",
-      "  ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::PlainObjectBase<Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/multiply_lower_tri_self_transpose.hpp:29:17: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "  matrix_d Lt = L.transpose();\n",
-      "                ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:391:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:431:20: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      lazyproduct::evalTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 0>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::PlainObjectBase<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 0>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/tcrossprod.hpp:20:12: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 0>>' requested here\n",
-      "    return M * M.transpose();\n",
-      "           ^\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::PlainObjectBase<Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:63:27: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>' requested here\n",
-      "      Eigen::VectorXd B = b.col(col);\n",
-      "                          ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:278:15: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "        Base::_set_noalias(other);\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:63:23: note: in instantiation of member function 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix' requested here\n",
-      "      Eigen::VectorXd B = b.col(col);\n",
-      "                      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:70:15: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "            B = t * A * B / (s * j);\n",
-      "              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 15 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:70:15: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "            B = t * A * B / (s * j);\n",
-      "              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 13 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:70:15: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "            B = t * A * B / (s * j);\n",
-      "              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 15 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:70:15: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "            B = t * A * B / (s * j);\n",
-      "              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:72:15: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Matrix<double, -1, 1, 0>>::operator+=<Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "            F += B;\n",
-      "              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::mul_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:21:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:80:11: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Matrix<double, -1, 1, 0>>::operator*=' requested here\n",
-      "        F *= eta;\n",
-      "          ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Assign.h:66:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::call_assignment(derived(), other.derived());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:341:5: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>::operator=<Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:161:5: note: in instantiation of function template specialization 'Eigen::internal::BlockImpl_dense<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true, true>::operator=<Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:111:5: note: in instantiation of function template specialization 'Eigen::BlockImpl<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true, Eigen::Dense>::operator=<Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:85:20: note: in instantiation of function template specialization 'Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>::operator=<Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "      res.col(col) = F;\n",
-      "                   ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::PlainObjectBase<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:112:27: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      Eigen::MatrixXd a = mat * t;\n",
-      "                          ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:391:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:431:20: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      lazyproduct::evalTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/EigenBase.h:103:9: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>>' requested here\n",
-      "    dst = dst * this->derived();\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/MatrixBase.h:500:19: note: in instantiation of function template specialization 'Eigen::EigenBase<Eigen::Matrix<double, -1, -1, 0>>::applyThisOnTheRight<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  other.derived().applyThisOnTheRight(derived());\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:115:11: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::operator*=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "        a *= a;\n",
-      "          ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, 1, -1, 1>, Eigen::Matrix<double, 1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, 1, -1, 1>, Eigen::Matrix<double, 1, -1, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, 1, -1, 1>, Eigen::Matrix<double, 1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:278:15: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, 1, -1, 1>>::_set_noalias<Eigen::Matrix<double, 1, -1, 1>>' requested here\n",
-      "        Base::_set_noalias(other);\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/to_row_vector.hpp:35:10: note: in instantiation of member function 'Eigen::Matrix<double, 1, -1, 1, 1, -1>::Matrix' requested here\n",
-      "  return result;\n",
-      "         ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_init1<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_covar_estimator.hpp:13:9: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "      : m_(Eigen::VectorXd::Zero(n)), m2_(Eigen::MatrixXd::Zero(n, n)) {\n",
-      "        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_init1<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_covar_estimator.hpp:13:39: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "      : m_(Eigen::VectorXd::Zero(n)), m2_(Eigen::MatrixXd::Zero(n, n)) {\n",
-      "                                      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_init1<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_covar_estimator.hpp:26:21: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "    Eigen::VectorXd delta(q - m_);\n",
-      "                    ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_covar_estimator.hpp:27:8: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Matrix<double, -1, 1, 0>>::operator+=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "    m_ += delta / num_samples_;\n",
-      "       ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_init1<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>, Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:796:41: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>>' requested here\n",
-      "  typename plain_matrix_type<Src>::type tmp(src);\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_covar_estimator.hpp:28:9: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::operator+=<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>>' requested here\n",
-      "    m2_ += (q - m_) * delta.transpose();\n",
-      "        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:797:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, tmp, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_covar_estimator.hpp:28:9: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::operator+=<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>>' requested here\n",
-      "    m2_ += (q - m_) * delta.transpose();\n",
-      "        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_covar_estimator.hpp:37:13: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      covar = m2_ / (num_samples_ - 1.0);\n",
-      "            ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_var_estimator.hpp:28:9: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Matrix<double, -1, 1, 0>>::operator+=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "    m2_ += delta.cwiseProduct(q - m_);\n",
-      "        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_var_estimator.hpp:37:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      var = m2_ / (num_samples_ - 1.0);\n",
-      "          ^\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:781:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:15:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/additive_combine.hpp:27:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/linear_congruential.hpp:140:20: warning: overlapping comparisons always evaluate to false [-Wtautological-overlap-compare]\n",
-      "        if(_x <= 0 && _x != 0) {\n",
-      "           ~~~~~~~~^~~~~~~~~~\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/linear_congruential.hpp:393:11: note: in instantiation of member function 'boost::random::linear_congruential_engine<unsigned long long, 25214903917, 11, 281474976710656>::seed' requested here\n",
-      "    { lcf.seed(cnv(x0)); }\n",
-      "          ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Swap.h:20:11: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::swap_assign_op<double>, 1>' requested here\n",
-      " : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn>\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:736:51: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::swap_assign_op<double>, 0>' requested here\n",
-      "class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>\n",
-      "                                                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:799:10: note: in instantiation of template class 'Eigen::internal::triangular_dense_assignment_kernel<2, 8, 0, Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::swap_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:847:5: note: in instantiation of function template specialization 'Eigen::internal::call_triangular_assignment_loop<10, false, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);  \n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:533:7: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "      call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:256:59: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10, Eigen::Dense>::swap<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>' requested here\n",
-      "      m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose());\n",
-      "                                                          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:288:50: note: in instantiation of member function 'Eigen::internal::inplace_transpose_selector<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, false, false>::run' requested here\n",
-      "  internal::inplace_transpose_selector<Derived>::run(derived());\n",
-      "                                                 ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:109:7: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>::transposeInPlace' requested here\n",
-      "    L.transposeInPlace();\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 1>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 1>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 1>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 1>>::_init1<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/DenseBase.h:406:14: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 1, -1, -1>::Matrix<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>' requested here\n",
-      "      return typename internal::eval<Derived>::type(derived());\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:258:25: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>::eval' requested here\n",
-      "      m = m.transpose().eval();\n",
-      "                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:288:50: note: in instantiation of member function 'Eigen::internal::inplace_transpose_selector<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, false, false>::run' requested here\n",
-      "  internal::inplace_transpose_selector<Derived>::run(derived());\n",
-      "                                                 ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:109:7: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>::transposeInPlace' requested here\n",
-      "    L.transposeInPlace();\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 1>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 1>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 1>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:278:15: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 1>>::_set_noalias<Eigen::Matrix<double, -1, -1, 1>>' requested here\n",
-      "        Base::_set_noalias(other);\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/DenseBase.h:406:14: note: in instantiation of member function 'Eigen::Matrix<double, -1, -1, 1, -1, -1>::Matrix' requested here\n",
-      "      return typename internal::eval<Derived>::type(derived());\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:258:25: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>::eval' requested here\n",
-      "      m = m.transpose().eval();\n",
-      "                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:288:50: note: in instantiation of member function 'Eigen::internal::inplace_transpose_selector<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, false, false>::run' requested here\n",
-      "  internal::inplace_transpose_selector<Derived>::run(derived());\n",
-      "                                                 ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:109:7: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>::transposeInPlace' requested here\n",
-      "    L.transposeInPlace();\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 3 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:161:5: note: in instantiation of function template specialization 'Eigen::internal::BlockImpl_dense<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false, true>::operator=<Eigen::Matrix<double, -1, -1, 1>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:111:5: note: in instantiation of function template specialization 'Eigen::BlockImpl<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false, Eigen::Dense>::operator=<Eigen::Matrix<double, -1, -1, 1>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:258:9: note: in instantiation of function template specialization 'Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>::operator=<Eigen::Matrix<double, -1, -1, 1>>' requested here\n",
-      "      m = m.transpose().eval();\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:288:50: note: in instantiation of member function 'Eigen::internal::inplace_transpose_selector<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, false, false>::run' requested here\n",
-      "  internal::inplace_transpose_selector<Derived>::run(derived());\n",
-      "                                                 ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:109:7: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>::transposeInPlace' requested here\n",
-      "    L.transposeInPlace();\n",
-      "      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, 4, 4, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, 4, 4, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Diagonal<Eigen::Matrix<double, 4, 4, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Diagonal<Eigen::Matrix<double, 4, 4, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Diagonal<Eigen::Matrix<double, 4, 4, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 12 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, 0>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_init1<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, 0>, Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, 0>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/DenseBase.h:406:14: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, 0>>' requested here\n",
-      "      return typename internal::eval<Derived>::type(derived());\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:110:49: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, 0>>::eval' requested here\n",
-      "    L_adj = (L * L_adj.triangularView<Lower>()).eval();\n",
-      "                                                ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Assign.h:66:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  internal::call_assignment(derived(), other.derived());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:341:5: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>::operator=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:161:5: note: in instantiation of function template specialization 'Eigen::internal::BlockImpl_dense<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false, true>::operator=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:111:5: note: in instantiation of function template specialization 'Eigen::BlockImpl<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false, Eigen::Dense>::operator=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:110:11: note: in instantiation of function template specialization 'Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>::operator=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "    L_adj = (L * L_adj.triangularView<Lower>()).eval();\n",
-      "          ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:736:51: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>\n",
-      "                                                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:799:10: note: in instantiation of template class 'Eigen::internal::triangular_dense_assignment_kernel<2, 8, 0, Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:829:5: note: in instantiation of function template specialization 'Eigen::internal::call_triangular_assignment_loop<10, false, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);  \n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>, Eigen::internal::assign_op<double, double>, Eigen::internal::Triangular2Triangular, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:580:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>>' requested here\n",
-      "  internal::call_assignment(derived(), other.derived());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:112:9: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10, Eigen::Dense>::operator=<Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>>' requested here\n",
-      "        = L_adj.adjoint().triangularView<StrictlyUpper>();\n",
-      "        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Assign.h:57:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>' requested here\n",
-      "  internal::call_assignment(derived(), other.derived());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:66:5: note: in instantiation of member function 'Eigen::MatrixBase<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>::operator=' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Transpose)\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:821:92: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) { Base::operator=(other); return *this; } \\\n",
-      "                                                                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SolveTriangular.h:182:11: note: in instantiation of member function 'Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>::operator=' requested here\n",
-      "    other = otherCopy;\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:511:14: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 2, Eigen::Dense>::solveInPlace<1, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>' requested here\n",
-      "    { return solveInPlace<OnTheLeft>(other); }\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:114:31: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 2, Eigen::Dense>::solveInPlace<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>' requested here\n",
-      "    L.triangularView<Upper>().solveInPlace(L_adj.transpose());\n",
-      "                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Assign.h:66:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>>' requested here\n",
-      "  internal::call_assignment(derived(), other.derived());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:341:5: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>::operator=<Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:161:5: note: in instantiation of function template specialization 'Eigen::internal::BlockImpl_dense<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false, true>::operator=<Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:111:5: note: in instantiation of function template specialization 'Eigen::BlockImpl<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false, Eigen::Dense>::operator=<Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:151:15: note: in instantiation of function template specialization 'Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>::operator=<Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>>' requested here\n",
-      "        C_adj = D.transpose()\n",
-      "              ^\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>>, Eigen::internal::sub_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:405:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:452:20: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::DenseShape, Eigen::DenseShape, 3>::subTo<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      lazyproduct::subTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:178:37: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::DenseShape, Eigen::DenseShape, 8>::subTo<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "    generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());\n",
-      "                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/NoAlias.h:58:7: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "      call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:155:25: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>>' requested here\n",
-      "        B_adj.noalias() -= C_adj * R;\n",
-      "                        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>>, Eigen::internal::sub_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:405:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:452:20: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::DenseShape, Eigen::DenseShape, 3>::subTo<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      lazyproduct::subTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:178:37: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::DenseShape, Eigen::DenseShape, 8>::subTo<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "    generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());\n",
-      "                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/NoAlias.h:58:7: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "      call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:156:25: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>>' requested here\n",
-      "        D_adj.noalias() -= C_adj.transpose() * C;\n",
-      "                        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::mul_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Diagonal<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Diagonal<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Diagonal<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:21:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Diagonal<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:161:24: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Diagonal<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>>::operator*=' requested here\n",
-      "      D_adj.diagonal() *= 0.5;\n",
-      "                       ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:736:51: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>\n",
-      "                                                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:799:10: note: in instantiation of template class 'Eigen::internal::triangular_dense_assignment_kernel<2, 8, 0, Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:847:5: note: in instantiation of function template specialization 'Eigen::internal::call_triangular_assignment_loop<10, false, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);  \n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Triangular, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:560:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  internal::call_assignment_no_alias(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:394:20: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10, Eigen::Dense>::operator=<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "    { return *this = MatrixType::Constant(derived().rows(), derived().cols(), value); }\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:397:44: note: in instantiation of member function 'Eigen::TriangularViewImpl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10, Eigen::Dense>::setConstant' requested here\n",
-      "    TriangularViewType& setZero() { return setConstant(Scalar(0)); }\n",
-      "                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:162:45: note: in instantiation of member function 'Eigen::TriangularViewImpl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10, Eigen::Dense>::setZero' requested here\n",
-      "      D_adj.triangularView<StrictlyUpper>().setZero();\n",
-      "                                            ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Ref.h:89:3: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>::operator=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(RefBase)\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Ref.h:227:5: note: in instantiation of function template specialization 'Eigen::RefBase<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>::operator=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Ref)\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:428:12: note: in instantiation of function template specialization 'Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>::operator=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  m_matrix = a.derived();\n",
-      "           ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 9 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 13 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:45:13: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 9 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 13 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:45:13: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 7 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:353:72: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<const Eigen::Transpose<const Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>, 2, Eigen::Dense>::solveInPlace<2, Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>' requested here\n",
-      "      if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);\n",
-      "                                                                       ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:278:15: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<stan::math::var, -1, -1, 0>>::_set_noalias<Eigen::Matrix<stan::math::var, -1, -1, 0>>' requested here\n",
-      "        Base::_set_noalias(other);\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:451:10: note: in instantiation of member function 'Eigen::Matrix<stan::math::var, -1, -1, 0, -1, -1>::Matrix' requested here\n",
-      "  return L;\n",
-      "         ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<stan::math::var, -1, -1, 0>>::_set_noalias<Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<stan::math::var, -1, -1, 0>>::_init1<Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/crossprod.hpp:17:21: note: in instantiation of function template specialization 'Eigen::Matrix<stan::math::var, -1, -1, 0, -1, -1>::Matrix<Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>>' requested here\n",
-      "  return tcrossprod(static_cast<matrix_v>(M.transpose()));\n",
-      "                    ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:278:15: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<stan::math::var, -1, 1, 0>>::_set_noalias<Eigen::Matrix<stan::math::var, -1, 1, 0>>' requested here\n",
-      "        Base::_set_noalias(other);\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/to_var.hpp:50:10: note: in instantiation of member function 'Eigen::Matrix<stan::math::var, -1, 1, 0, -1, 1>::Matrix' requested here\n",
-      "  return v_v;\n",
-      "         ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:278:15: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<stan::math::var, 1, -1, 1>>::_set_noalias<Eigen::Matrix<stan::math::var, 1, -1, 1>>' requested here\n",
-      "        Base::_set_noalias(other);\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/to_var.hpp:75:10: note: in instantiation of member function 'Eigen::Matrix<stan::math::var, 1, -1, 1, 1, -1>::Matrix' requested here\n",
-      "  return rv_v;\n",
-      "         ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set<Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/softmax.hpp:50:9: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>>' requested here\n",
-      "  theta = (v.array() - v.maxCoeff()).exp();\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/softmax.hpp:34:14: note: in instantiation of function template specialization 'stan::math::softmax<double>' requested here\n",
-      "    auto y = softmax(alpha);\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/functor/adj_jac_apply.hpp:414:40: note: in instantiation of function template specialization 'stan::math::internal::softmax_op::operator()<1>' requested here\n",
-      "    return build_return_varis_and_vars(f_(is_var_, value_of(args)...));\n",
-      "                                       ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::PlainObjectBase<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/softmax.hpp:51:10: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>' requested here\n",
-      "  return theta.array() / theta.sum();\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/softmax.hpp:34:14: note: in instantiation of function template specialization 'stan::math::softmax<double>' requested here\n",
-      "    auto y = softmax(alpha);\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/functor/adj_jac_apply.hpp:414:40: note: in instantiation of function template specialization 'stan::math::internal::softmax_op::operator()<1>' requested here\n",
-      "    return build_return_varis_and_vars(f_(is_var_, value_of(args)...));\n",
-      "                                       ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:781:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:15:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/additive_combine.hpp:27:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/linear_congruential.hpp:140:20: warning: overlapping comparisons always evaluate to false [-Wtautological-overlap-compare]\n",
-      "        if(_x <= 0 && _x != 0) {\n",
-      "           ~~~~~~~~^~~~~~~~~~\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/additive_combine.hpp:82:16: note: in instantiation of member function 'boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>::seed' requested here\n",
-      "        _mlcg1.seed(seed_arg);\n",
-      "               ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/create_rng.hpp:29:27: note: in instantiation of member function 'boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>::additive_combine_engine' requested here\n",
-      "        boost::ecuyer1988 rng(seed);\n",
-      "                          ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:781:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:15:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/additive_combine.hpp:27:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/linear_congruential.hpp:140:20: warning: overlapping comparisons always evaluate to false [-Wtautological-overlap-compare]\n",
-      "        if(_x <= 0 && _x != 0) {\n",
-      "           ~~~~~~~~^~~~~~~~~~\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/additive_combine.hpp:83:16: note: in instantiation of member function 'boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>::seed' requested here\n",
-      "        _mlcg2.seed(seed_arg);\n",
-      "               ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/create_rng.hpp:29:27: note: in instantiation of member function 'boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>::additive_combine_engine' requested here\n",
-      "        boost::ecuyer1988 rng(seed);\n",
-      "                          ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:736:51: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>\n",
-      "                                                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:799:10: note: in instantiation of template class 'Eigen::internal::triangular_dense_assignment_kernel<1, 0, 0, Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:847:5: note: in instantiation of function template specialization 'Eigen::internal::call_triangular_assignment_loop<1, false, Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);  \n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Triangular, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:560:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  internal::call_assignment_no_alias(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:386:112: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<Eigen::Matrix<double, -1, -1, 0>, 1, Eigen::Dense>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "    TriangularViewType&  operator/=(const typename internal::traits<MatrixType>::Scalar& other) { return *this = derived().nestedExpression() / other; }\n",
-      "                                                                                                               ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:435:40: note: in instantiation of member function 'Eigen::TriangularViewImpl<Eigen::Matrix<double, -1, -1, 0>, 1, Eigen::Dense>::operator/=' requested here\n",
-      "  mat.template triangularView<Lower>() /= scale;\n",
-      "                                       ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 10 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 12 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 13 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, -1, 1, false>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, -1, 1, false>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, -1, 1, false>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, -1, 1, false>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, -1, 1, false>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 4 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 3 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:446:10: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>' requested here\n",
-      "    diag = mat.diagonal().real();\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, -1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, -1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, -1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, -1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, -1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 3 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:447:13: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, -1>>' requested here\n",
-      "    subdiag = mat.template diagonal<-1>().real();\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 14 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 10>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:736:51: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 10>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>\n",
-      "                                                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:799:10: note: in instantiation of template class 'Eigen::internal::triangular_dense_assignment_kernel<2, 8, 0, Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 10>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:847:5: note: in instantiation of function template specialization 'Eigen::internal::call_triangular_assignment_loop<10, false, Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 10>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);  \n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 10>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Triangular, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:560:13: note: (skipping 12 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment_no_alias(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::mul_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::mul_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:21:13: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 19 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 20 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 18 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 20 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>>, Eigen::internal::sub_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:164:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: (skipping 17 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::sub_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:164:3: note: (skipping 17 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 19 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Transpose<const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Transpose<const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Transpose<const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 21 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:473:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixVector.h:460:39: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "  const Index offset1 = (FirstAligned && alignmentStep==1)?3:1;\n",
-      "                                      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/GeneralProduct.h:334:129: note: in instantiation of member function 'Eigen::internal::general_matrix_vector_product<long, double, Eigen::internal::const_blas_data_mapper<double, long, 1>, 1, false, double, Eigen::internal::const_blas_data_mapper<double, long, 0>, false, 0>::run' requested here\n",
-      "        <Index,LhsScalar,LhsMapper,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(\n",
-      "                                                                                                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/GeneralProduct.h:192:9: note: in instantiation of function template specialization 'Eigen::internal::gemv_dense_selector<2, 1, true>::run<Eigen::Transpose<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>>, Eigen::Transpose<const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>, Eigen::Transpose<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>>' requested here\n",
-      "      ::run(rhs.transpose(), lhs.transpose(), destT, alpha);\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:377:31: note: in instantiation of function template specialization 'Eigen::internal::gemv_dense_selector<1, 0, true>::run<Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>' requested here\n",
-      "                           >::run(actual_lhs, actual_rhs, dst, alpha);\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:355:14: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl<Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, Eigen::DenseShape, Eigen::DenseShape, 7>::scaleAndAddTo<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>' requested here\n",
-      "  { Derived::scaleAndAddTo(dst,lhs,rhs,alpha); }\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:343:20: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl_base<Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, Eigen::internal::generic_product_impl<Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, Eigen::DenseShape, Eigen::DenseShape, 7>>::scaleAndAddTo<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>' requested here\n",
-      "  { dst.setZero(); scaleAndAddTo(dst, lhs, rhs, Scalar(1)); }\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:148:37: note: (skipping 13 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "    generic_product_impl<Lhs, Rhs>::evalTo(dst, src.lhs(), src.rhs());\n",
-      "                                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:473:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixVector.h:461:39: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "  const Index offset3 = (FirstAligned && alignmentStep==1)?1:3;\n",
-      "                                      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, 1, -1, 1>>, const Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, 1, -1, 1>>, const Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>>, Eigen::internal::sub_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, 1, -1, 1>>, const Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, 1, -1, 1>>, const Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, 1, -1, 1>>, const Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:164:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: (skipping 17 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 16 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Swap.h:20:11: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::swap_assign_op<double>, 1>' requested here\n",
-      " : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn>\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::swap_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::internal::swap_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/DenseBase.h:418:7: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "      call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:540:24: note: in instantiation of function template specialization 'Eigen::DenseBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>::swap<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>' requested here\n",
-      "          eivec.col(i).swap(eivec.col(k+i));\n",
-      "                       ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:439:22: note: in instantiation of function template specialization 'Eigen::internal::computeFromTridiagonal_impl<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  m_info = internal::computeFromTridiagonal_impl(diag, m_subdiag, m_maxIterations, computeEigenvectors, m_eivec);\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Product<Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Product<Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::Product<Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::PlainObjectBase<Eigen::Product<Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/newton.hpp:24:35: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::Product<Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>>' requested here\n",
-      "      vector_d eigenprojections = eigenvectors.transpose() * g;\n",
-      "                                  ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set<Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseNullaryOp.h:747:14: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "    return m = Derived::Identity(m.rows(), m.cols());\n",
-      "             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseNullaryOp.h:776:47: note: in instantiation of member function 'Eigen::internal::setIdentity_impl<Eigen::Matrix<double, -1, -1, 0>, false>::run' requested here\n",
-      "  return internal::setIdentity_impl<Derived>::run(derived());\n",
-      "                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/mcmc/hmc/hamiltonians/dense_e_point.hpp:28:23: note: in instantiation of member function 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::setIdentity' requested here\n",
-      "        inv_e_metric_.setIdentity();\n",
-      "                      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::PlainObjectBase<Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/to_matrix.hpp:118:10: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>>' requested here\n",
-      "  return Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> >(\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/read_dense_inv_metric.hpp:38:25: note: in instantiation of function template specialization 'stan::math::to_matrix<double>' requested here\n",
-      "            stan::math::to_matrix(dense_vals, num_params, num_params);\n",
-      "                        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Swap.h:20:11: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>>, Eigen::internal::swap_assign_op<double>, 1>' requested here\n",
-      " : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn>\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>>, Eigen::internal::swap_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>, Eigen::internal::swap_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: (skipping 3 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:517:42: note: in instantiation of function template specialization 'Eigen::internal::ldlt_inplace<1>::unblocked<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpositions<-1>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success : NumericalIssue;\n",
-      "                                         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:112:7: note: in instantiation of function template specialization 'Eigen::LDLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:664:10: note: in instantiation of function template specialization 'Eigen::LDLT<Eigen::Matrix<double, -1, -1, 0>, 1>::LDLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  return LDLT<PlainObject>(derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/err/check_pos_definite.hpp:38:59: note: in instantiation of member function 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::ldlt' requested here\n",
-      "  Eigen::LDLT<Eigen::MatrixXd> cholesky = value_of_rec(y).ldlt();\n",
-      "                                                          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/validate_dense_inv_metric.hpp:23:23: note: in instantiation of function template specialization 'stan::math::check_pos_definite<double>' requested here\n",
-      "          stan::math::check_pos_definite(\"check_pos_definite\",\n",
-      "                      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Swap.h:20:11: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::swap_assign_op<double>, 1>' requested here\n",
-      " : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn>\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::swap_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::internal::swap_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: (skipping 3 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:517:42: note: in instantiation of function template specialization 'Eigen::internal::ldlt_inplace<1>::unblocked<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpositions<-1>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success : NumericalIssue;\n",
-      "                                         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:112:7: note: in instantiation of function template specialization 'Eigen::LDLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:664:10: note: in instantiation of function template specialization 'Eigen::LDLT<Eigen::Matrix<double, -1, -1, 0>, 1>::LDLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  return LDLT<PlainObject>(derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/err/check_pos_definite.hpp:38:59: note: in instantiation of member function 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::ldlt' requested here\n",
-      "  Eigen::LDLT<Eigen::MatrixXd> cholesky = value_of_rec(y).ldlt();\n",
-      "                                                          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/validate_dense_inv_metric.hpp:23:23: note: in instantiation of function template specialization 'stan::math::check_pos_definite<double>' requested here\n",
-      "          stan::math::check_pos_definite(\"check_pos_definite\",\n",
-      "                      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Product<Eigen::DiagonalWrapper<const Eigen::Block<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, -1, 1, false>>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Product<Eigen::DiagonalWrapper<const Eigen::Block<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, -1, 1, false>>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::Product<Eigen::DiagonalWrapper<const Eigen::Block<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, -1, 1, false>>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::Product<Eigen::DiagonalWrapper<const Eigen::Block<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, -1, 1, false>>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::Product<Eigen::DiagonalWrapper<const Eigen::Block<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, -1, 1, false>>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 6 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:517:42: note: in instantiation of function template specialization 'Eigen::internal::ldlt_inplace<1>::unblocked<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpositions<-1>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success : NumericalIssue;\n",
-      "                                         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:112:7: note: in instantiation of function template specialization 'Eigen::LDLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:664:10: note: in instantiation of function template specialization 'Eigen::LDLT<Eigen::Matrix<double, -1, -1, 0>, 1>::LDLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  return LDLT<PlainObject>(derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/err/check_pos_definite.hpp:38:59: note: in instantiation of member function 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::ldlt' requested here\n",
-      "  Eigen::LDLT<Eigen::MatrixXd> cholesky = value_of_rec(y).ldlt();\n",
-      "                                                          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/validate_dense_inv_metric.hpp:23:23: note: in instantiation of function template specialization 'stan::math::check_pos_definite<double>' requested here\n",
-      "          stan::math::check_pos_definite(\"check_pos_definite\",\n",
-      "                      ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/mcmc/covar_adaptation.hpp:27:17: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>>' requested here\n",
-      "          covar = (n / (n + 5.0)) * covar\n",
-      "                ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/mcmc/var_adaptation.hpp:27:15: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>>' requested here\n",
-      "          var = (n / (n + 5.0)) * var\n",
-      "              ^\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_init1<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:189:32: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "        return normal_fullrank(Eigen::VectorXd(mu_.array().square()),\n",
-      "                               ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_init1<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:190:32: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "                               Eigen::MatrixXd(L_chol_.array().square()));\n",
-      "                               ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_init1<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:204:32: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "        return normal_fullrank(Eigen::VectorXd(mu_.array().sqrt()),\n",
-      "                               ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_init1<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:205:32: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "                               Eigen::MatrixXd(L_chol_.array().sqrt()));\n",
-      "                               ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ArrayBase.h:220:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:273:21: note: in instantiation of function template specialization 'Eigen::ArrayBase<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>::operator/=<Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "        mu_.array() /= rhs.mu().array();\n",
-      "                    ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::evaluator<Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::evaluator<Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ArrayBase.h:220:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:274:25: note: in instantiation of function template specialization 'Eigen::ArrayBase<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>>::operator/=<Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "        L_chol_.array() /= rhs.L_chol().array();\n",
-      "                        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:29:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::add_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:290:21: note: in instantiation of member function 'Eigen::ArrayBase<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>::operator+=' requested here\n",
-      "        mu_.array() += scalar;\n",
-      "                    ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, -1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, -1, 0>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, -1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, -1, 0>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, -1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:29:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, -1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::add_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:291:25: note: in instantiation of member function 'Eigen::ArrayBase<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>>::operator+=' requested here\n",
-      "        L_chol_.array() += scalar;\n",
-      "                        ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::mul_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::mul_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:21:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:309:17: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Matrix<double, -1, -1, 0>>::operator*=' requested here\n",
-      "        L_chol_ *= scalar;\n",
-      "                ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::PlainObjectBase<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:363:16: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "        return (L_chol_ * eta) + mu_;\n",
-      "               ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::PlainObjectBase<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/families/normal_meanfield.hpp:324:16: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "        return eta.array().cwiseProduct(omega_.array().exp()) + mu_.array();\n",
-      "               ^\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::PlainObjectBase<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/subtract.hpp:43:10: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>' requested here\n",
-      "  return m.array() - c;\n",
-      "         ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/anon_model_ce583eb84b35032d795f7056cd3c761e.hpp:280:47: note: in instantiation of function template specialization 'stan::math::subtract<double, double, -1, 1>' requested here\n",
-      "            stan::math::assign(RESPONSE_TRAN, subtract(RESPONSE, MEAN_Y));\n",
-      "                                              ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:97:11: note: in instantiation of function template specialization 'stan::io::random_var_context::random_var_context<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          random_context(model, rng, init_radius, is_initialized_with_zero);\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:97:11: note: in instantiation of function template specialization 'stan::io::random_var_context::random_var_context<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          random_context(model, rng, init_radius, is_initialized_with_zero);\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<double, 1, -1, 1>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<double, 1, -1, 1>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, 1, -1, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, 1, -1, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, 1, -1, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:97:11: note: in instantiation of function template specialization 'stan::io::random_var_context::random_var_context<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          random_context(model, rng, init_radius, is_initialized_with_zero);\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, 1, -1, 1>, Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, 1, -1, 1>, Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, 1, -1, 1>, Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:97:11: note: in instantiation of function template specialization 'stan::io::random_var_context::random_var_context<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          random_context(model, rng, init_radius, is_initialized_with_zero);\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:97:11: note: in instantiation of function template specialization 'stan::io::random_var_context::random_var_context<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          random_context(model, rng, init_radius, is_initialized_with_zero);\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:97:11: note: in instantiation of function template specialization 'stan::io::random_var_context::random_var_context<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          random_context(model, rng, init_radius, is_initialized_with_zero);\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<stan::math::var, -1, 1, 0>, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<stan::math::var, -1, 1, 0>, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::Map<Eigen::Matrix<stan::math::var, -1, 1, 0>, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::Map<Eigen::Matrix<stan::math::var, -1, 1, 0>, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::Map<Eigen::Matrix<stan::math::var, -1, 1, 0>, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<stan::math::var, -1, -1, 0>, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<stan::math::var, -1, -1, 0>, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Map<Eigen::Matrix<stan::math::var, -1, -1, 0>, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Map<Eigen::Matrix<stan::math::var, -1, -1, 0>, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Map<Eigen::Matrix<stan::math::var, -1, -1, 0>, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, 1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, 1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, -1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, -1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:391:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:431:20: note: (skipping 10 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      lazyproduct::evalTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Transpose<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Transpose<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Transpose<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Transpose<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:391:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Transpose<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:431:20: note: (skipping 15 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      lazyproduct::evalTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Block<const Eigen::Matrix<stan::math::var, -1, -1, 0>, 1, -1, false>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Block<const Eigen::Matrix<stan::math::var, -1, -1, 0>, 1, -1, false>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::Block<const Eigen::Matrix<stan::math::var, -1, -1, 0>, 1, -1, false>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::Block<const Eigen::Matrix<stan::math::var, -1, -1, 0>, 1, -1, false>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::Block<const Eigen::Matrix<stan::math::var, -1, -1, 0>, 1, -1, false>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, stan::math::var>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<stan::math::var, 1, -1, 1>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, stan::math::var>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<stan::math::var, 1, -1, 1>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, stan::math::var>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, stan::math::var>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, stan::math::var>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<stan::math::var>, const Eigen::Matrix<stan::math::var, -1, 1, 0>, const Eigen::Matrix<stan::math::var, -1, 1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<stan::math::var>, const Eigen::Matrix<stan::math::var, -1, 1, 0>, const Eigen::Matrix<stan::math::var, -1, 1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<stan::math::var>, const Eigen::Matrix<stan::math::var, -1, 1, 0>, const Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<stan::math::var>, const Eigen::Matrix<stan::math::var, -1, 1, 0>, const Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<stan::math::var>, const Eigen::Matrix<stan::math::var, -1, 1, 0>, const Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:409:9: note: in instantiation of member function 'stan::optimization::BFGSLineSearch<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::initialize' requested here\n",
-      "        initialize(params_r);\n",
-      "        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:74:19: note: in instantiation of member function 'stan::optimization::BFGSLineSearch<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::BFGSLineSearch' requested here\n",
-      "        Optimizer bfgs(model, cont_vector, disc_vector, &bfgs_ss);\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/NoAlias.h:42:7: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs_linesearch.hpp:253:22: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "        x1.noalias() = x0 + alpha1 * p;\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:205:21: note: in instantiation of function template specialization 'stan::optimization::WolfeLineSearch<stan::optimization::ModelAdaptor<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>, double, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "          retCode = WolfeLineSearch(_func, _alpha, _xk_1, _fk_1, _gk_1,\n",
-      "                    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: (skipping 10 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::sub_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:164:3: note: (skipping 10 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:391:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:431:20: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      lazyproduct::evalTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: (skipping 13 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 1>, Eigen::Product<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 1>, Eigen::Product<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:391:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 1>, Eigen::Product<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:431:20: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      lazyproduct::evalTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 1>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 1>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 1>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 1>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 1>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 16 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:797:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, tmp, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 4 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:45:13: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:246:17: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Matrix<double, -1, 1, 0>>::operator/=' requested here\n",
-      "          _pk_1 /= B0fact;\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/NoAlias.h:42:7: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs_update.hpp:56:22: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "        pk.noalias() = -(_Hk*gk);\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:253:13: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::search_direction' requested here\n",
-      "        _qn.search_direction(_pk, _gk);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::sub_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:164:3: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:253:13: note: in instantiation of member function 'stan::optimization::LBFGSUpdate<double, -1>::search_direction' requested here\n",
-      "        _qn.search_direction(_pk, _gk);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/optimize/lbfgs.hpp:123:23: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>, stan::optimization::LBFGSUpdate<double, -1>, double, -1>::step' requested here\n",
-      "          ret = lbfgs.step();\n",
-      "                      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:924:41: note: in instantiation of function template specialization 'stan::services::optimize::lbfgs<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            = stan::services::optimize::lbfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:253:13: note: in instantiation of member function 'stan::optimization::LBFGSUpdate<double, -1>::search_direction' requested here\n",
-      "        _qn.search_direction(_pk, _gk);\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/optimize/lbfgs.hpp:123:23: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>, stan::optimization::LBFGSUpdate<double, -1>, double, -1>::step' requested here\n",
-      "          ret = lbfgs.step();\n",
-      "                      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:924:41: note: in instantiation of function template specialization 'stan::services::optimize::lbfgs<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            = stan::services::optimize::lbfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, 1, -1, 1>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, 1, -1, 1>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, 1, -1, 1>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 23 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/mcmc/hmc/nuts/dense_e_nuts.hpp:20:11: note: in instantiation of member function 'stan::mcmc::base_nuts<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, dense_e_metric, expl_leapfrog, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::base_nuts' requested here\n",
-      "        : base_nuts<Model, dense_e_metric, expl_leapfrog,\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/sample/hmc_nuts_dense_e.hpp:79:11: note: in instantiation of member function 'stan::mcmc::dense_e_nuts<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::dense_e_nuts' requested here\n",
-      "          sampler(model, rng);\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1008:19: note: in instantiation of function template specialization 'stan::services::sample::hmc_nuts_dense_e<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "                ::hmc_nuts_dense_e(model, *init_context_ptr, *metric_context_ptr,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Transpose<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Transpose<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Transpose<const Eigen::Transpose<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Transpose<const Eigen::Transpose<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Transpose<const Eigen::Transpose<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 25 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/mcmc/hmc/nuts/dense_e_nuts.hpp:20:11: note: in instantiation of member function 'stan::mcmc::base_nuts<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, dense_e_metric, expl_leapfrog, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::base_nuts' requested here\n",
-      "        : base_nuts<Model, dense_e_metric, expl_leapfrog,\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/sample/hmc_nuts_dense_e.hpp:79:11: note: in instantiation of member function 'stan::mcmc::dense_e_nuts<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::dense_e_nuts' requested here\n",
-      "          sampler(model, rng);\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1008:19: note: in instantiation of function template specialization 'stan::services::sample::hmc_nuts_dense_e<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "                ::hmc_nuts_dense_e(model, *init_context_ptr, *metric_context_ptr,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 15 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/mcmc/hmc/nuts/dense_e_nuts.hpp:20:11: note: in instantiation of member function 'stan::mcmc::base_nuts<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, dense_e_metric, expl_leapfrog, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::base_nuts' requested here\n",
-      "        : base_nuts<Model, dense_e_metric, expl_leapfrog,\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/sample/hmc_nuts_dense_e.hpp:79:11: note: in instantiation of member function 'stan::mcmc::dense_e_nuts<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::dense_e_nuts' requested here\n",
-      "          sampler(model, rng);\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1008:19: note: in instantiation of function template specialization 'stan::services::sample::hmc_nuts_dense_e<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "                ::hmc_nuts_dense_e(model, *init_context_ptr, *metric_context_ptr,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 15 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/mcmc/hmc/nuts/dense_e_nuts.hpp:20:11: note: in instantiation of member function 'stan::mcmc::base_nuts<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, dense_e_metric, expl_leapfrog, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::base_nuts' requested here\n",
-      "        : base_nuts<Model, dense_e_metric, expl_leapfrog,\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/sample/hmc_nuts_dense_e.hpp:79:11: note: in instantiation of member function 'stan::mcmc::dense_e_nuts<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::dense_e_nuts' requested here\n",
-      "          sampler(model, rng);\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1008:19: note: in instantiation of function template specialization 'stan::services::sample::hmc_nuts_dense_e<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "                ::hmc_nuts_dense_e(model, *init_context_ptr, *metric_context_ptr,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 4 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/mcmc/hmc/nuts/diag_e_nuts.hpp:20:11: note: in instantiation of member function 'stan::mcmc::base_nuts<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, diag_e_metric, expl_leapfrog, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::base_nuts' requested here\n",
-      "        : base_nuts<Model, diag_e_metric, expl_leapfrog,\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/sample/hmc_nuts_diag_e.hpp:79:11: note: in instantiation of member function 'stan::mcmc::diag_e_nuts<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::diag_e_nuts' requested here\n",
-      "          sampler(model, rng);\n",
-      "          ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1062:19: note: in instantiation of function template specialization 'stan::services::sample::hmc_nuts_diag_e<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "                ::hmc_nuts_diag_e(model, *init_context_ptr, *metric_context_ptr,\n",
-      "                  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:45:13: note: (skipping 4 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/advi.hpp:497:17: note: in instantiation of member function 'stan::variational::advi<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, stan::variational::normal_fullrank, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::adapt_eta' requested here\n",
-      "          eta = adapt_eta(variational, adapt_iterations, logger);\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/experimental/advi/fullrank.hpp:87:20: note: in instantiation of member function 'stan::variational::advi<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, stan::variational::normal_fullrank, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::run' requested here\n",
-      "          cmd_advi.run(eta, adapt_engaged, adapt_iterations,\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1378:15: note: in instantiation of function template specialization 'stan::services::experimental::advi::fullrank<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            ::fullrank(model, *init_context_ptr,\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_op<double>, const Eigen::ArrayWrapper<const Eigen::Diagonal<const Eigen::Matrix<double, -1, -1, 0>, 0>>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_op<double>, const Eigen::ArrayWrapper<const Eigen::Diagonal<const Eigen::Matrix<double, -1, -1, 0>, 0>>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::ArrayWrapper<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_op<double>, const Eigen::ArrayWrapper<const Eigen::Diagonal<const Eigen::Matrix<double, -1, -1, 0>, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::ArrayWrapper<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_op<double>, const Eigen::ArrayWrapper<const Eigen::Diagonal<const Eigen::Matrix<double, -1, -1, 0>, 0>>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::ArrayWrapper<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_op<double>, const Eigen::ArrayWrapper<const Eigen::Diagonal<const Eigen::Matrix<double, -1, -1, 0>, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ArrayBase.h:194:3: note: (skipping 4 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/advi.hpp:497:17: note: in instantiation of member function 'stan::variational::advi<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, stan::variational::normal_fullrank, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::adapt_eta' requested here\n",
-      "          eta = adapt_eta(variational, adapt_iterations, logger);\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/experimental/advi/fullrank.hpp:87:20: note: in instantiation of member function 'stan::variational::advi<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, stan::variational::normal_fullrank, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::run' requested here\n",
-      "          cmd_advi.run(eta, adapt_engaged, adapt_iterations,\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1378:15: note: in instantiation of function template specialization 'stan::services::experimental::advi::fullrank<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            ::fullrank(model, *init_context_ptr,\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ArrayBase.h:194:3: note: (skipping 4 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/advi.hpp:497:17: note: in instantiation of member function 'stan::variational::advi<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, stan::variational::normal_meanfield, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::adapt_eta' requested here\n",
-      "          eta = adapt_eta(variational, adapt_iterations, logger);\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/experimental/advi/meanfield.hpp:87:20: note: in instantiation of member function 'stan::variational::advi<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, stan::variational::normal_meanfield, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::run' requested here\n",
-      "          cmd_advi.run(eta, adapt_engaged, adapt_iterations,\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1388:15: note: in instantiation of function template specialization 'stan::services::experimental::advi::meanfield<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            ::meanfield(model, *init_context_ptr,\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 6 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/variational/advi.hpp:497:17: note: in instantiation of member function 'stan::variational::advi<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, stan::variational::normal_meanfield, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::adapt_eta' requested here\n",
-      "          eta = adapt_eta(variational, adapt_iterations, logger);\n",
-      "                ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/services/experimental/advi/meanfield.hpp:87:20: note: in instantiation of member function 'stan::variational::advi<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, stan::variational::normal_meanfield, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::run' requested here\n",
-      "          cmd_advi.run(eta, adapt_engaged, adapt_iterations,\n",
-      "                   ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1388:15: note: in instantiation of function template specialization 'stan::services::experimental::advi::meanfield<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e>' requested here\n",
-      "            ::meanfield(model, *init_context_ptr,\n",
-      "              ^\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:9585:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_ce583eb84b35032d795f7056cd3c761e_namespace::anon_model_ce583eb84b35032d795f7056cd3c761e, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:780:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:336:\n",
-      "In file included from /Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/prob/poisson_log_glm_log.hpp:5:\n",
-      "/Users/gavin.steininger/.pyenv/versions/3.7.12/lib/python3.7/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/prob/poisson_log_glm_lpmf.hpp:64:59: warning: unused typedef 'T_alpha_val' [-Wunused-local-typedef]\n",
-      "      typename partials_return_type<T_alpha>::type>::type T_alpha_val;\n",
-      "                                                          ^\n",
-      "In file included from /var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/stanfit4anon_model_ce583eb84b35032d795f7056cd3c761e_2504781463713120771.cpp:783:\n",
-      "/var/folders/2r/w1lk3nxj09s73plknkyzzj5m0000gn/T/pystan_oxaut9yl/anon_model_ce583eb84b35032d795f7056cd3c761e.hpp:312:24: warning: unused typedef 'local_scalar_t__' [-Wunused-local-typedef]\n",
-      "        typedef double local_scalar_t__;\n",
-      "                       ^\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "165 warnings generated.\n",
-      "INFO:orbit:Using SVI(Pyro) with steps:1000 , samples:1000 , learning rate:0.1, learning_rate_total_decay:1.0 and particles:100.\n",
-      "INFO:root:Guessed max_plate_nesting = 1\n",
-      "INFO:orbit:step    0 loss = -129.28, scale = 0.08837\n",
-      "INFO:orbit:step  100 loss = -352.57, scale = 0.03726\n",
-      "INFO:orbit:step  200 loss = -351.47, scale = 0.034317\n",
-      "INFO:orbit:step  300 loss = -352.31, scale = 0.037732\n",
-      "INFO:orbit:step  400 loss = -352.89, scale = 0.036369\n",
-      "INFO:orbit:step  500 loss = -354.05, scale = 0.036469\n",
-      "INFO:orbit:step  600 loss = -354.12, scale = 0.038294\n",
-      "INFO:orbit:step  700 loss = -351.81, scale = 0.039109\n",
-      "INFO:orbit:step  800 loss = -355.12, scale = 0.037118\n",
-      "INFO:orbit:step  900 loss = -353.15, scale = 0.037624\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x1436873d0>"
-      ]
-     },
-     "execution_count": 7,
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-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ktr.fit(train_df)"
-   ]
-  },
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\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ktr.plot_lev_knots(figsize=(16, 8));"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:05:59.244358Z",
-     "start_time": "2022-01-26T02:05:58.906869Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ktr.plot_regression_coefs(with_knot=True, include_ci=False, figsize=(16, 8));"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:01.156136Z",
-     "start_time": "2022-01-26T02:06:01.094356Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>prediction_5</th>\n",
-       "      <th>prediction</th>\n",
-       "      <th>prediction_95</th>\n",
-       "      <th>trend_5</th>\n",
-       "      <th>trend</th>\n",
-       "      <th>trend_95</th>\n",
-       "      <th>regression_5</th>\n",
-       "      <th>regression</th>\n",
-       "      <th>regression_95</th>\n",
-       "      <th>seasonality_52_5</th>\n",
-       "      <th>seasonality_52</th>\n",
-       "      <th>seasonality_52_95</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2017-07-02</td>\n",
-       "      <td>12.18674</td>\n",
-       "      <td>12.34466</td>\n",
-       "      <td>12.50898</td>\n",
-       "      <td>12.18138</td>\n",
-       "      <td>12.33745</td>\n",
-       "      <td>12.50050</td>\n",
-       "      <td>0.01329</td>\n",
-       "      <td>0.02123</td>\n",
-       "      <td>0.02894</td>\n",
-       "      <td>-0.01367</td>\n",
-       "      <td>-0.01367</td>\n",
-       "      <td>-0.01367</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2017-07-09</td>\n",
-       "      <td>12.19311</td>\n",
-       "      <td>12.35454</td>\n",
-       "      <td>12.50465</td>\n",
-       "      <td>12.19271</td>\n",
-       "      <td>12.35026</td>\n",
-       "      <td>12.50263</td>\n",
-       "      <td>0.01524</td>\n",
-       "      <td>0.02434</td>\n",
-       "      <td>0.03319</td>\n",
-       "      <td>-0.02090</td>\n",
-       "      <td>-0.02090</td>\n",
-       "      <td>-0.02090</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2017-07-16</td>\n",
-       "      <td>12.17019</td>\n",
-       "      <td>12.32331</td>\n",
-       "      <td>12.49775</td>\n",
-       "      <td>12.18430</td>\n",
-       "      <td>12.33771</td>\n",
-       "      <td>12.51030</td>\n",
-       "      <td>0.01127</td>\n",
-       "      <td>0.01801</td>\n",
-       "      <td>0.02455</td>\n",
-       "      <td>-0.03211</td>\n",
-       "      <td>-0.03211</td>\n",
-       "      <td>-0.03211</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2017-07-23</td>\n",
-       "      <td>12.14305</td>\n",
-       "      <td>12.31592</td>\n",
-       "      <td>12.46666</td>\n",
-       "      <td>12.17380</td>\n",
-       "      <td>12.34777</td>\n",
-       "      <td>12.49755</td>\n",
-       "      <td>0.01024</td>\n",
-       "      <td>0.01636</td>\n",
-       "      <td>0.02230</td>\n",
-       "      <td>-0.04732</td>\n",
-       "      <td>-0.04732</td>\n",
-       "      <td>-0.04732</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2017-07-30</td>\n",
-       "      <td>12.13637</td>\n",
-       "      <td>12.29054</td>\n",
-       "      <td>12.45037</td>\n",
-       "      <td>12.18249</td>\n",
-       "      <td>12.33905</td>\n",
-       "      <td>12.50050</td>\n",
-       "      <td>0.01127</td>\n",
-       "      <td>0.01801</td>\n",
-       "      <td>0.02455</td>\n",
-       "      <td>-0.06621</td>\n",
-       "      <td>-0.06621</td>\n",
-       "      <td>-0.06621</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week  prediction_5  prediction  prediction_95  trend_5    trend  \\\n",
-       "0 2017-07-02      12.18674    12.34466       12.50898 12.18138 12.33745   \n",
-       "1 2017-07-09      12.19311    12.35454       12.50465 12.19271 12.35026   \n",
-       "2 2017-07-16      12.17019    12.32331       12.49775 12.18430 12.33771   \n",
-       "3 2017-07-23      12.14305    12.31592       12.46666 12.17380 12.34777   \n",
-       "4 2017-07-30      12.13637    12.29054       12.45037 12.18249 12.33905   \n",
-       "\n",
-       "   trend_95  regression_5  regression  regression_95  seasonality_52_5  \\\n",
-       "0  12.50050       0.01329     0.02123        0.02894          -0.01367   \n",
-       "1  12.50263       0.01524     0.02434        0.03319          -0.02090   \n",
-       "2  12.51030       0.01127     0.01801        0.02455          -0.03211   \n",
-       "3  12.49755       0.01024     0.01636        0.02230          -0.04732   \n",
-       "4  12.50050       0.01127     0.01801        0.02455          -0.06621   \n",
-       "\n",
-       "   seasonality_52  seasonality_52_95  \n",
-       "0        -0.01367           -0.01367  \n",
-       "1        -0.02090           -0.02090  \n",
-       "2        -0.03211           -0.03211  \n",
-       "3        -0.04732           -0.04732  \n",
-       "4        -0.06621           -0.06621  "
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predicted_df = ktr.predict(df=test_df, decompose=True)\n",
-    "predicted_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:01.502121Z",
-     "start_time": "2022-01-26T02:06:01.465315Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'SMAPE: 0.73%'"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f\"SMAPE: {smape(predicted_df['prediction'].values, test_df[RESPONSE_COL].values):.2%}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:03.397740Z",
-     "start_time": "2022-01-26T02:06:03.088949Z"
-    },
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_predicted_data(training_actual_df=train_df,\n",
-    "                        predicted_df=predicted_df,\n",
-    "                        date_col=DATE_COL,\n",
-    "                        actual_col=RESPONSE_COL,\n",
-    "                        test_actual_df=test_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### multiple regression_segments\n",
-    "\n",
-    "Change `regression_segments=0` args to `regression_segments=5`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:05.249898Z",
-     "start_time": "2022-01-26T02:06:05.216728Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ktr = KTR(\n",
-    "    date_col=DATE_COL,\n",
-    "    response_col=RESPONSE_COL,\n",
-    "    # regressor_col=['trend.unemploy', 'trend.filling', 'trend.job'],\n",
-    "    regressor_col=['trend.unemploy'],\n",
-    "    seasonality=[52],\n",
-    "    seasonality_fs_order=[3],\n",
-    "    level_knot_scale=.1,\n",
-    "    level_segments=10,\n",
-    "    regression_segments=5,\n",
-    "    regression_rho=0.15,\n",
-    "    # pyro optimization parameters\n",
-    "    seed=8888,\n",
-    "    num_steps=1000,\n",
-    "    num_sample=1000,\n",
-    "    learning_rate=0.1,\n",
-    "    estimator='pyro-svi',\n",
-    "    n_bootstrap_draws=-1,\n",
-    "    ktrlite_optim_args = dict()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:18.926575Z",
-     "start_time": "2022-01-26T02:06:06.828565Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:Optimizing(PyStan) with algorithm:LBFGS .\n",
-      "INFO:orbit:Using SVI(Pyro) with steps:1000 , samples:1000 , learning rate:0.1, learning_rate_total_decay:1.0 and particles:100 .\n",
-      "INFO:root:Guessed max_plate_nesting = 1\n",
-      "INFO:orbit:step    0 loss = -122.05, scale = 0.084961\n",
-      "INFO:orbit:step  100 loss = -352.24, scale = 0.046498\n",
-      "INFO:orbit:step  200 loss = -354.46, scale = 0.050182\n",
-      "INFO:orbit:step  300 loss = -352.78, scale = 0.048628\n",
-      "INFO:orbit:step  400 loss = -349.38, scale = 0.049816\n",
-      "INFO:orbit:step  500 loss = -351.55, scale = 0.049404\n",
-      "INFO:orbit:step  600 loss = -350.36, scale = 0.049911\n",
-      "INFO:orbit:step  700 loss = -352.88, scale = 0.050363\n",
-      "INFO:orbit:step  800 loss = -353.24, scale = 0.050048\n",
-      "INFO:orbit:step  900 loss = -351.19, scale = 0.049696\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x13f728f50>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ktr.fit(train_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:18.961440Z",
-     "start_time": "2022-01-26T02:06:18.928642Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "        vertical-align: middle;\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>0.10386</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>0.10377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>0.10367</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>0.10357</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>0.10348</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week  trend.unemploy\n",
-       "0 2010-01-03         0.10386\n",
-       "1 2010-01-10         0.10377\n",
-       "2 2010-01-17         0.10367\n",
-       "3 2010-01-24         0.10357\n",
-       "4 2010-01-31         0.10348"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ktr.get_regression_coefs().head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:18.998463Z",
-     "start_time": "2022-01-26T02:06:18.964140Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>step</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.11035</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2011-07-03</td>\n",
-       "      <td>78</td>\n",
-       "      <td>0.08710</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2012-12-30</td>\n",
-       "      <td>156</td>\n",
-       "      <td>0.11927</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2014-06-29</td>\n",
-       "      <td>234</td>\n",
-       "      <td>0.06504</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2015-12-27</td>\n",
-       "      <td>312</td>\n",
-       "      <td>0.09255</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>2017-06-25</td>\n",
-       "      <td>390</td>\n",
-       "      <td>0.13490</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week  step  trend.unemploy\n",
-       "0 2010-01-03     0         0.11035\n",
-       "1 2011-07-03    78         0.08710\n",
-       "2 2012-12-30   156         0.11927\n",
-       "3 2014-06-29   234         0.06504\n",
-       "4 2015-12-27   312         0.09255\n",
-       "5 2017-06-25   390         0.13490"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ktr.get_regression_coef_knots()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:19.309108Z",
-     "start_time": "2022-01-26T02:06:19.000896Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 1152x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ktr.plot_lev_knots(figsize=(16, 8), use_orbit_style=False);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:23.316124Z",
-     "start_time": "2022-01-26T02:06:23.052106Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ktr.plot_regression_coefs(with_knot=True, figsize=(10, 5), include_ci=False);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:24.903463Z",
-     "start_time": "2022-01-26T02:06:24.840106Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>prediction_5</th>\n",
-       "      <th>prediction</th>\n",
-       "      <th>prediction_95</th>\n",
-       "      <th>trend_5</th>\n",
-       "      <th>trend</th>\n",
-       "      <th>trend_95</th>\n",
-       "      <th>regression_5</th>\n",
-       "      <th>regression</th>\n",
-       "      <th>regression_95</th>\n",
-       "      <th>seasonality_52_5</th>\n",
-       "      <th>seasonality_52</th>\n",
-       "      <th>seasonality_52_95</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2017-07-02</td>\n",
-       "      <td>12.17602</td>\n",
-       "      <td>12.33509</td>\n",
-       "      <td>12.49622</td>\n",
-       "      <td>12.16346</td>\n",
-       "      <td>12.32028</td>\n",
-       "      <td>12.48274</td>\n",
-       "      <td>0.00148</td>\n",
-       "      <td>0.02692</td>\n",
-       "      <td>0.05021</td>\n",
-       "      <td>-0.01367</td>\n",
-       "      <td>-0.01367</td>\n",
-       "      <td>-0.01367</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2017-07-09</td>\n",
-       "      <td>12.17908</td>\n",
-       "      <td>12.33787</td>\n",
-       "      <td>12.48704</td>\n",
-       "      <td>12.16675</td>\n",
-       "      <td>12.32876</td>\n",
-       "      <td>12.48605</td>\n",
-       "      <td>0.00170</td>\n",
-       "      <td>0.03087</td>\n",
-       "      <td>0.05757</td>\n",
-       "      <td>-0.02090</td>\n",
-       "      <td>-0.02090</td>\n",
-       "      <td>-0.02090</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2017-07-16</td>\n",
-       "      <td>12.15634</td>\n",
-       "      <td>12.31097</td>\n",
-       "      <td>12.47849</td>\n",
-       "      <td>12.16617</td>\n",
-       "      <td>12.32353</td>\n",
-       "      <td>12.48354</td>\n",
-       "      <td>0.00126</td>\n",
-       "      <td>0.02284</td>\n",
-       "      <td>0.04260</td>\n",
-       "      <td>-0.03211</td>\n",
-       "      <td>-0.03211</td>\n",
-       "      <td>-0.03211</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2017-07-23</td>\n",
-       "      <td>12.13226</td>\n",
-       "      <td>12.29340</td>\n",
-       "      <td>12.45650</td>\n",
-       "      <td>12.15828</td>\n",
-       "      <td>12.31989</td>\n",
-       "      <td>12.48232</td>\n",
-       "      <td>0.00114</td>\n",
-       "      <td>0.02075</td>\n",
-       "      <td>0.03869</td>\n",
-       "      <td>-0.04732</td>\n",
-       "      <td>-0.04732</td>\n",
-       "      <td>-0.04732</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2017-07-30</td>\n",
-       "      <td>12.12414</td>\n",
-       "      <td>12.27593</td>\n",
-       "      <td>12.43266</td>\n",
-       "      <td>12.16706</td>\n",
-       "      <td>12.32113</td>\n",
-       "      <td>12.47936</td>\n",
-       "      <td>0.00126</td>\n",
-       "      <td>0.02284</td>\n",
-       "      <td>0.04260</td>\n",
-       "      <td>-0.06621</td>\n",
-       "      <td>-0.06621</td>\n",
-       "      <td>-0.06621</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week  prediction_5  prediction  prediction_95  trend_5    trend  \\\n",
-       "0 2017-07-02      12.17602    12.33509       12.49622 12.16346 12.32028   \n",
-       "1 2017-07-09      12.17908    12.33787       12.48704 12.16675 12.32876   \n",
-       "2 2017-07-16      12.15634    12.31097       12.47849 12.16617 12.32353   \n",
-       "3 2017-07-23      12.13226    12.29340       12.45650 12.15828 12.31989   \n",
-       "4 2017-07-30      12.12414    12.27593       12.43266 12.16706 12.32113   \n",
-       "\n",
-       "   trend_95  regression_5  regression  regression_95  seasonality_52_5  \\\n",
-       "0  12.48274       0.00148     0.02692        0.05021          -0.01367   \n",
-       "1  12.48605       0.00170     0.03087        0.05757          -0.02090   \n",
-       "2  12.48354       0.00126     0.02284        0.04260          -0.03211   \n",
-       "3  12.48232       0.00114     0.02075        0.03869          -0.04732   \n",
-       "4  12.47936       0.00126     0.02284        0.04260          -0.06621   \n",
-       "\n",
-       "   seasonality_52  seasonality_52_95  \n",
-       "0        -0.01367           -0.01367  \n",
-       "1        -0.02090           -0.02090  \n",
-       "2        -0.03211           -0.03211  \n",
-       "3        -0.04732           -0.04732  \n",
-       "4        -0.06621           -0.06621  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predicted_df = ktr.predict(df=test_df, decompose=True)\n",
-    "predicted_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:26.396865Z",
-     "start_time": "2022-01-26T02:06:26.363892Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'SMAPE: 0.71%'"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f\"SMAPE: {smape(predicted_df['prediction'].values, test_df[RESPONSE_COL].values):.2%}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:28.160116Z",
-     "start_time": "2022-01-26T02:06:27.848385Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_predicted_data(training_actual_df=train_df,\n",
-    "                        predicted_df=predicted_df,\n",
-    "                        date_col=DATE_COL,\n",
-    "                        actual_col=RESPONSE_COL,\n",
-    "                        test_actual_df=test_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:29.539887Z",
-     "start_time": "2022-01-26T02:06:29.500820Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>step</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.11035</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2011-07-03</td>\n",
-       "      <td>78</td>\n",
-       "      <td>0.08710</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2012-12-30</td>\n",
-       "      <td>156</td>\n",
-       "      <td>0.11927</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2014-06-29</td>\n",
-       "      <td>234</td>\n",
-       "      <td>0.06504</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2015-12-27</td>\n",
-       "      <td>312</td>\n",
-       "      <td>0.09255</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>2017-06-25</td>\n",
-       "      <td>390</td>\n",
-       "      <td>0.13490</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week  step  trend.unemploy\n",
-       "0 2010-01-03     0         0.11035\n",
-       "1 2011-07-03    78         0.08710\n",
-       "2 2012-12-30   156         0.11927\n",
-       "3 2014-06-29   234         0.06504\n",
-       "4 2015-12-27   312         0.09255\n",
-       "5 2017-06-25   390         0.13490"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "knot_df = ktr.get_regression_coef_knots()\n",
-    "knot_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### KTR - Median"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:44.721706Z",
-     "start_time": "2022-01-26T02:06:31.464229Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:Optimizing(PyStan) with algorithm:LBFGS .\n",
-      "INFO:orbit:Using SVI(Pyro) with steps:1000 , samples:1000 , learning rate:0.1, learning_rate_total_decay:1.0 and particles:100 .\n",
-      "INFO:root:Guessed max_plate_nesting = 1\n",
-      "INFO:orbit:step    0 loss = 31.752, scale = 0.096946\n",
-      "INFO:orbit:step  100 loss = -346.54, scale = 0.073014\n",
-      "INFO:orbit:step  200 loss = -347.01, scale = 0.072465\n",
-      "INFO:orbit:step  300 loss = -348.48, scale = 0.073846\n",
-      "INFO:orbit:step  400 loss = -345.43, scale = 0.073653\n",
-      "INFO:orbit:step  500 loss = -344.18, scale = 0.072283\n",
-      "INFO:orbit:step  600 loss = -345.95, scale = 0.07405\n",
-      "INFO:orbit:step  700 loss = -346.15, scale = 0.073452\n",
-      "INFO:orbit:step  800 loss = -343.87, scale = 0.076183\n",
-      "INFO:orbit:step  900 loss = -344.45, scale = 0.074191\n"
-     ]
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ktr = KTR(\n",
-    "    date_col=DATE_COL,\n",
-    "    response_col=RESPONSE_COL,\n",
-    "    regressor_col=['trend.unemploy', 'trend.filling', 'trend.job'],\n",
-    "    seasonality=[52],\n",
-    "    seasonality_fs_order=[3],\n",
-    "    level_knot_scale=.1,\n",
-    "    level_segments=10,\n",
-    "    seasonality_segments=2,\n",
-    "    regression_segments=5,\n",
-    "    regression_rho=0.15,\n",
-    "    # pyro optimization parameters\n",
-    "    seed=8888,\n",
-    "    num_steps=1000,\n",
-    "    num_sample=1000,\n",
-    "    learning_rate=0.1,\n",
-    "    estimator='pyro-svi',\n",
-    "    n_bootstrap_draws=-1\n",
-    ")\n",
-    "\n",
-    "ktr.fit(df=train_df, point_method='median')\n",
-    "ktr.get_regression_coefs().head()\n",
-    "\n",
-    "predicted_df = ktr.predict(df=test_df, decompose=True)\n",
-    "predicted_df.tail()\n",
-    "\n",
-    "f\"SMAPE: {smape(predicted_df['prediction'].values, test_df[RESPONSE_COL].values):.2%}\"\n",
-    "\n",
-    "_ = plot_predicted_data(training_actual_df=train_df,\n",
-    "                        predicted_df=predicted_df,\n",
-    "                        date_col=DATE_COL,\n",
-    "                        actual_col=RESPONSE_COL,\n",
-    "                        test_actual_df=test_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Electricity data (dual seasoanlity, no regressor)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:45.421270Z",
-     "start_time": "2022-01-26T02:06:44.724518Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(3288, 2)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>electricity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2000-01-01</td>\n",
-       "      <td>9.43760</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2000-01-02</td>\n",
-       "      <td>9.50130</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2000-01-03</td>\n",
-       "      <td>9.63565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2000-01-04</td>\n",
-       "      <td>9.65392</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2000-01-05</td>\n",
-       "      <td>9.66089</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        date  electricity\n",
-       "0 2000-01-01      9.43760\n",
-       "1 2000-01-02      9.50130\n",
-       "2 2000-01-03      9.63565\n",
-       "3 2000-01-04      9.65392\n",
-       "4 2000-01-05      9.66089"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# from 2000-01-01 to 2008-12-31\n",
-    "df = load_electricity_demand()\n",
-    "\n",
-    "df['electricity'] = np.log(df['electricity'])\n",
-    "\n",
-    "DATE_COL = 'date'\n",
-    "RESPONSE_COL = 'electricity'\n",
-    "\n",
-    "print(df.shape)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:45.455638Z",
-     "start_time": "2022-01-26T02:06:45.423898Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "test_size = 365\n",
-    "\n",
-    "train_df = df[:-test_size]\n",
-    "test_df = df[-test_size:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:06:45.498821Z",
-     "start_time": "2022-01-26T02:06:45.458003Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ktr = KTR(\n",
-    "    date_col=DATE_COL,\n",
-    "    response_col=RESPONSE_COL,\n",
-    "    seasonality=[7, 365.25],\n",
-    "    seasonality_fs_order=[2, 5],\n",
-    "    level_knot_scale=.1,\n",
-    "    level_segments=20,\n",
-    "    seasonality_segments=3,\n",
-    "    regression_segments=5,\n",
-    "    regression_rho=0.15,\n",
-    "    # pyro optimization parameters\n",
-    "    seed=8888,\n",
-    "    num_steps=1000,\n",
-    "    num_sample=1000,\n",
-    "    learning_rate=0.1,\n",
-    "    estimator='pyro-svi',\n",
-    "    n_bootstrap_draws=-1\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:07:31.129106Z",
-     "start_time": "2022-01-26T02:06:45.500909Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:Optimizing(PyStan) with algorithm:LBFGS .\n",
-      "INFO:orbit:Using SVI(Pyro) with steps:1000 , samples:1000 , learning rate:0.1, learning_rate_total_decay:1.0 and particles:100 .\n",
-      "INFO:root:Guessed max_plate_nesting = 1\n",
-      "INFO:orbit:step    0 loss = -2427.7, scale = 0.081873\n",
-      "INFO:orbit:step  100 loss = -4735.7, scale = 0.0060206\n",
-      "INFO:orbit:step  200 loss = -4709.7, scale = 0.00632\n",
-      "INFO:orbit:step  300 loss = -4652.6, scale = 0.006399\n",
-      "INFO:orbit:step  400 loss = -4574.9, scale = 0.0064561\n",
-      "INFO:orbit:step  500 loss = -4710.8, scale = 0.0063456\n",
-      "INFO:orbit:step  600 loss = -4674.2, scale = 0.0064545\n",
-      "INFO:orbit:step  700 loss = -4689.3, scale = 0.006469\n",
-      "INFO:orbit:step  800 loss = -4655.4, scale = 0.0062099\n",
-      "INFO:orbit:step  900 loss = -4657.3, scale = 0.0063414\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x13f867190>"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ktr.fit(df=train_df, point_method='median')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:07:31.195271Z",
-     "start_time": "2022-01-26T02:07:31.147205Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>prediction</th>\n",
-       "      <th>trend</th>\n",
-       "      <th>regression</th>\n",
-       "      <th>seasonality_7</th>\n",
-       "      <th>seasonality_365.25</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>360</th>\n",
-       "      <td>2008-12-27</td>\n",
-       "      <td>9.97291</td>\n",
-       "      <td>9.99328</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>-0.02937</td>\n",
-       "      <td>0.00900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>361</th>\n",
-       "      <td>2008-12-28</td>\n",
-       "      <td>9.91402</td>\n",
-       "      <td>9.99328</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>-0.08773</td>\n",
-       "      <td>0.00846</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>362</th>\n",
-       "      <td>2008-12-29</td>\n",
-       "      <td>9.97976</td>\n",
-       "      <td>9.99328</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>-0.02162</td>\n",
-       "      <td>0.00810</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>363</th>\n",
-       "      <td>2008-12-30</td>\n",
-       "      <td>10.04459</td>\n",
-       "      <td>9.99328</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.04336</td>\n",
-       "      <td>0.00794</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>364</th>\n",
-       "      <td>2008-12-31</td>\n",
-       "      <td>10.02630</td>\n",
-       "      <td>9.99328</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.02505</td>\n",
-       "      <td>0.00797</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          date  prediction   trend  regression  seasonality_7  \\\n",
-       "360 2008-12-27     9.97291 9.99328     0.00000       -0.02937   \n",
-       "361 2008-12-28     9.91402 9.99328     0.00000       -0.08773   \n",
-       "362 2008-12-29     9.97976 9.99328     0.00000       -0.02162   \n",
-       "363 2008-12-30    10.04459 9.99328     0.00000        0.04336   \n",
-       "364 2008-12-31    10.02630 9.99328     0.00000        0.02505   \n",
-       "\n",
-       "     seasonality_365.25  \n",
-       "360             0.00900  \n",
-       "361             0.00846  \n",
-       "362             0.00810  \n",
-       "363             0.00794  \n",
-       "364             0.00797  "
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predicted_df = ktr.predict(df=test_df, decompose=True)\n",
-    "predicted_df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:07:31.230313Z",
-     "start_time": "2022-01-26T02:07:31.197564Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'SMAPE: 0.49%'"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f\"SMAPE: {smape(predicted_df['prediction'].values, test_df[RESPONSE_COL].values):.2%}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:07:31.640381Z",
-     "start_time": "2022-01-26T02:07:31.232742Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_predicted_data(training_actual_df=train_df,\n",
-    "                        predicted_df=predicted_df,\n",
-    "                        date_col=DATE_COL,\n",
-    "                        actual_col=RESPONSE_COL,\n",
-    "                        test_actual_df=test_df,\n",
-    "                       lw=0.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "p37_2022",
-   "language": "python",
-   "name": "dev"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.12"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/ktr_simulation.ipynb b/examples/ktr_simulation.ipynb
index 4e5e1b54..5e6dc538 100644
--- a/examples/ktr_simulation.ipynb
+++ b/examples/ktr_simulation.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 1,
    "id": "speaking-david",
    "metadata": {
     "ExecuteTime": {
@@ -18,16 +18,7 @@
      "start_time": "2021-09-03T01:37:02.632658Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import math\n",
     "from math import pi\n",
@@ -39,8 +30,8 @@
     "from scipy import stats\n",
     "from scipy.spatial.distance import cdist\n",
     "\n",
-    "from orbit.estimators.pyro_estimator import PyroEstimatorVI\n",
-    "from orbit.estimators.stan_estimator import StanEstimatorMAP\n",
+    "# from orbit.estimators.pyro_estimator import PyroEstimatorVI\n",
+    "# from orbit.estimators.stan_estimator import StanEstimatorMAP\n",
     "from orbit.diagnostics.metrics import smape\n",
     "from orbit.utils.features import make_fourier_series_df, make_fourier_series\n",
     "from orbit.diagnostics.plot import plot_predicted_data\n",
@@ -594,7 +585,7 @@
     {
      "data": {
       "text/plain": [
-       "array([  0,  39,  79, 119, 159, 199, 239, 279, 319, 359, 399])"
+       "array([  0,  40,  80, 120, 160, 200, 239, 279, 319, 359, 399])"
       ]
      },
      "execution_count": 6,
@@ -721,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "id": "a1d4840c",
    "metadata": {
     "ExecuteTime": {
@@ -729,7 +720,18 @@
      "start_time": "2021-09-03T01:37:06.045489Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index([0, 181, 251], dtype='int64')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "knot_idx = get_knot_idx(date_array=date_array, knot_dates=knot_dates)\n",
     "knot_idx"
@@ -764,20 +766,7 @@
     {
      "data": {
       "text/plain": [
-       "array(['2018-01-01T00:00:00.000000000', '2018-01-10T00:00:00.000000000',\n",
-       "       '2018-01-20T00:00:00.000000000', '2018-01-30T00:00:00.000000000',\n",
-       "       '2018-02-09T00:00:00.000000000', '2018-02-19T00:00:00.000000000',\n",
-       "       '2018-03-01T00:00:00.000000000', '2018-03-11T00:00:00.000000000',\n",
-       "       '2018-03-21T00:00:00.000000000', '2018-03-31T00:00:00.000000000',\n",
-       "       '2018-04-10T00:00:00.000000000', '2018-04-20T00:00:00.000000000',\n",
-       "       '2018-04-30T00:00:00.000000000', '2018-05-10T00:00:00.000000000',\n",
-       "       '2018-05-20T00:00:00.000000000', '2018-05-30T00:00:00.000000000',\n",
-       "       '2018-06-09T00:00:00.000000000', '2018-06-19T00:00:00.000000000',\n",
-       "       '2018-06-29T00:00:00.000000000', '2018-07-09T00:00:00.000000000',\n",
-       "       '2018-07-19T00:00:00.000000000', '2018-07-29T00:00:00.000000000',\n",
-       "       '2018-08-08T00:00:00.000000000', '2018-08-18T00:00:00.000000000',\n",
-       "       '2018-08-28T00:00:00.000000000', '2018-09-07T00:00:00.000000000'],\n",
-       "      dtype='datetime64[ns]')"
+       "DatetimeIndex(['2018-01-01', '2018-07-01', '2018-09-09'], dtype='datetime64[ns]', freq=None)"
       ]
      },
      "execution_count": 13,
@@ -791,7 +780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "388d84e1",
    "metadata": {
     "ExecuteTime": {
@@ -799,7 +788,18 @@
      "start_time": "2021-09-03T01:37:06.175737Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2018-01-01', '2018-07-01', '2018-09-09'], dtype='datetime64[ns]', freq=None)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "get_knot_dates(date_array[0], knot_idx, infer_freq)"
    ]
@@ -824,7 +824,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "id": "b671b470",
    "metadata": {
     "ExecuteTime": {
@@ -839,7 +839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "id": "promotional-reach",
    "metadata": {
     "ExecuteTime": {
@@ -855,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "id": "opposite-bowling",
    "metadata": {
     "ExecuteTime": {
@@ -863,15 +863,7 @@
      "start_time": "2021-09-03T01:37:06.318893Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:root:Using 501 steps, 1000 samples, 0.2 learning rate and 100 particles for SVI.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ktrx_neutral = KTR(\n",
     "    response_col='y',\n",
@@ -902,17 +894,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "id": "93b6e994",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-09-03T01:37:13.029263Z",
      "start_time": "2021-09-03T01:37:06.361655Z"
     },
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    },
     "tags": []
    },
    "outputs": [
@@ -920,6468 +908,41 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4 NOW.\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:701:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/numpy/core/include/numpy/arrayobject.h:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/numpy/core/include/numpy/ndarrayobject.h:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/numpy/core/include/numpy/ndarraytypes.h:1944:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: \"Using deprecated NumPy API, disable it with \"          \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-W#warnings]\n",
-      "#warning \"Using deprecated NumPy API, disable it with \" \\\n",
-      " ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:52:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/err/is_cholesky_factor_corr.hpp:5:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/err/is_cholesky_factor.hpp:6:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/err/is_lower_triangular.hpp:25:27: warning: 'ptr_fun<double, double>' is deprecated [-Wdeprecated-declarations]\n",
-      "  return y.unaryExpr(std::ptr_fun(internal::notNan))\n",
-      "                          ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/functional:1107:1: note: 'ptr_fun<double, double>' has been explicitly marked deprecated here\n",
-      "_LIBCPP_DEPRECATED_IN_CXX11 inline _LIBCPP_INLINE_VISIBILITY\n",
-      "^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/__config:1046:39: note: expanded from macro '_LIBCPP_DEPRECATED_IN_CXX11'\n",
-      "#  define _LIBCPP_DEPRECATED_IN_CXX11 _LIBCPP_DEPRECATED\n",
-      "                                      ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/__config:1035:48: note: expanded from macro '_LIBCPP_DEPRECATED'\n",
-      "#    define _LIBCPP_DEPRECATED __attribute__ ((deprecated))\n",
-      "                                               ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:711:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:841:12: warning: 'auto_ptr<stan::io::var_context>' is deprecated [-Wdeprecated-declarations]\n",
-      "      std::auto_ptr<stan::io::var_context> init_context_ptr;\n",
-      "           ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/memory:1851:28: note: 'auto_ptr<stan::io::var_context>' has been explicitly marked deprecated here\n",
-      "class _LIBCPP_TEMPLATE_VIS _LIBCPP_DEPRECATED_IN_CXX11 auto_ptr\n",
-      "                           ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/__config:1046:39: note: expanded from macro '_LIBCPP_DEPRECATED_IN_CXX11'\n",
-      "#  define _LIBCPP_DEPRECATED_IN_CXX11 _LIBCPP_DEPRECATED\n",
-      "                                      ^\n",
-      "/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/__config:1035:48: note: expanded from macro '_LIBCPP_DEPRECATED'\n",
-      "#    define _LIBCPP_DEPRECATED __attribute__ ((deprecated))\n",
-      "                                               ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9470:30: warning: comparison of integers of different signs: 'Py_ssize_t' (aka 'long') and 'std::__1::vector<std::__1::basic_string<char>>::size_type' (aka 'unsigned long') [-Wsign-compare]\n",
-      "    __pyx_t_12 = ((__pyx_t_9 != __pyx_v_fitptr->param_names_oi().size()) != 0);\n",
-      "                   ~~~~~~~~~ ^  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:473:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixVector.h:186:39: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "  const Index offset1 = (FirstAligned && alignmentStep==1)?3:1;\n",
-      "                                      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/GeneralProduct.h:239:131: note: in instantiation of member function 'Eigen::internal::general_matrix_vector_product<long, double, Eigen::internal::const_blas_data_mapper<double, long, 0>, 0, false, double, Eigen::internal::const_blas_data_mapper<double, long, 1>, false, 0>::run' requested here\n",
-      "          <Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(\n",
-      "                                                                                                                                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:377:31: note: in instantiation of function template specialization 'Eigen::internal::gemv_dense_selector<2, 0, true>::run<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>' requested here\n",
-      "                           >::run(actual_lhs, actual_rhs, dst, alpha);\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:355:14: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::DenseShape, Eigen::DenseShape, 7>::scaleAndAddTo<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>' requested here\n",
-      "  { Derived::scaleAndAddTo(dst,lhs,rhs,alpha); }\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:351:5: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl_base<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::internal::generic_product_impl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::DenseShape, Eigen::DenseShape, 7>>::scaleAndAddTo<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>' requested here\n",
-      "  { scaleAndAddTo(dst, lhs, rhs, Scalar(-1)); }\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:178:37: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl_base<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::internal::generic_product_impl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::DenseShape, Eigen::DenseShape, 7>>::subTo<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>' requested here\n",
-      "    generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());\n",
-      "                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:473:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixVector.h:187:39: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "  const Index offset3 = (FirstAligned && alignmentStep==1)?1:3;\n",
-      "                                      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 12 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 10 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 12 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:45:13: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:322:21: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>::operator/=' requested here\n",
-      "      if (rs>0) A21 /= x;\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 10 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 12 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:321:38: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, Eigen::Transpose<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>>, 0>>' requested here\n",
-      "      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();\n",
-      "                                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:45:13: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:322:21: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, false>>::operator/=' requested here\n",
-      "      if (rs>0) A21 /= x;\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 6 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SolveTriangular.h:182:11: note: in instantiation of member function 'Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>::operator=' requested here\n",
-      "    other = otherCopy;\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:353:72: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2, Eigen::Dense>::solveInPlace<2, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);\n",
-      "                                                                       ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, 4, 4, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 4, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, 4, 4, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 4, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, 4, 4, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 4, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, 4, 4, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 4, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, 4, 4, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 4, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/SelfadjointProduct.h:126:59: note: in instantiation of member function 'Eigen::selfadjoint_product_selector<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, false>::run' requested here\n",
-      "  selfadjoint_product_selector<MatrixType,DerivedU,UpLo>::run(_expression().const_cast_derived(), u.derived(), alpha);\n",
-      "                                                          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:354:54: note: in instantiation of function template specialization 'Eigen::SelfAdjointView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>::rankUpdate<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,typename NumTraits<RealScalar>::Literal(-1)); // bottleneck\n",
-      "                                                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Matrix<double, -1, -1, 0>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:84:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  llt.compute(m);\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:736:51: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>\n",
-      "                                                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:799:10: note: in instantiation of template class 'Eigen::internal::triangular_dense_assignment_kernel<1, 0, 1, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:838:5: note: in instantiation of function template specialization 'Eigen::internal::call_triangular_assignment_loop<1, true, Eigen::Matrix<double, -1, -1, 0>, Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_triangular_assignment_loop<SrcXprType::Mode, (SrcXprType::Mode&SelfAdjoint)==0>(dst, src, func);  \n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Triangular2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Assign.h:75:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>' requested here\n",
-      "  internal::call_assignment(derived(), other.derived());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:571:13: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::operator=<Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>' requested here\n",
-      "      Base::operator=(other.derived());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:548:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::operator=<Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>' requested here\n",
-      "      *this = other.derived();\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::PlainObjectBase<Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:86:10: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::TriangularView<const Eigen::Matrix<double, -1, -1, 0>, 1>>' requested here\n",
-      "  return llt.matrixL();\n",
-      "         ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:278:15: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "        Base::_set_noalias(other);\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/cholesky_decompose.hpp:86:3: note: in instantiation of member function 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix' requested here\n",
-      "  return llt.matrixL();\n",
-      "  ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::PlainObjectBase<Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/multiply_lower_tri_self_transpose.hpp:29:17: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "  matrix_d Lt = L.transpose();\n",
-      "                ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:391:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:431:20: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      lazyproduct::evalTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 0>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::PlainObjectBase<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 0>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/tcrossprod.hpp:20:12: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<double, -1, -1, 0>>, 0>>' requested here\n",
-      "    return M * M.transpose();\n",
-      "           ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::PlainObjectBase<Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:63:27: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>' requested here\n",
-      "      Eigen::VectorXd B = b.col(col);\n",
-      "                          ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:278:15: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "        Base::_set_noalias(other);\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:63:23: note: in instantiation of member function 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix' requested here\n",
-      "      Eigen::VectorXd B = b.col(col);\n",
-      "                      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:70:15: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "            B = t * A * B / (s * j);\n",
-      "              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 15 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:70:15: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "            B = t * A * B / (s * j);\n",
-      "              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 13 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:70:15: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "            B = t * A * B / (s * j);\n",
-      "              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 15 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:70:15: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "            B = t * A * B / (s * j);\n",
-      "              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:72:15: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Matrix<double, -1, 1, 0>>::operator+=<Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "            F += B;\n",
-      "              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::mul_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:21:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:80:11: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Matrix<double, -1, 1, 0>>::operator*=' requested here\n",
-      "        F *= eta;\n",
-      "          ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Assign.h:66:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::call_assignment(derived(), other.derived());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:341:5: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>::operator=<Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:161:5: note: in instantiation of function template specialization 'Eigen::internal::BlockImpl_dense<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true, true>::operator=<Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:111:5: note: in instantiation of function template specialization 'Eigen::BlockImpl<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true, Eigen::Dense>::operator=<Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:85:20: note: in instantiation of function template specialization 'Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>::operator=<Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "      res.col(col) = F;\n",
-      "                   ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::PlainObjectBase<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:112:27: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      Eigen::MatrixXd a = mat * t;\n",
-      "                          ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:391:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:431:20: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      lazyproduct::evalTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/EigenBase.h:103:9: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>>' requested here\n",
-      "    dst = dst * this->derived();\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/MatrixBase.h:500:19: note: in instantiation of function template specialization 'Eigen::EigenBase<Eigen::Matrix<double, -1, -1, 0>>::applyThisOnTheRight<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  other.derived().applyThisOnTheRight(derived());\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/matrix_exp_action_handler.hpp:115:11: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::operator*=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "        a *= a;\n",
-      "          ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, 1, -1, 1>, Eigen::Matrix<double, 1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, 1, -1, 1>, Eigen::Matrix<double, 1, -1, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, 1, -1, 1>, Eigen::Matrix<double, 1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:278:15: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, 1, -1, 1>>::_set_noalias<Eigen::Matrix<double, 1, -1, 1>>' requested here\n",
-      "        Base::_set_noalias(other);\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/to_row_vector.hpp:35:10: note: in instantiation of member function 'Eigen::Matrix<double, 1, -1, 1, 1, -1>::Matrix' requested here\n",
-      "  return result;\n",
-      "         ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_init1<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_covar_estimator.hpp:13:9: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "      : m_(Eigen::VectorXd::Zero(n)), m2_(Eigen::MatrixXd::Zero(n, n)) {\n",
-      "        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_init1<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_covar_estimator.hpp:13:39: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "      : m_(Eigen::VectorXd::Zero(n)), m2_(Eigen::MatrixXd::Zero(n, n)) {\n",
-      "                                      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_init1<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_covar_estimator.hpp:26:21: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "    Eigen::VectorXd delta(q - m_);\n",
-      "                    ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_covar_estimator.hpp:27:8: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Matrix<double, -1, 1, 0>>::operator+=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "    m_ += delta / num_samples_;\n",
-      "       ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_init1<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>, Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:796:41: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>>' requested here\n",
-      "  typename plain_matrix_type<Src>::type tmp(src);\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_covar_estimator.hpp:28:9: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::operator+=<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>>' requested here\n",
-      "    m2_ += (q - m_) * delta.transpose();\n",
-      "        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:797:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, tmp, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_covar_estimator.hpp:28:9: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::operator+=<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, 1, 0>>, 0>>' requested here\n",
-      "    m2_ += (q - m_) * delta.transpose();\n",
-      "        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_covar_estimator.hpp:37:13: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      covar = m2_ / (num_samples_ - 1.0);\n",
-      "            ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_var_estimator.hpp:28:9: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Matrix<double, -1, 1, 0>>::operator+=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "    m2_ += delta.cwiseProduct(q - m_);\n",
-      "        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/welford_var_estimator.hpp:37:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      var = m2_ / (num_samples_ - 1.0);\n",
-      "          ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:711:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:15:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/additive_combine.hpp:27:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/linear_congruential.hpp:140:20: warning: overlapping comparisons always evaluate to false [-Wtautological-overlap-compare]\n",
-      "        if(_x <= 0 && _x != 0) {\n",
-      "           ~~~~~~~~^~~~~~~~~~\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/linear_congruential.hpp:393:11: note: in instantiation of member function 'boost::random::linear_congruential_engine<unsigned long long, 25214903917, 11, 281474976710656>::seed' requested here\n",
-      "    { lcf.seed(cnv(x0)); }\n",
-      "          ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Swap.h:20:11: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::swap_assign_op<double>, 1>' requested here\n",
-      " : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn>\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:736:51: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::swap_assign_op<double>, 0>' requested here\n",
-      "class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>\n",
-      "                                                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:799:10: note: in instantiation of template class 'Eigen::internal::triangular_dense_assignment_kernel<2, 8, 0, Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::swap_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:847:5: note: in instantiation of function template specialization 'Eigen::internal::call_triangular_assignment_loop<10, false, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);  \n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:533:7: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "      call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:256:59: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10, Eigen::Dense>::swap<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>' requested here\n",
-      "      m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose());\n",
-      "                                                          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:288:50: note: in instantiation of member function 'Eigen::internal::inplace_transpose_selector<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, false, false>::run' requested here\n",
-      "  internal::inplace_transpose_selector<Derived>::run(derived());\n",
-      "                                                 ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:109:7: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>::transposeInPlace' requested here\n",
-      "    L.transposeInPlace();\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 1>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 1>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 1>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 1>>::_init1<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/DenseBase.h:406:14: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 1, -1, -1>::Matrix<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>' requested here\n",
-      "      return typename internal::eval<Derived>::type(derived());\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:258:25: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>::eval' requested here\n",
-      "      m = m.transpose().eval();\n",
-      "                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:288:50: note: in instantiation of member function 'Eigen::internal::inplace_transpose_selector<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, false, false>::run' requested here\n",
-      "  internal::inplace_transpose_selector<Derived>::run(derived());\n",
-      "                                                 ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:109:7: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>::transposeInPlace' requested here\n",
-      "    L.transposeInPlace();\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 1>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 1>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 1>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:278:15: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 1>>::_set_noalias<Eigen::Matrix<double, -1, -1, 1>>' requested here\n",
-      "        Base::_set_noalias(other);\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/DenseBase.h:406:14: note: in instantiation of member function 'Eigen::Matrix<double, -1, -1, 1, -1, -1>::Matrix' requested here\n",
-      "      return typename internal::eval<Derived>::type(derived());\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:258:25: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>::eval' requested here\n",
-      "      m = m.transpose().eval();\n",
-      "                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:288:50: note: in instantiation of member function 'Eigen::internal::inplace_transpose_selector<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, false, false>::run' requested here\n",
-      "  internal::inplace_transpose_selector<Derived>::run(derived());\n",
-      "                                                 ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:109:7: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>::transposeInPlace' requested here\n",
-      "    L.transposeInPlace();\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 3 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:161:5: note: in instantiation of function template specialization 'Eigen::internal::BlockImpl_dense<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false, true>::operator=<Eigen::Matrix<double, -1, -1, 1>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:111:5: note: in instantiation of function template specialization 'Eigen::BlockImpl<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false, Eigen::Dense>::operator=<Eigen::Matrix<double, -1, -1, 1>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:258:9: note: in instantiation of function template specialization 'Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>::operator=<Eigen::Matrix<double, -1, -1, 1>>' requested here\n",
-      "      m = m.transpose().eval();\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:288:50: note: in instantiation of member function 'Eigen::internal::inplace_transpose_selector<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, false, false>::run' requested here\n",
-      "  internal::inplace_transpose_selector<Derived>::run(derived());\n",
-      "                                                 ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:109:7: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>::transposeInPlace' requested here\n",
-      "    L.transposeInPlace();\n",
-      "      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, 4, 4, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, 4, 4, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Diagonal<Eigen::Matrix<double, 4, 4, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Diagonal<Eigen::Matrix<double, 4, 4, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Diagonal<Eigen::Matrix<double, 4, 4, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 4, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 12 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, 0>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_init1<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, 0>, Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, 0>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/DenseBase.h:406:14: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, 0>>' requested here\n",
-      "      return typename internal::eval<Derived>::type(derived());\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:110:49: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, 0>>::eval' requested here\n",
-      "    L_adj = (L * L_adj.triangularView<Lower>()).eval();\n",
-      "                                                ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Assign.h:66:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  internal::call_assignment(derived(), other.derived());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:341:5: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>::operator=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:161:5: note: in instantiation of function template specialization 'Eigen::internal::BlockImpl_dense<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false, true>::operator=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:111:5: note: in instantiation of function template specialization 'Eigen::BlockImpl<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false, Eigen::Dense>::operator=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:110:11: note: in instantiation of function template specialization 'Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>::operator=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "    L_adj = (L * L_adj.triangularView<Lower>()).eval();\n",
-      "          ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:736:51: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>\n",
-      "                                                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:799:10: note: in instantiation of template class 'Eigen::internal::triangular_dense_assignment_kernel<2, 8, 0, Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:829:5: note: in instantiation of function template specialization 'Eigen::internal::call_triangular_assignment_loop<10, false, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);  \n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>, Eigen::internal::assign_op<double, double>, Eigen::internal::Triangular2Triangular, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:580:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>>' requested here\n",
-      "  internal::call_assignment(derived(), other.derived());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:112:9: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10, Eigen::Dense>::operator=<Eigen::TriangularView<const Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 10>>' requested here\n",
-      "        = L_adj.adjoint().triangularView<StrictlyUpper>();\n",
-      "        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::evaluator<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Assign.h:57:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>' requested here\n",
-      "  internal::call_assignment(derived(), other.derived());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Transpose.h:66:5: note: in instantiation of member function 'Eigen::MatrixBase<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>::operator=' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Transpose)\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:821:92: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) { Base::operator=(other); return *this; } \\\n",
-      "                                                                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SolveTriangular.h:182:11: note: in instantiation of member function 'Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>::operator=' requested here\n",
-      "    other = otherCopy;\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:511:14: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 2, Eigen::Dense>::solveInPlace<1, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>' requested here\n",
-      "    { return solveInPlace<OnTheLeft>(other); }\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:114:31: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 2, Eigen::Dense>::solveInPlace<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>' requested here\n",
-      "    L.triangularView<Upper>().solveInPlace(L_adj.transpose());\n",
-      "                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Assign.h:66:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>>' requested here\n",
-      "  internal::call_assignment(derived(), other.derived());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:341:5: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>::operator=<Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:161:5: note: in instantiation of function template specialization 'Eigen::internal::BlockImpl_dense<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false, true>::operator=<Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Block.h:111:5: note: in instantiation of function template specialization 'Eigen::BlockImpl<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false, Eigen::Dense>::operator=<Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:151:15: note: in instantiation of function template specialization 'Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>::operator=<Eigen::Transpose<const Eigen::Solve<Eigen::TriangularView<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, 2>, Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>>>>' requested here\n",
-      "        C_adj = D.transpose()\n",
-      "              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>>, Eigen::internal::sub_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:405:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:452:20: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::DenseShape, Eigen::DenseShape, 3>::subTo<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      lazyproduct::subTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:178:37: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::DenseShape, Eigen::DenseShape, 8>::subTo<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "    generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());\n",
-      "                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/NoAlias.h:58:7: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "      call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:155:25: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>>' requested here\n",
-      "        B_adj.noalias() -= C_adj * R;\n",
-      "                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>>, Eigen::internal::sub_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:405:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:452:20: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::DenseShape, Eigen::DenseShape, 3>::subTo<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "      lazyproduct::subTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:178:37: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::DenseShape, Eigen::DenseShape, 8>::subTo<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>' requested here\n",
-      "    generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());\n",
-      "                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/NoAlias.h:58:7: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "      call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:156:25: note: in instantiation of function template specialization 'Eigen::NoAlias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::MatrixBase>::operator-=<Eigen::Product<Eigen::Transpose<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>>' requested here\n",
-      "        D_adj.noalias() -= C_adj.transpose() * C;\n",
-      "                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::mul_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Diagonal<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Diagonal<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Diagonal<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:21:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Diagonal<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:161:24: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Diagonal<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 0>>::operator*=' requested here\n",
-      "      D_adj.diagonal() *= 0.5;\n",
-      "                       ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:736:51: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>\n",
-      "                                                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:799:10: note: in instantiation of template class 'Eigen::internal::triangular_dense_assignment_kernel<2, 8, 0, Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:847:5: note: in instantiation of function template specialization 'Eigen::internal::call_triangular_assignment_loop<10, false, Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);  \n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Triangular, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:560:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::TriangularView<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  internal::call_assignment_no_alias(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:394:20: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10, Eigen::Dense>::operator=<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "    { return *this = MatrixType::Constant(derived().rows(), derived().cols(), value); }\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:397:44: note: in instantiation of member function 'Eigen::TriangularViewImpl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10, Eigen::Dense>::setConstant' requested here\n",
-      "    TriangularViewType& setZero() { return setConstant(Scalar(0)); }\n",
-      "                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:162:45: note: in instantiation of member function 'Eigen::TriangularViewImpl<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 10, Eigen::Dense>::setZero' requested here\n",
-      "      D_adj.triangularView<StrictlyUpper>().setZero();\n",
-      "                                            ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, Eigen::Matrix<double, -1, -1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Ref.h:89:3: note: in instantiation of function template specialization 'Eigen::MatrixBase<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>::operator=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(RefBase)\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Ref.h:227:5: note: in instantiation of function template specialization 'Eigen::RefBase<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>::operator=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Ref)\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:839:53: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_OPERATORS'\n",
-      "#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)\n",
-      "                                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/util/Macros.h:823:108: note: expanded from macro 'EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR'\n",
-      "    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }\n",
-      "                                                                                                           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:428:12: note: in instantiation of function template specialization 'Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>::operator=<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  m_matrix = a.derived();\n",
-      "           ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 9 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 13 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:45:13: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:333:14: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "      return unblocked(m);\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 9 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 13 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:45:13: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:352:15: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::unblocked<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>' requested here\n",
-      "      if((ret=unblocked(A11))>=0) return k+ret;\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 7 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:353:72: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<const Eigen::Transpose<const Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>, 2, Eigen::Dense>::solveInPlace<2, Eigen::Block<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, -1, -1, false>>' requested here\n",
-      "      if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);\n",
-      "                                                                       ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:397:61: note: in instantiation of function template specialization 'Eigen::internal::llt_inplace<double, 1>::blocked<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>>' requested here\n",
-      "  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }\n",
-      "                                                            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:444:21: note: in instantiation of member function 'Eigen::internal::LLT_Traits<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::inplace_decomposition' requested here\n",
-      "  bool ok = Traits::inplace_decomposition(m_matrix);\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LLT.h:111:7: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:412:57: note: in instantiation of function template specialization 'Eigen::LLT<Eigen::Ref<Eigen::Matrix<double, -1, -1, 0>, 0, Eigen::OuterStride<-1>>, 1>::LLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  Eigen::LLT<Eigen::Ref<Eigen::MatrixXd>, Eigen::Lower> L_factor(L_A);\n",
-      "                                                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:278:15: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<stan::math::var, -1, -1, 0>>::_set_noalias<Eigen::Matrix<stan::math::var, -1, -1, 0>>' requested here\n",
-      "        Base::_set_noalias(other);\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/cholesky_decompose.hpp:451:10: note: in instantiation of member function 'Eigen::Matrix<stan::math::var, -1, -1, 0, -1, -1>::Matrix' requested here\n",
-      "  return L;\n",
-      "         ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<stan::math::var, -1, -1, 0>>::_set_noalias<Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<stan::math::var, -1, -1, 0>>::_init1<Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/crossprod.hpp:17:21: note: in instantiation of function template specialization 'Eigen::Matrix<stan::math::var, -1, -1, 0, -1, -1>::Matrix<Eigen::Transpose<const Eigen::Matrix<stan::math::var, -1, -1, 0>>>' requested here\n",
-      "  return tcrossprod(static_cast<matrix_v>(M.transpose()));\n",
-      "                    ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:278:15: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<stan::math::var, -1, 1, 0>>::_set_noalias<Eigen::Matrix<stan::math::var, -1, 1, 0>>' requested here\n",
-      "        Base::_set_noalias(other);\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/to_var.hpp:50:10: note: in instantiation of member function 'Eigen::Matrix<stan::math::var, -1, 1, 0, -1, 1>::Matrix' requested here\n",
-      "  return v_v;\n",
-      "         ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:278:15: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<stan::math::var, 1, -1, 1>>::_set_noalias<Eigen::Matrix<stan::math::var, 1, -1, 1>>' requested here\n",
-      "        Base::_set_noalias(other);\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/to_var.hpp:75:10: note: in instantiation of member function 'Eigen::Matrix<stan::math::var, 1, -1, 1, 1, -1>::Matrix' requested here\n",
-      "  return rv_v;\n",
-      "         ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set<Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/softmax.hpp:50:9: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>>' requested here\n",
-      "  theta = (v.array() - v.maxCoeff()).exp();\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/softmax.hpp:34:14: note: in instantiation of function template specialization 'stan::math::softmax<double>' requested here\n",
-      "    auto y = softmax(alpha);\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/functor/adj_jac_apply.hpp:414:40: note: in instantiation of function template specialization 'stan::math::internal::softmax_op::operator()<1>' requested here\n",
-      "    return build_return_varis_and_vars(f_(is_var_, value_of(args)...));\n",
-      "                                       ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::PlainObjectBase<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/softmax.hpp:51:10: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>' requested here\n",
-      "  return theta.array() / theta.sum();\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/fun/softmax.hpp:34:14: note: in instantiation of function template specialization 'stan::math::softmax<double>' requested here\n",
-      "    auto y = softmax(alpha);\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/rev/mat/functor/adj_jac_apply.hpp:414:40: note: in instantiation of function template specialization 'stan::math::internal::softmax_op::operator()<1>' requested here\n",
-      "    return build_return_varis_and_vars(f_(is_var_, value_of(args)...));\n",
-      "                                       ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:711:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:15:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/additive_combine.hpp:27:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/linear_congruential.hpp:140:20: warning: overlapping comparisons always evaluate to false [-Wtautological-overlap-compare]\n",
-      "        if(_x <= 0 && _x != 0) {\n",
-      "           ~~~~~~~~^~~~~~~~~~\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/additive_combine.hpp:82:16: note: in instantiation of member function 'boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>::seed' requested here\n",
-      "        _mlcg1.seed(seed_arg);\n",
-      "               ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/create_rng.hpp:29:27: note: in instantiation of member function 'boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>::additive_combine_engine' requested here\n",
-      "        boost::ecuyer1988 rng(seed);\n",
-      "                          ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:711:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:15:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/additive_combine.hpp:27:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/linear_congruential.hpp:140:20: warning: overlapping comparisons always evaluate to false [-Wtautological-overlap-compare]\n",
-      "        if(_x <= 0 && _x != 0) {\n",
-      "           ~~~~~~~~^~~~~~~~~~\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/boost_1.69.0/boost/random/additive_combine.hpp:83:16: note: in instantiation of member function 'boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>::seed' requested here\n",
-      "        _mlcg2.seed(seed_arg);\n",
-      "               ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/create_rng.hpp:29:27: note: in instantiation of member function 'boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>::additive_combine_engine' requested here\n",
-      "        boost::ecuyer1988 rng(seed);\n",
-      "                          ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:736:51: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>\n",
-      "                                                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:799:10: note: in instantiation of template class 'Eigen::internal::triangular_dense_assignment_kernel<1, 0, 0, Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:847:5: note: in instantiation of function template specialization 'Eigen::internal::call_triangular_assignment_loop<1, false, Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);  \n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Triangular, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:560:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  internal::call_assignment_no_alias(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:386:112: note: in instantiation of function template specialization 'Eigen::TriangularViewImpl<Eigen::Matrix<double, -1, -1, 0>, 1, Eigen::Dense>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Matrix<double, -1, -1, 0>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "    TriangularViewType&  operator/=(const typename internal::traits<MatrixType>::Scalar& other) { return *this = derived().nestedExpression() / other; }\n",
-      "                                                                                                               ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:435:40: note: in instantiation of member function 'Eigen::TriangularViewImpl<Eigen::Matrix<double, -1, -1, 0>, 1, Eigen::Dense>::operator/=' requested here\n",
-      "  mat.template triangularView<Lower>() /= scale;\n",
-      "                                       ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 10 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_quotient_op<double>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 12 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 13 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, -1, 1, false>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, -1, 1, false>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, -1, 1, false>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, -1, 1, false>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, -1, 1, false>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, -1, 1, false>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 4 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:445:5: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "    tridiagonalization_inplace(mat,hCoeffs);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 3 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:446:10: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>' requested here\n",
-      "    diag = mat.diagonal().real();\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, -1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, -1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, -1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, -1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, -1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 3 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:447:13: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, -1>>' requested here\n",
-      "    subdiag = mat.template diagonal<-1>().real();\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 14 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 10>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:736:51: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 10>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>\n",
-      "                                                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:799:10: note: in instantiation of template class 'Eigen::internal::triangular_dense_assignment_kernel<2, 8, 0, Eigen::internal::evaluator<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 10>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:847:5: note: in instantiation of function template specialization 'Eigen::internal::call_triangular_assignment_loop<10, false, Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 10>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);  \n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::TriangularView<Eigen::Matrix<double, -1, -1, 0>, 10>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Triangular, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/TriangularMatrix.h:560:13: note: (skipping 12 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment_no_alias(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::mul_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::mul_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:21:13: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 19 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 20 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 18 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 2>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 20 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>>, Eigen::internal::sub_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:164:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: (skipping 17 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::sub_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:164:3: note: (skipping 17 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 19 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Transpose<const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Transpose<const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Transpose<const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 21 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:473:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixVector.h:460:39: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "  const Index offset1 = (FirstAligned && alignmentStep==1)?3:1;\n",
-      "                                      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/GeneralProduct.h:334:129: note: in instantiation of member function 'Eigen::internal::general_matrix_vector_product<long, double, Eigen::internal::const_blas_data_mapper<double, long, 1>, 1, false, double, Eigen::internal::const_blas_data_mapper<double, long, 0>, false, 0>::run' requested here\n",
-      "        <Index,LhsScalar,LhsMapper,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(\n",
-      "                                                                                                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/GeneralProduct.h:192:9: note: in instantiation of function template specialization 'Eigen::internal::gemv_dense_selector<2, 1, true>::run<Eigen::Transpose<const Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>>, Eigen::Transpose<const Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>, Eigen::Transpose<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>>' requested here\n",
-      "      ::run(rhs.transpose(), lhs.transpose(), destT, alpha);\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:377:31: note: in instantiation of function template specialization 'Eigen::internal::gemv_dense_selector<1, 0, true>::run<Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>' requested here\n",
-      "                           >::run(actual_lhs, actual_rhs, dst, alpha);\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:355:14: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl<Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, Eigen::DenseShape, Eigen::DenseShape, 7>::scaleAndAddTo<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>' requested here\n",
-      "  { Derived::scaleAndAddTo(dst,lhs,rhs,alpha); }\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:343:20: note: in instantiation of function template specialization 'Eigen::internal::generic_product_impl_base<Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, Eigen::internal::generic_product_impl<Eigen::Transpose<const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, -1, -1, false>, Eigen::DenseShape, Eigen::DenseShape, 7>>::scaleAndAddTo<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>' requested here\n",
-      "  { dst.setZero(); scaleAndAddTo(dst, lhs, rhs, Scalar(1)); }\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:148:37: note: (skipping 13 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "    generic_product_impl<Lhs, Rhs>::evalTo(dst, src.lhs(), src.rhs());\n",
-      "                                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:473:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixVector.h:461:39: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "  const Index offset3 = (FirstAligned && alignmentStep==1)?1:3;\n",
-      "                                      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, 1, -1, 1>>, const Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, 1, -1, 1>>, const Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>>, Eigen::internal::sub_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, 1, -1, 1>>, const Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, 1, -1, 1>>, const Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, -1, false>, 1, -1, false>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, 1, -1, 1>>, const Eigen::Map<Eigen::Matrix<double, 1, -1, 1>, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:164:3: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, -1, 1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: (skipping 17 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 16 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:449:11: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::HouseholderSequence<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 1>>' requested here\n",
-      "      mat = HouseholderSequenceType(mat, hCoeffs.conjugate())\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/./Tridiagonalization.h:430:52: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace_selector<Eigen::Matrix<double, -1, -1, 0>, -1, false>::run<Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ);\n",
-      "                                                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:437:13: note: in instantiation of function template specialization 'Eigen::internal::tridiagonalization_inplace<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Swap.h:20:11: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::swap_assign_op<double>, 1>' requested here\n",
-      " : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn>\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::swap_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::internal::swap_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/DenseBase.h:418:7: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "      call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:540:24: note: in instantiation of function template specialization 'Eigen::DenseBase<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>::swap<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>' requested here\n",
-      "          eivec.col(i).swap(eivec.col(k+i));\n",
-      "                       ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:439:22: note: in instantiation of function template specialization 'Eigen::internal::computeFromTridiagonal_impl<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  m_info = internal::computeFromTridiagonal_impl(diag, m_subdiag, m_maxIterations, computeEigenvectors, m_eivec);\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h:168:7: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived(), options);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:21:47: note: in instantiation of function template specialization 'Eigen::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>::SelfAdjointEigenSolver<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      Eigen::SelfAdjointEigenSolver<matrix_d> solver(H);\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Product<Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Product<Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::Product<Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::PlainObjectBase<Eigen::Product<Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/newton.hpp:24:35: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::Product<Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, Eigen::Matrix<double, -1, 1, 0>, 0>>' requested here\n",
-      "      vector_d eigenprojections = eigenvectors.transpose() * g;\n",
-      "                                  ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set<Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseNullaryOp.h:747:14: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "    return m = Derived::Identity(m.rows(), m.cols());\n",
-      "             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseNullaryOp.h:776:47: note: in instantiation of member function 'Eigen::internal::setIdentity_impl<Eigen::Matrix<double, -1, -1, 0>, false>::run' requested here\n",
-      "  return internal::setIdentity_impl<Derived>::run(derived());\n",
-      "                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/mcmc/hmc/hamiltonians/dense_e_point.hpp:28:23: note: in instantiation of member function 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::setIdentity' requested here\n",
-      "        inv_e_metric_.setIdentity();\n",
-      "                      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::PlainObjectBase<Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/to_matrix.hpp:118:10: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::Map<const Eigen::Matrix<double, -1, -1, 0>, 0>>' requested here\n",
-      "  return Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> >(\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/read_dense_inv_metric.hpp:38:25: note: in instantiation of function template specialization 'stan::math::to_matrix<double>' requested here\n",
-      "            stan::math::to_matrix(dense_vals, num_params, num_params);\n",
-      "                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Swap.h:20:11: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>>, Eigen::internal::swap_assign_op<double>, 1>' requested here\n",
-      " : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn>\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>>, Eigen::internal::swap_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, 1, -1, false>, Eigen::internal::swap_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: (skipping 3 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:517:42: note: in instantiation of function template specialization 'Eigen::internal::ldlt_inplace<1>::unblocked<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpositions<-1>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success : NumericalIssue;\n",
-      "                                         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:112:7: note: in instantiation of function template specialization 'Eigen::LDLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:664:10: note: in instantiation of function template specialization 'Eigen::LDLT<Eigen::Matrix<double, -1, -1, 0>, 1>::LDLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  return LDLT<PlainObject>(derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/err/check_pos_definite.hpp:38:59: note: in instantiation of member function 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::ldlt' requested here\n",
-      "  Eigen::LDLT<Eigen::MatrixXd> cholesky = value_of_rec(y).ldlt();\n",
-      "                                                          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/validate_dense_inv_metric.hpp:23:23: note: in instantiation of function template specialization 'stan::math::check_pos_definite<double>' requested here\n",
-      "          stan::math::check_pos_definite(\"check_pos_definite\",\n",
-      "                      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Swap.h:20:11: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::swap_assign_op<double>, 1>' requested here\n",
-      " : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn>\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>>, Eigen::internal::swap_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::internal::swap_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::Block<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, -1, 1, false>, Eigen::internal::swap_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: (skipping 3 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:517:42: note: in instantiation of function template specialization 'Eigen::internal::ldlt_inplace<1>::unblocked<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpositions<-1>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success : NumericalIssue;\n",
-      "                                         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:112:7: note: in instantiation of function template specialization 'Eigen::LDLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:664:10: note: in instantiation of function template specialization 'Eigen::LDLT<Eigen::Matrix<double, -1, -1, 0>, 1>::LDLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  return LDLT<PlainObject>(derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/err/check_pos_definite.hpp:38:59: note: in instantiation of member function 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::ldlt' requested here\n",
-      "  Eigen::LDLT<Eigen::MatrixXd> cholesky = value_of_rec(y).ldlt();\n",
-      "                                                          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/validate_dense_inv_metric.hpp:23:23: note: in instantiation of function template specialization 'stan::math::check_pos_definite<double>' requested here\n",
-      "          stan::math::check_pos_definite(\"check_pos_definite\",\n",
-      "                      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Product<Eigen::DiagonalWrapper<const Eigen::Block<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, -1, 1, false>>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>>, Eigen::internal::evaluator<Eigen::Product<Eigen::DiagonalWrapper<const Eigen::Block<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, -1, 1, false>>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::Product<Eigen::DiagonalWrapper<const Eigen::Block<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, -1, 1, false>>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::Product<Eigen::DiagonalWrapper<const Eigen::Block<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, -1, 1, false>>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, 1, 0>, -1, 1, false>, Eigen::Product<Eigen::DiagonalWrapper<const Eigen::Block<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>, -1, 1, false>>, Eigen::Transpose<const Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 6 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:517:42: note: in instantiation of function template specialization 'Eigen::internal::ldlt_inplace<1>::unblocked<Eigen::Matrix<double, -1, -1, 0>, Eigen::Transpositions<-1>, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "  m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success : NumericalIssue;\n",
-      "                                         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:112:7: note: in instantiation of function template specialization 'Eigen::LDLT<Eigen::Matrix<double, -1, -1, 0>, 1>::compute<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "      compute(matrix.derived());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Cholesky/LDLT.h:664:10: note: in instantiation of function template specialization 'Eigen::LDLT<Eigen::Matrix<double, -1, -1, 0>, 1>::LDLT<Eigen::Matrix<double, -1, -1, 0>>' requested here\n",
-      "  return LDLT<PlainObject>(derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/err/check_pos_definite.hpp:38:59: note: in instantiation of member function 'Eigen::MatrixBase<Eigen::Matrix<double, -1, -1, 0>>::ldlt' requested here\n",
-      "  Eigen::LDLT<Eigen::MatrixXd> cholesky = value_of_rec(y).ldlt();\n",
-      "                                                          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/validate_dense_inv_metric.hpp:23:23: note: in instantiation of function template specialization 'stan::math::check_pos_definite<double>' requested here\n",
-      "          stan::math::check_pos_definite(\"check_pos_definite\",\n",
-      "                      ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/mcmc/covar_adaptation.hpp:27:17: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_identity_op<double>, Eigen::Matrix<double, -1, -1, 0>>>>>' requested here\n",
-      "          covar = (n / (n + 5.0)) * covar\n",
-      "                ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:710:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>>' requested here\n",
-      "      internal::call_assignment(this->derived(), other.derived());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:225:20: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>>' requested here\n",
-      "      return Base::_set(other);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/mcmc/var_adaptation.hpp:27:15: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::operator=<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>>>' requested here\n",
-      "          var = (n / (n + 5.0)) * var\n",
-      "              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_init1<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:189:32: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "        return normal_fullrank(Eigen::VectorXd(mu_.array().square()),\n",
-      "                               ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_init1<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:190:32: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::CwiseUnaryOp<Eigen::internal::scalar_square_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "                               Eigen::MatrixXd(L_chol_.array().square()));\n",
-      "                               ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_init1<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:204:32: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "        return normal_fullrank(Eigen::VectorXd(mu_.array().sqrt()),\n",
-      "                               ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_set_noalias<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:296:22: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0>>::_init1<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "      Base::template _init1<T>(x);\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:205:32: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, -1, 0, -1, -1>::Matrix<Eigen::CwiseUnaryOp<Eigen::internal::scalar_sqrt_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>>' requested here\n",
-      "                               Eigen::MatrixXd(L_chol_.array().sqrt()));\n",
-      "                               ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ArrayBase.h:220:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:273:21: note: in instantiation of function template specialization 'Eigen::ArrayBase<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>::operator/=<Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "        mu_.array() /= rhs.mu().array();\n",
-      "                    ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::evaluator<Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::evaluator<Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ArrayBase.h:220:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:274:25: note: in instantiation of function template specialization 'Eigen::ArrayBase<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>>::operator/=<Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, -1, 0>>>' requested here\n",
-      "        L_chol_.array() /= rhs.L_chol().array();\n",
-      "                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:29:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::add_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:290:21: note: in instantiation of member function 'Eigen::ArrayBase<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>::operator+=' requested here\n",
-      "        mu_.array() += scalar;\n",
-      "                    ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, -1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, -1, 0>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, -1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, -1, 0>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, -1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:29:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Array<double, -1, -1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::add_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:291:25: note: in instantiation of member function 'Eigen::ArrayBase<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, -1, 0>>>::operator+=' requested here\n",
-      "        L_chol_.array() += scalar;\n",
-      "                        ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::mul_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::mul_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:21:13: note: in instantiation of function template specialization 'Eigen::internal::call_assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::mul_assign_op<double>>' requested here\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:309:17: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Matrix<double, -1, -1, 0>>::operator*=' requested here\n",
-      "        L_chol_ *= scalar;\n",
-      "                ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::PlainObjectBase<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/families/normal_fullrank.hpp:363:16: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>, const Eigen::Matrix<double, -1, 1, 0>>>' requested here\n",
-      "        return (L_chol_ * eta) + mu_;\n",
-      "               ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::PlainObjectBase<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/families/normal_meanfield.hpp:324:16: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>' requested here\n",
-      "        return eta.array().cwiseProduct(omega_.array().exp()) + mu_.array();\n",
-      "               ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::_set_noalias<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>' requested here\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/Matrix.h:379:9: note: in instantiation of function template specialization 'Eigen::PlainObjectBase<Eigen::Matrix<double, -1, 1, 0>>::PlainObjectBase<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>' requested here\n",
-      "      : Base(other.derived())\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/fun/subtract.hpp:43:10: note: in instantiation of function template specialization 'Eigen::Matrix<double, -1, 1, 0, -1, 1>::Matrix<Eigen::CwiseBinaryOp<Eigen::internal::scalar_difference_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>' requested here\n",
-      "  return m.array() - c;\n",
-      "         ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4.hpp:266:47: note: in instantiation of function template specialization 'stan::math::subtract<double, double, -1, 1>' requested here\n",
-      "            stan::math::assign(RESPONSE_TRAN, subtract(RESPONSE, MEAN_Y));\n",
-      "                                              ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:97:11: note: in instantiation of function template specialization 'stan::io::random_var_context::random_var_context<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          random_context(model, rng, init_radius, is_initialized_with_zero);\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:97:11: note: in instantiation of function template specialization 'stan::io::random_var_context::random_var_context<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          random_context(model, rng, init_radius, is_initialized_with_zero);\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<double, 1, -1, 1>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<double, 1, -1, 1>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, 1, -1, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, 1, -1, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, 1, -1, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:97:11: note: in instantiation of function template specialization 'stan::io::random_var_context::random_var_context<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          random_context(model, rng, init_radius, is_initialized_with_zero);\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, 1, -1, 1>, Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, 1, -1, 1>, Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, 1, -1, 1>, Eigen::Block<const Eigen::Matrix<double, -1, -1, 0>, 1, -1, false>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:97:11: note: in instantiation of function template specialization 'stan::io::random_var_context::random_var_context<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          random_context(model, rng, init_radius, is_initialized_with_zero);\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:97:11: note: in instantiation of function template specialization 'stan::io::random_var_context::random_var_context<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          random_context(model, rng, init_radius, is_initialized_with_zero);\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Array<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:97:11: note: in instantiation of function template specialization 'stan::io::random_var_context::random_var_context<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          random_context(model, rng, init_radius, is_initialized_with_zero);\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<stan::math::var, -1, 1, 0>, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<stan::math::var, -1, 1, 0>, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::Map<Eigen::Matrix<stan::math::var, -1, 1, 0>, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::Map<Eigen::Matrix<stan::math::var, -1, 1, 0>, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::Map<Eigen::Matrix<stan::math::var, -1, 1, 0>, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<stan::math::var, -1, -1, 0>, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<stan::math::var, -1, -1, 0>, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Map<Eigen::Matrix<stan::math::var, -1, -1, 0>, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Map<Eigen::Matrix<stan::math::var, -1, -1, 0>, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::Map<Eigen::Matrix<stan::math::var, -1, -1, 0>, 0>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, 1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, 1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, -1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, -1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<stan::math::var>, Eigen::Matrix<stan::math::var, -1, -1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:391:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:431:20: note: (skipping 10 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      lazyproduct::evalTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Transpose<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Transpose<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::Matrix<double, -1, -1, 0>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Transpose<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Transpose<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:391:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::Transpose<Eigen::Map<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::Matrix<double, -1, -1, 0>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:431:20: note: (skipping 15 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      lazyproduct::evalTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Block<const Eigen::Matrix<stan::math::var, -1, -1, 0>, 1, -1, false>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::Block<const Eigen::Matrix<stan::math::var, -1, -1, 0>, 1, -1, false>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::Block<const Eigen::Matrix<stan::math::var, -1, -1, 0>, 1, -1, false>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::Block<const Eigen::Matrix<stan::math::var, -1, -1, 0>, 1, -1, false>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::Block<const Eigen::Matrix<stan::math::var, -1, -1, 0>, 1, -1, false>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, stan::math::var>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<stan::math::var, 1, -1, 1>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, stan::math::var>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<stan::math::var, 1, -1, 1>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, stan::math::var>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, stan::math::var>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, 1, -1, 1>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, stan::math::var>, const Eigen::Matrix<double, 1, -1, 1>, const Eigen::Matrix<stan::math::var, 1, -1, 1>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<stan::math::var>, const Eigen::Matrix<stan::math::var, -1, 1, 0>, const Eigen::Matrix<stan::math::var, -1, 1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<stan::math::var>, const Eigen::Matrix<stan::math::var, -1, 1, 0>, const Eigen::Matrix<stan::math::var, -1, 1, 0>>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<stan::math::var>, const Eigen::Matrix<stan::math::var, -1, 1, 0>, const Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<stan::math::var>, const Eigen::Matrix<stan::math::var, -1, 1, 0>, const Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<stan::math::var, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<stan::math::var>, const Eigen::Matrix<stan::math::var, -1, 1, 0>, const Eigen::Matrix<stan::math::var, -1, 1, 0>>, Eigen::internal::assign_op<stan::math::var, stan::math::var>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/util/initialize.hpp:166:31: note: in instantiation of function template specialization 'stan::model::log_prob_grad<true, true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "      log_prob = stan::model::log_prob_grad<true, Jacobian>\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/diagnose/diagnose.hpp:54:19: note: in instantiation of function template specialization 'stan::services::util::initialize<true, anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "          = util::initialize(model, init, rng, init_radius,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:864:49: note: in instantiation of function template specialization 'stan::services::diagnose::diagnose<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "        return_code = stan::services::diagnose::diagnose(model,\n",
-      "                                                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:409:9: note: in instantiation of member function 'stan::optimization::BFGSLineSearch<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::initialize' requested here\n",
-      "        initialize(params_r);\n",
-      "        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:74:19: note: in instantiation of member function 'stan::optimization::BFGSLineSearch<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::BFGSLineSearch' requested here\n",
-      "        Optimizer bfgs(model, cont_vector, disc_vector, &bfgs_ss);\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/NoAlias.h:42:7: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_sum_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs_linesearch.hpp:253:22: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "        x1.noalias() = x0 + alpha1 * p;\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:205:21: note: in instantiation of function template specialization 'stan::optimization::WolfeLineSearch<stan::optimization::ModelAdaptor<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>, double, Eigen::Matrix<double, -1, 1, 0>>' requested here\n",
-      "          retCode = WolfeLineSearch(_func, _alpha, _xk_1, _fk_1, _gk_1,\n",
-      "                    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: (skipping 10 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::sub_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:164:3: note: (skipping 10 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:391:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:431:20: note: (skipping 5 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      lazyproduct::evalTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, -1, 0>>, const Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:812:13: note: (skipping 13 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      this->_set_noalias(other);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::Product<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 1>, Eigen::Product<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 1>, Eigen::Product<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ProductEvaluators.h:391:5: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 1>, Eigen::Product<Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 0>, 0>, Eigen::Transpose<Eigen::Matrix<double, -1, -1, 0>>, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/products/GeneralMatrixMatrix.h:431:20: note: (skipping 11 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      lazyproduct::evalTo(dst, lhs, rhs);\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 1>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 1>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 1>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 1>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 1>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 16 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 1>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:797:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, -1, 1>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, tmp, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 4 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Block<Eigen::Matrix<double, -1, -1, 0>, -1, 1, true>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 8 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:245:31: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::update' requested here\n",
-      "          Scalar B0fact = _qn.update(yk, sk, true);\n",
-      "                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:45:13: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:246:17: note: in instantiation of member function 'Eigen::DenseBase<Eigen::Matrix<double, -1, 1, 0>>::operator/=' requested here\n",
-      "          _pk_1 /= B0fact;\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/NoAlias.h:42:7: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_opposite_op<double>, const Eigen::Product<Eigen::Matrix<double, -1, -1, 0>, Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs_update.hpp:56:22: note: (skipping 1 context in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "        pk.noalias() = -(_Hk*gk);\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:253:13: note: in instantiation of member function 'stan::optimization::BFGSUpdate_HInv<double, -1>::search_direction' requested here\n",
-      "        _qn.search_direction(_pk, _gk);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/optimize/bfgs.hpp:121:22: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>, stan::optimization::BFGSUpdate_HInv<double, -1>, double, -1>::step' requested here\n",
-      "          ret = bfgs.step();\n",
-      "                     ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:900:41: note: in instantiation of function template specialization 'stan::services::optimize::bfgs<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            = stan::services::optimize::bfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::sub_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::sub_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:164:3: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:253:13: note: in instantiation of member function 'stan::optimization::LBFGSUpdate<double, -1>::search_direction' requested here\n",
-      "        _qn.search_direction(_pk, _gk);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/optimize/lbfgs.hpp:123:23: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>, stan::optimization::LBFGSUpdate<double, -1>, double, -1>::step' requested here\n",
-      "          ret = lbfgs.step();\n",
-      "                      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:924:41: note: in instantiation of function template specialization 'stan::services::optimize::lbfgs<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            = stan::services::optimize::lbfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, -1, 1, 0>>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/CwiseBinaryOp.h:177:3: note: (skipping 2 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/optimization/bfgs.hpp:253:13: note: in instantiation of member function 'stan::optimization::LBFGSUpdate<double, -1>::search_direction' requested here\n",
-      "        _qn.search_direction(_pk, _gk);\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/optimize/lbfgs.hpp:123:23: note: in instantiation of member function 'stan::optimization::BFGSMinimizer<stan::optimization::ModelAdaptor<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>, stan::optimization::LBFGSUpdate<double, -1>, double, -1>::step' requested here\n",
-      "          ret = lbfgs.step();\n",
-      "                      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:924:41: note: in instantiation of function template specialization 'stan::services::optimize::lbfgs<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            = stan::services::optimize::lbfgs(model, *init_context_ptr,\n",
-      "                                        ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, 1, -1, 1>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, 1, -1, 1>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, 1, -1, 1>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, 1, -1, 1>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 23 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/mcmc/hmc/nuts/dense_e_nuts.hpp:20:11: note: in instantiation of member function 'stan::mcmc::base_nuts<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, dense_e_metric, expl_leapfrog, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::base_nuts' requested here\n",
-      "        : base_nuts<Model, dense_e_metric, expl_leapfrog,\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/sample/hmc_nuts_dense_e.hpp:79:11: note: in instantiation of member function 'stan::mcmc::dense_e_nuts<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::dense_e_nuts' requested here\n",
-      "          sampler(model, rng);\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1008:19: note: in instantiation of function template specialization 'stan::services::sample::hmc_nuts_dense_e<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "                ::hmc_nuts_dense_e(model, *init_context_ptr, *metric_context_ptr,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Transpose<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>>, Eigen::internal::evaluator<Eigen::Transpose<const Eigen::Transpose<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Transpose<const Eigen::Transpose<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Transpose<const Eigen::Transpose<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 0>, Eigen::Transpose<const Eigen::Transpose<const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 25 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/mcmc/hmc/nuts/dense_e_nuts.hpp:20:11: note: in instantiation of member function 'stan::mcmc::base_nuts<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, dense_e_metric, expl_leapfrog, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::base_nuts' requested here\n",
-      "        : base_nuts<Model, dense_e_metric, expl_leapfrog,\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/sample/hmc_nuts_dense_e.hpp:79:11: note: in instantiation of member function 'stan::mcmc::dense_e_nuts<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::dense_e_nuts' requested here\n",
-      "          sampler(model, rng);\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1008:19: note: in instantiation of function template specialization 'stan::services::sample::hmc_nuts_dense_e<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "                ::hmc_nuts_dense_e(model, *init_context_ptr, *metric_context_ptr,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>>, Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>, Eigen::Matrix<double, -1, 1, 0>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 15 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/mcmc/hmc/nuts/dense_e_nuts.hpp:20:11: note: in instantiation of member function 'stan::mcmc::base_nuts<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, dense_e_metric, expl_leapfrog, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::base_nuts' requested here\n",
-      "        : base_nuts<Model, dense_e_metric, expl_leapfrog,\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/sample/hmc_nuts_dense_e.hpp:79:11: note: in instantiation of member function 'stan::mcmc::dense_e_nuts<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::dense_e_nuts' requested here\n",
-      "          sampler(model, rng);\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1008:19: note: in instantiation of function template specialization 'stan::services::sample::hmc_nuts_dense_e<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "                ::hmc_nuts_dense_e(model, *init_context_ptr, *metric_context_ptr,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::Map<Eigen::Matrix<double, -1, 1, 0>, 16>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 15 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/mcmc/hmc/nuts/dense_e_nuts.hpp:20:11: note: in instantiation of member function 'stan::mcmc::base_nuts<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, dense_e_metric, expl_leapfrog, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::base_nuts' requested here\n",
-      "        : base_nuts<Model, dense_e_metric, expl_leapfrog,\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/sample/hmc_nuts_dense_e.hpp:79:11: note: in instantiation of member function 'stan::mcmc::dense_e_nuts<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::dense_e_nuts' requested here\n",
-      "          sampler(model, rng);\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1008:19: note: in instantiation of function template specialization 'stan::services::sample::hmc_nuts_dense_e<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "                ::hmc_nuts_dense_e(model, *init_context_ptr, *metric_context_ptr,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:728:17: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, 1, 0>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::Matrix<double, -1, 1, 0>, const Eigen::Matrix<double, -1, 1, 0>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/PlainObjectBase.h:537:7: note: (skipping 4 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "      _set_noalias(other);\n",
-      "      ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/mcmc/hmc/nuts/diag_e_nuts.hpp:20:11: note: in instantiation of member function 'stan::mcmc::base_nuts<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, diag_e_metric, expl_leapfrog, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::base_nuts' requested here\n",
-      "        : base_nuts<Model, diag_e_metric, expl_leapfrog,\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/sample/hmc_nuts_diag_e.hpp:79:11: note: in instantiation of member function 'stan::mcmc::diag_e_nuts<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::diag_e_nuts' requested here\n",
-      "          sampler(model, rng);\n",
-      "          ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1062:19: note: in instantiation of function template specialization 'stan::services::sample::hmc_nuts_diag_e<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "                ::hmc_nuts_diag_e(model, *init_context_ptr, *metric_context_ptr,\n",
-      "                  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::evaluator<Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>>, Eigen::internal::div_assign_op<double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::div_assign_op<double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::Matrix<double, -1, -1, 0>, Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, Eigen::Matrix<double, -1, -1, 0>>, Eigen::internal::div_assign_op<double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/SelfCwiseBinaryOp.h:45:13: note: (skipping 4 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());\n",
-      "            ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/advi.hpp:497:17: note: in instantiation of member function 'stan::variational::advi<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, stan::variational::normal_fullrank, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::adapt_eta' requested here\n",
-      "          eta = adapt_eta(variational, adapt_iterations, logger);\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/experimental/advi/fullrank.hpp:87:20: note: in instantiation of member function 'stan::variational::advi<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, stan::variational::normal_fullrank, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::run' requested here\n",
-      "          cmd_advi.run(eta, adapt_engaged, adapt_iterations,\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1378:15: note: in instantiation of function template specialization 'stan::services::experimental::advi::fullrank<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            ::fullrank(model, *init_context_ptr,\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_op<double>, const Eigen::ArrayWrapper<const Eigen::Diagonal<const Eigen::Matrix<double, -1, -1, 0>, 0>>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>>, Eigen::internal::evaluator<Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_op<double>, const Eigen::ArrayWrapper<const Eigen::Diagonal<const Eigen::Matrix<double, -1, -1, 0>, 0>>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::ArrayWrapper<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_op<double>, const Eigen::ArrayWrapper<const Eigen::Diagonal<const Eigen::Matrix<double, -1, -1, 0>, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::ArrayWrapper<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_op<double>, const Eigen::ArrayWrapper<const Eigen::Diagonal<const Eigen::Matrix<double, -1, -1, 0>, 0>>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::ArrayWrapper<Eigen::Diagonal<Eigen::Matrix<double, -1, -1, 0>, 0>>, Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_op<double>, const Eigen::ArrayWrapper<const Eigen::Diagonal<const Eigen::Matrix<double, -1, -1, 0>, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ArrayBase.h:194:3: note: (skipping 4 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/advi.hpp:497:17: note: in instantiation of member function 'stan::variational::advi<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, stan::variational::normal_fullrank, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::adapt_eta' requested here\n",
-      "          eta = adapt_eta(variational, adapt_iterations, logger);\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/experimental/advi/fullrank.hpp:87:20: note: in instantiation of member function 'stan::variational::advi<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, stan::variational::normal_fullrank, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::run' requested here\n",
-      "          cmd_advi.run(eta, adapt_engaged, adapt_iterations,\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1378:15: note: in instantiation of function template specialization 'stan::services::experimental::advi::fullrank<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            ::fullrank(model, *init_context_ptr,\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::add_assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::add_assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/ArrayBase.h:194:3: note: (skipping 4 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/advi.hpp:497:17: note: in instantiation of member function 'stan::variational::advi<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, stan::variational::normal_meanfield, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::adapt_eta' requested here\n",
-      "          eta = adapt_eta(variational, adapt_iterations, logger);\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/experimental/advi/meanfield.hpp:87:20: note: in instantiation of member function 'stan::variational::advi<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, stan::variational::normal_meanfield, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::run' requested here\n",
-      "          cmd_advi.run(eta, adapt_engaged, adapt_iterations,\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1388:15: note: in instantiation of function template specialization 'stan::services::experimental::advi::meanfield<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            ::meanfield(model, *init_context_ptr,\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:9:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/meta/as_array_or_scalar.hpp:4:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Dense:1:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/Core:420:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:86:63: warning: converting the enum constant to a boolean [-Wint-in-bool-context]\n",
-      "    MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess\n",
-      "                                                              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:607:20: note: in instantiation of template class 'Eigen::internal::copy_using_evaluator_traits<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  typedef typename AssignmentTraits::PacketType PacketType;\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:739:10: note: in instantiation of template class 'Eigen::internal::generic_dense_assignment_kernel<Eigen::internal::evaluator<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>>, Eigen::internal::evaluator<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>>, Eigen::internal::assign_op<double, double>, 0>' requested here\n",
-      "  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());\n",
-      "         ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:879:5: note: in instantiation of function template specialization 'Eigen::internal::call_dense_assignment_loop<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "    call_dense_assignment_loop(dst, src, func);\n",
-      "    ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:836:46: note: in instantiation of member function 'Eigen::internal::Assignment<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>, Eigen::internal::Dense2Dense, void>::run' requested here\n",
-      "  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);\n",
-      "                                             ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:804:3: note: in instantiation of function template specialization 'Eigen::internal::call_assignment_no_alias<Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double>, const Eigen::ArrayWrapper<Eigen::Matrix<double, -1, 1, 0>>, const Eigen::CwiseUnaryOp<Eigen::internal::scalar_exp_op<double>, const Eigen::ArrayWrapper<const Eigen::Matrix<double, -1, 1, 0>>>>, Eigen::internal::assign_op<double, double>>' requested here\n",
-      "  call_assignment_no_alias(dst, src, func);\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/lib/eigen_3.3.3/Eigen/src/Core/AssignEvaluator.h:782:3: note: (skipping 6 contexts in backtrace; use -ftemplate-backtrace-limit=0 to see all)\n",
-      "  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());\n",
-      "  ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/variational/advi.hpp:497:17: note: in instantiation of member function 'stan::variational::advi<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, stan::variational::normal_meanfield, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::adapt_eta' requested here\n",
-      "          eta = adapt_eta(variational, adapt_iterations, logger);\n",
-      "                ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/services/experimental/advi/meanfield.hpp:87:20: note: in instantiation of member function 'stan::variational::advi<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, stan::variational::normal_meanfield, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::run' requested here\n",
-      "          cmd_advi.run(eta, adapt_engaged, adapt_iterations,\n",
-      "                   ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1388:15: note: in instantiation of function template specialization 'stan::services::experimental::advi::meanfield<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4>' requested here\n",
-      "            ::meanfield(model, *init_context_ptr,\n",
-      "              ^\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan_fit.hpp:1649:15: note: in instantiation of function template specialization 'pystan::(anonymous namespace)::command<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>' requested here\n",
-      "        ret = command(args, model_, holder, names_oi_tidx_,\n",
-      "              ^\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:9509:34: note: in instantiation of member function 'pystan::stan_fit<anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_namespace::anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4, boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399>>>::call_sampler' requested here\n",
-      "    __pyx_t_15 = __pyx_v_fitptr->call_sampler((*__pyx_v_argsptr), (*__pyx_v_holderptr));\n",
-      "                                 ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:710:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/py_var_context.hpp:12:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/src/stan/io/dump.hpp:6:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat.hpp:336:\n",
-      "In file included from /Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/prob/poisson_log_glm_log.hpp:5:\n",
-      "/Users/ariel.jiang/Documents/python_virtual_envs/orbit/env_orbit/lib/python3.9/site-packages/pystan/stan/lib/stan_math/stan/math/prim/mat/prob/poisson_log_glm_lpmf.hpp:64:59: warning: unused typedef 'T_alpha_val' [-Wunused-local-typedef]\n",
-      "      typename partials_return_type<T_alpha>::type>::type T_alpha_val;\n",
-      "                                                          ^\n",
-      "In file included from /var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/stanfit4anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4_5767594763401460140.cpp:713:\n",
-      "/var/folders/ln/k7ffpq9d62z_r4bhk5vyj6z40000gn/T/pystan_75g95vtl/anon_model_5f019f85b312f9bb6f21f9ec9a33c1c4.hpp:296:24: warning: unused typedef 'local_scalar_t__' [-Wunused-local-typedef]\n",
-      "        typedef double local_scalar_t__;\n",
-      "                       ^\n",
-      "165 warnings generated.\n",
-      "INFO:root:Guessed max_plate_nesting = 1\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial log joint probability = -1746.54\n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      19      -510.885     0.0488094       32.4314      0.2891      0.2891       23   \n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      39      -508.614   0.000254171       33.3664      0.2697      0.8316       50   \n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      59      -508.605   5.50184e-07       32.6815      0.3379           1       78   \n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      70      -508.605   3.22758e-08       33.2243      0.1599      0.1599       94   \n",
-      "Optimization terminated normally: \n",
-      "  Convergence detected: relative gradient magnitude is below tolerance\n",
-      "step    0 loss = 1530.3, scale = 0.10223\n",
-      "step   50 loss = 353.14, scale = 0.053381\n",
-      "step  100 loss = 341.11, scale = 0.054994\n",
-      "step  150 loss = 322.2, scale = 0.052265\n",
-      "step  200 loss = 311.67, scale = 0.052946\n",
-      "step  250 loss = 306.05, scale = 0.05286\n",
-      "step  300 loss = 303.59, scale = 0.052459\n",
-      "step  350 loss = 301.37, scale = 0.051735\n",
-      "step  400 loss = 300.76, scale = 0.052673\n",
-      "step  450 loss = 300.87, scale = 0.052548\n",
-      "step  500 loss = 300.74, scale = 0.052597\n"
+      "2024-01-21 17:24:16 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 17:24:16 - orbit - INFO - Using SVI (Pyro) with steps: 501, samples: 1000, learning rate: 0.2, learning_rate_total_decay: 0.05 and particles: 100.\n",
+      "/Users/towinazure/opt/miniconda3/envs/orbit39/lib/python3.9/site-packages/torch/__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/tensor/python_tensor.cpp:453.)\n",
+      "  _C._set_default_tensor_type(t)\n",
+      "2024-01-21 17:24:16 - orbit - INFO - step    0 loss = 1529.1, scale = 0.10131\n",
+      "INFO:orbit:step    0 loss = 1529.1, scale = 0.10131\n",
+      "2024-01-21 17:24:16 - orbit - INFO - step   50 loss = 384.48, scale = 0.052799\n",
+      "INFO:orbit:step   50 loss = 384.48, scale = 0.052799\n",
+      "2024-01-21 17:24:17 - orbit - INFO - step  100 loss = 332.66, scale = 0.056398\n",
+      "INFO:orbit:step  100 loss = 332.66, scale = 0.056398\n",
+      "2024-01-21 17:24:17 - orbit - INFO - step  150 loss = 325.49, scale = 0.051683\n",
+      "INFO:orbit:step  150 loss = 325.49, scale = 0.051683\n",
+      "2024-01-21 17:24:18 - orbit - INFO - step  200 loss = 317.82, scale = 0.053095\n",
+      "INFO:orbit:step  200 loss = 317.82, scale = 0.053095\n",
+      "2024-01-21 17:24:18 - orbit - INFO - step  250 loss = 306.25, scale = 0.052981\n",
+      "INFO:orbit:step  250 loss = 306.25, scale = 0.052981\n",
+      "2024-01-21 17:24:18 - orbit - INFO - step  300 loss = 302.64, scale = 0.052313\n",
+      "INFO:orbit:step  300 loss = 302.64, scale = 0.052313\n",
+      "2024-01-21 17:24:19 - orbit - INFO - step  350 loss = 302.83, scale = 0.051899\n",
+      "INFO:orbit:step  350 loss = 302.83, scale = 0.051899\n",
+      "2024-01-21 17:24:19 - orbit - INFO - step  400 loss = 300.67, scale = 0.052531\n",
+      "INFO:orbit:step  400 loss = 300.67, scale = 0.052531\n",
+      "2024-01-21 17:24:19 - orbit - INFO - step  450 loss = 300.73, scale = 0.052478\n",
+      "INFO:orbit:step  450 loss = 300.73, scale = 0.052478\n",
+      "2024-01-21 17:24:20 - orbit - INFO - step  500 loss = 301.12, scale = 0.052574\n",
+      "INFO:orbit:step  500 loss = 301.12, scale = 0.052574\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x14d0ae4c0>"
+       "<orbit.forecaster.svi.SVIForecaster at 0x29ead5490>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7392,7 +953,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "id": "547805d9-9a53-4291-becc-60dd0880b24b",
    "metadata": {
     "ExecuteTime": {
@@ -7407,7 +968,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 20,
    "id": "1fac8353-3d16-4148-b75d-54df047d68fa",
    "metadata": {},
    "outputs": [],
@@ -7417,7 +978,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 21,
    "id": "a4a0da1f-500f-43ed-a5a3-f9ecab96bdc9",
    "metadata": {},
    "outputs": [
@@ -7427,7 +988,7 @@
        "'#276EF1'"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7438,7 +999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 22,
    "id": "32a2ebae-23e0-48f3-94e1-5b58120e543c",
    "metadata": {
     "ExecuteTime": {
@@ -7449,7 +1010,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -7470,7 +1031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 23,
    "id": "b71dd77e-49a4-4cca-9720-6eed9e4f1ea0",
    "metadata": {
     "ExecuteTime": {
@@ -7481,7 +1042,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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oSGW9JCnY7qM5E2I0bWy0EgYEeTwWANKlY5x6f3uB3tmar+lJ0TSzBgAAgClIRgAAAHTCN6simt02uUocyj0aqqLyIBmGRVaroQhHjQaEVmtAaLVCg2vV1v3/BqtDn+emacbItntHWCwW+fva5O9rU0SQn4ZE/vfGfUlJsCIiIiRJ9U3NcpXXKa+sVvlldcorrVVuaY0OldRoV0HFcWParBbF/ad6Ii4sQAMddg102DUg2K4BwX4Ktvt0eumnqvomFVbUqbCiTgVlddqa59Kq3TWqqbtcOdmNinMcUnx4sOZMiNFZQyOUFBtKFQTQw6xWi34wZbgeeitDf/00S4/OSTI7JAAAAPRDJCMAAAA6IS07TTmlJbLUnq0vS2LU7LbJYjEU7ijUwIgjCg8pkdXqliTVSzpyYuGCpGOVD3mVeUrLTtO88fO6HI/dx6YhkUHHJStalNc2Kre05j8JimOvvLIafZlV3OYSUFaL5PD3VbDdRw5/H/n5WGWzWmS1WGSzWtTsNlTb0KzaxubWio2ahubjxiipLVaTUacRsb6qaMrQ7Ak23XXe93p1fwugPxgbG6IpowZo/d4iZeSWaXx8mNkhAQAAoJ8hGQEAANBBh0tq9M7mRh3Jv0qGYVWgf7ViBrg0ILxIvj5N/znKIqkDTZgtUkJYggqrCz0Wb2iAr0IDQpUUG3rc9sZmtwor6lRU1aCjlfWtr8q6RlXVN6myrkmuijo1NRtqdhtqNo79r81qUaDfsWqNILtNUSF2OR3+igqxKzrEXxZbpV7Z8U/5+UrRwdFyVdVoW9EauaouarP6A/3XunXr9MQTT2jr1q2qra3VuHHjdM899+i73/1ul8YzDEPTpk1TZmamcnNze+y83ub75w3V5/uL9fKGHD19bShJQgAAAPQokhEAAACnUFxVr1e+yFHaniMyjGGamRiqq1JjdeaQ8NO+mWdRz98M9LVZFR8eqPjwwG4d97WMT1TWcETjwo710nAGOZVRmHHa1R/oW5YuXaqbbrpJPj4+uvjii2Wz2bR69Wpdd9112rlzpxYuXNjpMe+//36tXr1acXFxPXpeb+MM8dfM5GitzCjQxqwSnTc80uyQAAAA0I+QjAAAAGhHXWOzlm/J078256q+ya2UQWGad85gjYkJMTu0XuebvTRaEjQWi0XOYKfSDqZpakLbvTHQvxQWFuqOO+5QUFCQ1q1bp9TUVElSZmamLrroIj3++OO68sorW7efSk1Nje688069/PLLPXpeb/a9swZp9e5CvbrxoM5JiJC1B5raAwAAAJJE90AAAIA2bDhQpB//c7OWfnlIA4LtemTOWD12ZRKJiHakZaepuLZYziDncdudQU4V1RQpLTvNpMjQm7zwwguqra3V3XfffdyN/8TERC1atEiGYWjJkiUdGuudd95RSkqKXn75ZQ0bNqzHzuvtwgL9dEVKnA6X1GpN5hGzwwEAAEA/QjICAADgGyrqGvU/H2Rq0fuZqm1s1h1Thun5eWforKERrK/ejraqIlp8szqioLLApAjRW6xcuVKSNHfu3BP2zZ07VxaLRe+9994pxykrK9PcuXOVlZWln/3sZ6d8T3edt6+4+ow4Bdt99NpXh9TQ5DY7HAAAAPQTJCMAAAD+Y8OBIt21NF2f7ivSOQkR+uMNE3XFhFj52PiV6WTaq4poQXUEpGNNpnft2iVJSk5OPmF/eHi4oqOjVVpaqry8vJOOZbVaNW/ePGVkZGjJkiUKCAjokfP2FUF2H333rHgdrazXqh0kCQEAANAz+GYNAAD6vdqGZi3+aI8WvZ+ppmZD904fpQWXj1F4kJ/ZofV6J6uKaEF1BCSptLRUdXV1cjgcCgoKavOYmJhjfUUKCwtPOlZISIiWLl2qsWPH9uh5+5JZ42IUGeynZV8fVm1Ds9nhAAAAoB8gGQEAAPq1g0XV+vkbW5W256jOGhqhF25I1dTR7d9Yx/FOVRXRguoIVFdXS5ICAwPbPaalwqGqqqpXnbe+vl4VFRXHverr67stRjPYfWy6/uzBqqht0nsZ+WaHAwAAgH7Ax+wAAAAAzGAYhj7ZfUQvrjugJreh2y9I0JUpsSQhOqEjVREtvlkdMTVhqmIcMT0UJXoLm80mSR36O+Z2d18fg+4476JFi7Rw4cLjtj300EN68MEHTz/ALigtLe2Wcc5w+irUbtG/NuXogsEBsvvwrFp36q55gmcxT96BefIOzJN3YJ68gxnzFBER4fFzkIwAAAD9TkOTW39Yu1+rdx/RgGA/PTgzUWNiQswOy+u0VEWMixrXoeOdQU5lFGYoLTtN88bP83B06G2Cg4MlSbW1te0e07Kv5dject758+fr3nvvPW6b3W6X3W7vpig7r7u+LF5/7jC9uO6Avsqv15Upcd0yJv6rJ77U4/QxT96BefIOzJN3YJ68Q1+cJx59AQAA/UpZTYMWvL1dq3cf0cQh4frf688gEdEFnamKaEHviP7N4XDI4XCovLy83cRAQcGxz0VLD4fecl673a6QkJDjXmYmIrrTtLFOhQX66t/peWpo6r6KFAAAAODbSEYAAIB+41Bxje5/c5syXZW6OjVOj8weqxB/X7PD8kod7RXxbfSO6L8sFouSkpIkSbt37z5hf0lJiVwul8LDwxUX131P6Jt1Xm/h52PV1alxKqlu0JrM/tPAGwAAAD2PZAQAAOgXNueU6v5/bdPRqgb99JKRuvX8BFmt9Ifoiq5URbSgOqJ/u+yyyyRJy5cvP2Hf8uXLZRiGZs2a1WfO6y1mJsUo2O6jf23OVVMz1REAAADwDJIRAACgz1uZUaDHVuyUzWLRb65M1rSxnXuaH8fralVEC6oj+q/bbrtNgYGBWrx4sTZs2NC6fc+ePVqwYIEk6YEHHmjdXlBQoMzMzNZllHrqvP1NgJ9Nc8+IVWFFvdbvO2p2OAAAAOijSEYAAIA+q9lt6M/rD+jFdQfkDPHX09+doHHxoWaH5dVOpyqiBdUR/Vd8fLyee+451dTUaMqUKZo2bZpmz56tlJQUuVwuLVq0SBMmTGg9fv78+RozZozmz5/fo+ftjy4fH6sAP5ve/DpXbrdhdjgAAADog3zMDgAAAMATahqa9D8f7NHmnFIlx4Vo/qwx9IfoBmnZacqrzNOgkEFyVbm6PI5hGMqrzFNadprmjZ/XjRGit7v99tsVHx+vJ598Uhs3bpTNZlNqaqruu+8+XX311X3uvN4i2O6jOeNjtOzrXG04UKwLRg4wOyQAAAD0MSQjAABAn3Okok4L39ulQ8U1unSMU3dOHS5fGwWh3aGwulAJYQmnP5BFSghLUGE1DXP7oxkzZmjGjBmnPO6ll17SSy+9dMrjhg4dKsM49dP8HT1vf3XFhDgt35qvf6fn6vwRkV2ufgIAAADaQjICAAD0KXtclfrNyl0qq2nUzZOG6jupcdxQ60b3nHuPDHXfEi4WMTdAbxEa6KuLE6P0wQ6XduZXKDmOZe0AAADQfXhEEAAA9Bnr9x7V/H9nqKahWfMvS9Q1E+NJRHQzi8Uiq8XabS/mB+hd5p4RJ4tFentLntmhAAAAoI/psWTEunXrNH36dEVFRcnhcGjSpElatmxZp8Y4cOCAbr75ZsXExMhut2vIkCH60Y9+pMOHD3soagAA4A0Mw9D/fXVIv/twj4L9ffXk1eM0aQTrnQNAZ8WFBejsoRH6KrtEuaU1ZocDAACAPqRHkhFLly7V1KlTtXbtWqWmpmrKlClKT0/Xddddp0cffbRDY+zbt09nnXWWXnnlFYWFhenyyy9XcHCw/vSnP2ncuHHKyMjw8FUAAIDeqKHJrWc/3qulXx5SwoAgLf7uBI10OswOCwC81lWpcZKkd7bmmxwJAAAA+hKPJyMKCwt1xx13KCgoSBs3btQHH3yglStXauvWrXI6nXr88ceVnp5+ynFuvPFGlZaWauHChdq1a5f+/e9/a+fOnVq4cKHKy8t1xx13ePpSAABAL1Ne06hfLt+utD1HdXZChJ76zngNCLabHRYAeLWxMSEa6QzW6t2FKqtpMDscAAAA9BEeT0a88MILqq2t1d13363U1NTW7YmJiVq0aJEMw9CSJUtOOsa+ffv01VdfaejQofrVr3513NrCv/zlLxUcHKyvvvpKJSUlnroMAADQyxwsqtZ9b27V7oJKzT0jTgtmjVGAn83ssADA61ksFl19Rrwamw29v91ldjgAAADoIzyejFi5cqUkae7cuSfsmzt3riwWi957772TjjFy5EgdOXJEH3300QlNDhsaGtTQcOxpHZuNGxAAAPQHGw4U6YF/bdPRqgbdNXWEbr8gQVYrjZABoLucNzxSzhC7Vm7PV31Ts9nhAAAAoA/waDLCMAzt2rVLkpScnHzC/vDwcEVHR6u0tFR5eXknHWvgwIEaOXLkcdtqamp01113qaGhQXPnzlVoaGj3BQ8AAHodt9vQ618d0qL3M2X3semJucmamRxtdlgA0OfYrBZdkRKnitompWUeMTscAAAA9AEeTUaUlpaqrq5ODodDQUFBbR4TExMj6VhviY565513NHPmTMXFxenvf/+75syZo5deeqk7QgYAAL1UbUOznvowU699o1F1chwPIgCAp0wb41SQ3ablW/LldhtmhwMAAAAv59FkRHV1tSQpMDCw3WMCAgIkSVVVVR0e95NPPtGHH36osrKy1vPs27fvpO+pr69XRUXFca/6+voOnxMAAJgnu6ha97yxRRv2F+v8EQP0P9eMV1SIv9lhAUCfFuBn04ykaOWV1WrL4VKzwwEAAICX8/Hk4C09HL7d56Etbre7w+MuWLBATz/9tPLy8vT888/r2Wef1dSpU7Vp0yYlJia2+Z5FixZp4cKFx2176KGH9OCDD3b4vN2ttJRf6L0B8+QdmCfvwDz1fr1xjtbtK9E/vsyT25CunxitmWMGqKayXDVmB2ai3jhPOJFZ8xQREWHKedE3XT4+Rsu35Gn5lnxNHMJnCwAAAF3n0WREcHCwJKm2trbdY1r2tRzbEdHRx9aGHjZsmBYvXqyamhr96U9/0pNPPtnuck3z58/Xvffee9w2u90uu93e4fN6Al8WvQPz5B2YJ+/APPV+vWWO6hqb9eK6A1q9+4gGOAL00GWJSowOMTusXqO3zBNOjnmCt4ty+GvSiAH6bF+RDhXXaHBk+1XvAAAAwMl4dJkmh8Mhh8Oh8vLydhMSBQUFkv7bO6IrbrrpJklSenp6u8fY7XaFhIQc9zI7EQEAANq2K79CP3l9i1bvPqKJQ8L1v9efQSICAExyZUqsJOndbXkmRwIAAABv5tFkhMViUVJSkiRp9+7dJ+wvKSmRy+VSeHi44uLi2h3n008/1Q9+8AP99a9/bXN/S1KhsbGxG6IGAABmaWhy6x+fZ+vhf2eouKpe/29ygh6ZPVYh/r5mhwYA/VZidIhGOR1ak3lE5bV85wIAAEDXeDQZIUmXXXaZJGn58uUn7Fu+fLkMw9CsWbNOOkZxcbH+8pe/6Omnn26zt8SqVaskSRMnTjz9gAEAgCn2FVbq529s1b/T8zQiKlj/+70zdGVKnKzWU/eeAgB41pUpsWpsNvThDpfZoQAAAMBLeTwZcdtttykwMFCLFy/Whg0bWrfv2bNHCxYskCQ98MADrdsLCgqUmZnZunyTdCyhMWTIEO3Zs0cPP/zwcQmJFStW6IknnpDNZjuhJwQAAOj9Kuoa9ULaft335jblltXqpnOH6HfXTNCgCNYlB4DeYtLwSEUG++m97QVqbD7xATEAAADgVDyejIiPj9dzzz2nmpoaTZkyRdOmTdPs2bOVkpIil8ulRYsWacKECa3Hz58/X2PGjNH8+fNbt9ntdr3++utyOBz63e9+p1GjRunqq69WSkqKrrjiCjU3N+tPf/qTUlNTPX05AACgEwzDaHef223ow50u/ejVzfpgh0tJsSF67nsp+u5Zg2SjGgIAehUfm1Wzx8eqtLpBn+0rMjscAAAAeCGPJyMk6fbbb9eqVas0efJkbdy4UZ999plSU1P11ltv6eGHH+7QGOedd562bt2qW2+9VbW1tVqxYoUKCgp07bXX6osvvtDtt9/u4asAAACd4apyafEXi+WqOn5JD8MwtDmnVPe9uU3Pr9kvm9Wi+6aP0m+vGqchkUEmRQsAOJUZSU7Zfax6Z2veSZPNAAAAQFt8eupEM2bM0IwZM0553EsvvaSXXnqpzX3Dhg3T3//+926ODAAAeMKarDVan7NeMcExmjd+niRpZ365/rkxRzvyKuRjs+jKlFjNO2ewAv167FcSAEAXOfx9dfGYKK3a7tKuggolxYaaHRIAAAC8CN/8AQBAtyuoLFDawTTZrDatyU5TtP1srd9Tr805pbJapOljnbru7EGKcvibHSoAoBPmjI/Vqu0uvbs1n2QEAAAAOoVkBAAA6HZp2WkqqilRuPV8bc700e49OxUZGKkpowZo3jlDFBcWYHaIAIAuGBQRqIlDwrUxq1iFFXVyhpBUBgAAQMf0SM8IAADQf2Tk5+iVjdk6nHO5tu0fJDWHK9CRqcevGqQHZiSSiAAAL3dlSqzchrRiW77ZoQAAAMCLUBkBAABOW11jszZmFevjXYVauy9LpbVxCg/y1dC4Qg12liizeKt2Fg/ShLghZocKADhNKYPCNDgiUB/tKtQN5wxRgJ/N7JAAAADgBUhGAACALqlrbNbmnFJ9tr9Im7JLVN/klttolG9AlsbFH1FijF0Wy7FjncFOpR1M09SEqYpxxJgbOADgtFgsFs2ZEKsX0vbr492FumJCrNkhAQAAwAuQjAAAwIMMw5Cl5Y58H1BUdawJdXpOqTbnlKq+yS1JGhPj0AUjB+po41qtPPCpEqPG6ZuX7QxyKqMwQ2nZaZo3fp5J0QMAusvUxIF65YuDWrEtX7PHxchq7Ts/6wAAAOAZJCMAAPAQV5VLSzOW6obxNyg6ONrscLqkqdmtTFelNh0s0eacUuUU10iSrBYpMTpE548coEnDIzUg2K6CygI9kpamqKCoExIwFouF6ggA6EPsPjZdlhytZV/natPBEp0zLNLskAAAANDLkYwAAMBD1mSt0fqc9YoJjvGaaoD6Jrcycsu0K79CO/MrtMdVqdrGZklSaICvpiZG6cwh4UoZHKYQf9/j3puWnabi2mKNixrX5thURwBA3zJrXIzeSs/T8q35JCMAAABwSiQjAADwgILKAqUdTJPNauvV1QDlNY3aVVChXQUV2plfrr0F5bJYjzUi9bVZNDraoQnxYZo4JFzDBwa3uwxHy/W2VRXRguoIAOhbIoPtmjJygNL2HFXW0SoNGxhsdkgAAADoxUhGAADgAS1VAslRydpeuL1XVAMYhiFXRZ125VccexVUKLe0tnV/sN1HE+IcSk2I0tjYEA0fGCw/H2uHxj5VVUQLqiMAoG+5IiVWaXuO6t1t+brn0lFmhwMAAIBejGQEAADd7JtVAlaL1bRqALfbUFZR9bHKh/8kH0qrG1r3O0Psmjp6oMbGhmhsTKjiwwNUVlaqiIiITp2nI1URLaiOAIC+ZUSUQ2NjQrRu71HdfN5QhQf5mR0SAAAAeimSEQAAdLNvVwn0VDVAXWOz9hVWaWd+uXYVVCiz4L/9HiwWaUhkkCYNj9TYmBCNjQ3RgGB7t5y3o1URLaiOAIC+5cqUWC1alalVO1yad85gs8MBAABAL0UyAgB6gGEYp3xiHH1DW1UCnqoGqGlo0u6CCm3PLdfO/ArtPVIlt9uQdKzfwyinQ0mxxxIPo6NDFGzv/h/7namKaEF1BAD0LecOi5QzxK5VOwp0zcT4Di/xBwAAgP6FZAQAeJiryqWlGUt1w/gbFB0cbXY48LD2qgS6oxqgoq5RO/OONZrekVeu7KJq/Sf3oEA/m1IHhykpNlRJnez3cDo6WxXRguoIAOg7rFaLZo+P1d8+y9a6vUc1bazT7JAAAADQC5GMAAAPW5O1Rutz1ismOIabrn3cyaoEulIN0NjsVmZBpTbnlCj9UJmyi6pb94UE+OjcYZEaGxuicXGhGhoZJKu1Z6tvulIV0YLqCADoW6aNdeq1Lw/p3W35unRM538uAAAAoO8jGQEAHtRys9ZmtXHTtR84VZVAR6oBCivqtDmnVJtzSrU9t7y150NIgI8mjxygcXGhSooN1aCIANNv9HS1KqIF1REA0HcE2X106dgordhWoO155RofH2Z2SAAAAOhlSEYAgAe13KxNjkrW9sLt3HTtwzpSJdBeNcDhkhptOFCkz/cXt1Y/WC1SYnSIUoeEKXVwuIYPDO7xyoeTOZ2qiBZURwBA3zJ7fKzeyyjQO1vzSUYAAADgBCQjAMBDvnmz1mqxctO1j+tolUBLNcC7u9Yq3HqB1u09qkMlNZKkYLuPLk6M0jkJERo/KMwjDae7S1p2mvIq8zQoZJBcVa4uj2MYhvIq80jUAUAfEBsWoLOGRmjTwRLlldUqLizA7JAAAADQi/TeuxwA+hTDMExfUqanffvmNEvS9F0drRJoarbIVRKqItelemFfs2Id+xUZHKCZydE6b3ikxseFysfm+abT3aGwulAJYQmnP5BFSghLUGF14emPBQAw3dyUOH2VXaL3tuXrhxcONzscAAAA9CIkIwB4nKvKpaUZS3XD+BsUHRxtdjg9oq2b0yxJ03edqiqitt5HB10RyikMV2OTTRaLIf+gLE1O9tVDU6/1mgTEN91z7j0yZHTbeBb1r2QlAPRVyXEhShgQpE92F+qGc4f06io/AAAA9Czvu/sBwOusyVqj9TnrtSZrjdmh9JiWm9POIOdx251BThXVFCktO82kyNDdTlYVUVblr81747Q6faT25w2Qn2+zkoa6NP3MvUodlaODNZ/oaI13VgRYLBZZLdZue/W3yikA6KssFouumBCruka3Pt7V9WX8AAAA0PeQjADgUS03am1Wm9IOpqmgssDskDzuZDenv1kd0R/+W/QHbSWeyqr89eXuQfo0Y5jyi0I1ILRaZ485pKkp+zUstkR+vs0kpgD0a+vWrdP06dMVFRUlh8OhSZMmadmyZZ0ao6KiQr/85S+VmJiogIAAxcfH68c//rGOHDnS5vHPPvusLBZLu6+HH364Oy4NkqaMGqiwQF+9t61Aze7uq6IDAACAd6NmFoBHtdyoTY5K1vbC7f2iX8Kpluyhd0Tf8e3EU0W1XXsOR8lV4pAkxUZWaOSgowoJrD/hvSzbBaC/Wrp0qW666Sb5+Pjo4osvls1m0+rVq3Xddddp586dWrhw4SnHqKys1NSpU5Wenq7hw4dr9uzZ2r59u1588UWtWLFCGzduVHx8/HHvSU9PlyRdfvnlCgsLO2HMM844o1uuD5Kfj1WXJcfo9a8O6cusYk0aMcDskAAAANALkIwA4DHfvFFrtVj7xY3XjjQy5iZ039GSeBodnqKMA07lFIZLkmIiKzQq/qhCgk5MQnwTiSkA/U1hYaHuuOMOBQUFad26dUpNTZUkZWZm6qKLLtLjjz+uK6+8snV7ex599FGlp6fr+9//vv72t7/Jx8dHbrdb999/v5599lndddddeuedd457z5YtW2SxWPT666/L4XB47BpxzKxx0Xpz82Et35pHMgIAAACSWKYJgAd9e/ma/rAsTXu9Ir6tP/y36OsKKgu0JjtN7toJSts6UjmF4YoMqdbk8Vk6c3TuKRMREst2Aeh/XnjhBdXW1uruu+8+LuGQmJioRYsWyTAMLVmy5KRjVFRU6M9//rMCAwO1ZMkS+fgce77KarXqd7/7nYYNG6Z3331XBw4caH1PbW2tMjMzNWrUKBIRPSQs0E8XjYrS7oJK7SusNDscAAAA9AIkIwB4RFsVAr3txmtjs1ul1Q1yldfpUHGN9hVWakdeuXbklWtXfoUyXRXaV1ipQ8U1Kq6qV31T80nH60hVRIve9t8CnffmtnXatHuMsg6PUJNRp4RB25Uw5EvV6aBcVa4OvwzDUF5lHokpAP3CypUrJUlz5849Yd/cuXNlsVj03nvvnXSMdevWqbq6WlOmTFF4ePhx+2w2m+bMmXPcuSQpIyNDzc3Nmjhx4mleATpjzoRj1Z/vbss3ORIAAAD0BizTBMAj2uub0FPL0tQ3NSu/rE6u8jodqazT0cp6Ha2sV3F1g8prG1Ve26jahpMnF9ria7MoNMBX0aH+inL4yxnir5hQfw0fGKz1uSfvFfFtLNHjnZrdht7anKuln/nI0uxUnDNPg6MPy2ZzS7J1fkCLlBCWoMLqwm6PFQB6E8MwtGvXLklScnLyCfvDw8MVHR2tgoIC5eXlKS4urs1xdu7c2e4YkjR27FhJ0vbt21u3tfSLCA8P1w9/+EN9/PHHys/P19ChQ3XDDTfogQcekL+/f9cvDm0aNjBY4+JDtX5fkW6eNFQDgu1mhwQAAAATkYwA0O1OViHQ3f0S3G5DuaW12pZdqiO7ynW4pEa5pTU6Ulkvwzj+WKtFCg30U2iAr5whdoX4+8rh7yt/X6vsPjbZfazy9bHKIsltGGp2G3IbUl1js6rqm1Rd36TKuiaV1TToYFGNduRVtI7d2NwoV3WTggKmyVpjUWRItQaEVsvXx91u7PSO8D55ZbVa/NFe7S2s1MRBCfr5pSM1PCq4W8a26OTVNADg7UpLS1VXVyeHw6GgoKA2j4mJiVFBQYEKCwvbTUbk5+e3HtveGNKx/hQtWpIRL7zwggYOHKhJkyYpLi5Omzdv1iOPPKL3339fn3zySbtxoeuunBCr7bnlen97gb5/3lCzwwEAAICJSEYA6HbtVUW0OJ2KgPKaRu0qqNCuggplFlQou6ha9U1uNTU1ycfHR742i2LDAnT+iAGKDw9QzH8qGAY67IoM8pOPrftWp6uqb1JhRZ3ySmu1bNs6FeUcUXNjnLILbMouiJDFYijcUauosCpFR1TKEXhiDwGqI7zHhztd+vP6LDU2u3XVGXG68dwh8vNhtUMA6Kjq6mpJUmBgYLvHBAQESJKqqqq6PE5bY2zZskWSdNttt+kPf/iD7PZjT+hnZ2frqquu0saNG/Xwww/r97//fZtj1tfXq77++J/jdru9dRy076yhEYoN89f72wt0zcR4BfrxFRQAAKC/4jdBAN2qI30TOlMRUNfYrIzccm3OKVVGbplyS2tb9wXZbUqMcWj4wGBF+RuaMCxasaEBslp75gnzYLuPggcGK9C/UvWZH+rMRJucQVWqrvNTUXmQjpQGq6giSCUVgco8FKXQoDrFDyxT3IBy2f2OLRFFdUTvV9/UrBfXZumT3YWKctj182mjlBwXanZYAOB1bLZjS9mdqq+SJLnd7VcWdnScb46xfv16ZWdnKzExsfX9kpSQkKCXXnpJqamp+utf/6rf/e53bS7XtGjRIi1cuPC4bQ899JAefPDBU16LJ5SWlppy3q6aOSpMf96Qqzc37tec5Cizw+kx3jZP/RXz5B2YJ+/APHkH5sk7mDFPERERHj8HyQgA3epUVREtTlYRUFRVr8/3F2nTwRLtzK9QU/Ox9ZYGOuyaOnqgxsaGaGxMqOLD/5t4KCkpUUR4+09aetI3r9likYIDGhQc0KCh0aVyuy0qqQxUXlGI8otCtPNgtHblOBUVVqXhscWKCKmhOqIXK6yo02/f362so9WaOCRc904fpRB/X7PDAgCvFBx8bFm72trado9p2ddybFfGaWuMwMBAJSUltXl8SkqK4uPjdfjwYe3cubPNJtfz58/Xvffee9w2sysjeuLLYneZfWaY3t1Vok/2let7k0bK7tOFHkteypvmqT9jnrwD8+QdmCfvwDx5h744TyQjAHSbjlRFtPh2RYC/NVKfHyjSp3uLtKvgWC8GX5tFE+LDdMbgME0cEq64sIAOPU3Zk051zVaroQGhx/pHJCe4VFjiUO7RUBWWOlRY6lBYcK2GxxYrKojqiN5mc06pnv5wj6rqm3T92YP1vbMG9VjVDQD0RQ6HQw6HQ+Xl5aqtrW1dTumbCgoKJLXfD0JSay8Jl8vV5v6OjPFt0dHROnz4sGpqatrcb3biwdv52qy6OjVef16fpY93FWr2+FizQwIAAIAJSEYA6DYdrYpoERXo1MbsAj347y9UURkltyHZfay6YOQATR4xQKlDwuXv27ufnEvLTlNeZZ4GhQySq6rtmyLfZPUv0OBB0sAofxUcideREqeOZjrl7xci/5BNWpOVphsmUB1htve3F+hP6w4owM+mR+aM1VlD+97TCADQ0ywWi5KSkrRx40bt3r1bqampx+0vKSmRy+VSeHh4u82rJSk5OVmStGvXrjb379y5U5I0btyx30fy8/P1yCOPqL6+Xq+++mqb78nKypIkxcfHd+6i0GHTxjr1xqbDemtzrmYkRcu3G/t4AQAAwDuQjADQLTpTFdHYZFVOYbhyCsNVXjNKLqNGM8b4ac74BJ2dENHrExDfVFhdqISwhE6/L9i/USMHZ2tITK4KiqKVfzRW5UUX6p+fSSkDy5UUS08CM7jdhv6x4aCWb8lTTKi/Hr0iSXFhJz65CwDomssuu0wbN27U8uXLT0hGLF++XIZhaNasWScdY/LkyQoKCtLatWtVXl6u0ND//sxsbm7WihUrZLFYNHPmTElSSEiI/vnPf6q+vl733XefUlJSjhtv5cqVKi4u1qhRo5SQ0Pmf6egYf1+brkiJ1atf5GjtnqOaNtZpdkgAAADoYSQjAHSLjlRFNDRZlZUfqeyCCDU12+Tn26TE+FJVWzfojFFWTRl1dg9G3D3uOfceGTJOe5yK2ka9semw3t/u0sNvbdd5wyN1+wUJcoac2EQTnlHX2KxnP96rDQeKNSbGoQWXj1VoAP0hAKA73XbbbXrqqae0ePFizZw5U5MmTZIk7dmzRwsWLJAkPfDAA63HFxQUtCYcWpZdCgwM1O23367nnntOP/zhD/XKK6/Iz89PhmHowQcfVHZ2tq666iqNGjVK0rHeETfccIP+/ve/64477tD777+vgQMHSpL27dunu+66S5L0yCOP9Nh/h/7q8nExemtzrv61+bAuSYxi+UMAANphGEavW6Ya6A7UxgI4baeqimhotGl3TpRWbx6lfbkDZfdr1oQR+bp04j6NHXpUgyIcSjuYpoLKAhOiPz0Wi0VWi/W0X2GBdv3wwhH6w40TNWl4pL44UKy7lqbrna15crtPP9mBk6usa9Qvl+/QhgPFmjxygH4zdxyJCADwgPj4eD333HOqqanRlClTNG3aNM2ePVspKSlyuVxatGiRJkyY0Hr8/PnzNWbMGM2fP/+4cR5//HElJyfrjTfe0KhRo3Tttddq7NixWrx4sYYOHaoXXnjhuOOffvppjR8/Xl9//bVGjhypyy+/XDNnztS4ceOUk5Ojn//857rhhht65L9BfxZk99Hs8THKL6vT5weKzA4HAIBeyVXl0uIvFndoKWjA25CMAHDaWqoinEHHl9u7DSm7IFxrtozQ/rwBCrA3KnVkrqam7NfgqDLZrMdusjuDnCqqKVJadpoZ4fcqcWEBmj9rjB6fm6ywQF/99dNsPfhWhg4Vt91QE6evrKZB8/+9XXtclfpOapzunz5afj78eAQAT7n99tu1atUqTZ48WRs3btRnn32m1NRUvfXWW3r44Yc7NEZISIg+/fRT3XfffZKkFStWqKGhQXfeeae++OKLE5pXh4eHa8OGDfr1r3+t2NhYrV69Whs3btR5552nf//731q8eHG3XyfadkVKnOw+Vr35da4MgwcuAAD4tjVZa7Q+Z73WZK0xOxSg21kMfgM0TUlJiSIiaIra2zFPJ1dQWaBH0h6RzWpTdHB06/ai8kDtyI5RZY1d/n6NGjOkUHEDKtRelaGryqVmd7Mem/qYYhwxbR90En1xnuoam/XPjTlasS1fFotF884ZrGtS4716SYPeNk9FVfX65ds7lFdWq5snDdU1E2lc2tvmCG1jnrwD8wRP8fbP1l8/zdI7W/M1f1aiJg0fYHY4HuPt89RfME/egXnyDszT6Wu5x1JcW6zIgMgu3yM5GebJO/TVeeLRTwCnJS07TXmVeTIMQ64ql3LLirR+Z6jWb49TWbXkHHhAY0d+Lp+AvSqsdslV1fbLMAzlVeZRHfEN/r42/b/Jw/S7aycoLixAr36Ro1+9s0PFVfVmh9YnuMrr9PBbGcorq9UPLxxGIgIAgB5yzcR42X2seu3LQyxHCQDAN7SsPJEclcwKEuiTSEYAOC2F1YVKCEuQj9VHFZUR2rZnoopLnYoMKdXEMVuUEJsrPx+LbBbbSV8+Vh8lhCWosLrQ7EvqdUY5HVp83QTNTI5WRm65fvp/W7TpYInZYXm1wyU1euitDB2trNdPLxmp2eNjzQ4JAIB+IyzQT5ePj1FOcY02HCg2OxwAAHqFb/bjtFqscgY7vba/JtAeH7MDAODd7jn3HtU1NunlL3L0XlaBhobadMfkBF0ypu1m1qdikfcuQeRJdh+b7po6QmcMCtNza/bpsRW7dGVKrG6ZNFQ+NvLKnZF1tEqPvLNTlfVNum/6aE0ZNdDskAAA6HeuTo3Xqu0uvf7VIU0aHunVy1ACANAdWqoixkWNk3Ssv2ZGYYbSstM0b/w8k6MDugd3sACclvzyOt33ZoZWZriUHBuq5+elanpSjGxWm6wWa6dfXUlg9CeTRgzQc987Q4nRDr2zNV+/emeHymoazA7La+xxVeoXb29XdUOTfnFZIokIAABMEhrgqzkTYnSopEbr9x01OxwAAEz1zaqIlvsiFouF6gj0OSQjAHTZ5pwS3fvGVuWV1ur75w3Rb68aJ2eIv9lh9XlRIf5adPU4zR4fox15Fbrnja3aW1hpdli93o68cv1q+Q41NRt6dE6SzhkWaXZIAAD0a3PPiFOAr02vf3VIzfSOAAD0Yy1VEc4g53HbnUFOekegTyEZAaDTDMPQW5tz9diKXbJaLPr1FUm69sxBlNf3IB+bVT+8cLjunTZKFbWNeuitDH2402V2WL3W5pwSPfLODskiLbwySSmDwswOCQCAfs/h76srz4hVflmd1u09YnY4AACYoq2qiBZUR6CvIRkBoFMamtxa/PFevbThoOLDA/XMdyfojMHhZofVb01NjNL/XDNBEYF+en7Nfr247oCamt1mh9WrbDhQpMff2y27j01PzE1WUmyo2SEBAID/uDIlToF+Nr325WF+hwEA9EvtVUW0oDoCfQnJCAAdVtvQrIUrdmrtnqM6JyFCT187QbFhAWaH1e+NiArW4utSNC4+VCszCvTIuztVXttodli9QtqeI3pqVaYc/j5adPU4jXQ6zA4JAAB8Q7DdR1edEafCijp9srvQ7HAAAOhRJ6uKaEF1BPoSkhEAOqSirlELlm9XRm65Zo2L0S9mjVGAn83ssPAfoQG+euyKJF0+Pkbbc8t137KtyimuNjssU32wo0DPfrxXEUF+evI74zV0QJDZIQEAgDZcmRKnsEBfvfbVYdU1NpsdDgAAPeZUVREtqI5AX0EyAsApFVfVa/5b27WvsErfPTNeP7pwGP0heiEfm1U/unC47po6XEerGvTAmxnamFVsdlimeGdrnl5IOyBniL+e+s54xVHBAwBArxXgZ9N1Zw1SaXWD3t2ab3Y4AAD0iI5URbSgOgJ9BckIACdVWFGnh97K0KGSGt12wVDddN7QU/6QhLlmJsfoibnJ8vWx6ImVu/XGpkMyDMPssHqEYRh6dWOO/vpptgZFBOjJq8cpKsTf7LAAAMApzEiKVnSov/6VnquKOpabBAD0fR2timhBdQT6ApIRANpVVFWvBW9v15HKev3k4hG66ox4s0NCByXHherZ76YoYUCQ/rnxkJ76YE+fX/bA7Tb04rosLdt0WCOjgrXo6vGKDLabHRYAAOgAX5tVN507RLUNzVq26bDZ4QAA4FGdqYpoQXUE+oIeTUasW7dO06dPV1RUlBwOhyZNmqRly5Z1aoy9e/fq1ltv1eDBg+Xn56eIiAjNmDFDH374oYeiBvqnspoGLXh7uwor6vWTi0dqelK02SGhk6JC/PU/14zXpBGR+nx/kR78V4aOVNaZHZZHNDW7tfjjvXp/e4HGx4fqiavGKTTA1+ywAABAJ1wwYoCGDwzSyu0FOlLRN39nAQBA6nxVRAuqI+DteiwZsXTpUk2dOlVr165VamqqpkyZovT0dF133XV69NFHOzTG559/rtTUVL300kvy9/fX5ZdfrhEjRuijjz7SzJkz9fTTT3v4KoD+obKuUb96Z6fyy+r0wwuHadrYzv1wRO/h72vTwzMTdcM5g5VdVK1739imnfnlZofVrWobmvWblbu1bu9RnTssQo/OSaK5OgAAXshqteiW8xPU1Gzon18eMjscAAA8oitVES2ojoC365FkRGFhoe644w4FBQVp48aN+uCDD7Ry5Upt3bpVTqdTjz/+uNLT0086RlNTk2666SZVV1frySef1J49e/T222/rq6++0kcffSQ/Pz899NBD2rFjR09cEtBn1TQ06dF3dupgUbVunjRUs8fHmh0STpPFYtH3zh6s+bMSVd/UrAVv79CHO11mh9Utiqrq9dBbGdqcU6pLxzj18GVj5OfDCoQAAHirlEFhShkUprV7jii7qNrscAAA6HZdrYpoQXUEvFmP3LF54YUXVFtbq7vvvlupqamt2xMTE7Vo0SIZhqElS5acdIy1a9cqOztbZ511lh566KHjMofTpk3TD37wA7ndbr3xxhueugygz2tocus3K3dr35EqffesQbpmIj0i+pJJwwfod9dM0IBgPz2/Zr/+tO6AmprdZofVZfuPVOq+ZduUXVStm84dop9eMkI2K83VAQDwdjdPGipJ+vtn2TIMw9xgAADoRqdTFdGC6gh4M5+eOMnKlSslSXPnzj1h39y5c3X77bfrvffeO+kYlZWVOuuss3TZZZe1uX/UqFGSpPz8/NMLFuinDMPQ82v2aXtuuS4fH6MbzxlsdkjwgKEDgvTMd1P05KpMvZdRoIPF1bpv+mgN8LJGzxuzivX0h3vkNgw9MGO0powaaHZIAACgm4yICtbU0VFak3lEX+eU6qyhEWaHBABAt0jLTlNeZZ4GhQySq6rrKxYYhqG8yjylZadp3vh53Rgh4Fker4wwDEO7du2SJCUnJ5+wPzw8XNHR0SotLVVeXl6741x11VX66quvtHDhwjb3f/XVV5Kk+Hie5Aa64p9fHlLanqM6OyFCP5g8rMsZevR+oQG+evzKJM0eH6MdeRX6yWtb9GVWsdlhdUiz29CrG3P02/d3y9/XpieuGkciAgCAPuj75w2R3ceqv32a7dWVnAAAfFNhdaESwhLkY/WRzWLr8svH6qOEsAQVVheafUlAp3i8MqK0tFR1dXVyOBwKCgpq85iYmBgVFBSosLBQcXFxnT7H9u3b9frrr8tisejqq68+3ZCBfufDnS4t23RYI6OC9cCM0bKy1E2f52Oz6ocXDteEQWH630/26Tcrd+vy8TG69fyhsvv0zubPRVX1evrDPdqZX6HhA4M0f9YYOUP8zQ4LAAB4QGSwXddMjNfSLw/p/R0uXTEhVoZh8MAMAMCr3XPuPTLUfUsQWsTPRXgXjycjqquPNR0LDAxs95iAgABJUlVVVafHP3LkiL7zne+oublZt956qyZMmNDmcfX19aqvrz9um91ul93uXUuTAN1tc06p/pC2X84Qux6ZM1b+vr3zRjQ849xhkRoxL1jPfLRXKzMKtD23XHdfPEJjYkLMDu04mw6W6NmP96qyrklXTIjVzZOG0qgaAIA+bu4Zcfpgp0uvf3lISfGGlu/5P90w/gZFB0ebHRoAAF1isVhOmkAwDEOHS2q121WhfYWVyiur09HKOlXVN6mh6VilYKCfjxz+PnKG+CsuLEAjooI1OtqhmFB/kvbo9TyejLDZjt3Y7MhfBre7c+W3+fn5mjZtmvbt26czzzxTzz//fLvHLlq06IQlnh566CE9+OCDnTpndyotLTXt3Oi4vjxP+eV1+s3K/fK1WnTPlDi566pUUmd2VF3Tl+fJ06yS7rswVu9u99W/txXqvjfSdfGoSF13RrSC7N2bnOrsPNU2NOuNLS59nFmsID+bfjI5XmcODlVVRVm3xoX/4u+Sd2CevINZ8xQRwfr66Bv8fW26edJQLf5or57+5Avlu9crJjiGtbEBAH3OgaNVWr27UF9mlehI5X8fpg7ws8kZ4i9niL/8fKwyDKmusVllNY3KyC3T5pz//r7pDPHXmUPDNXnkAI2JDmHVC/RKHk9GBAcHS5Jqa2vbPaZlX8uxHbFjxw7Nnj1bOTk5Ouuss/Thhx+etPpi/vz5uvfee4/b1hsqI/iy6B364jxV1zfp9yuz5JZVC69IVnJ8mNkhnba+OE896baLIjVt/GD9Ye1+rTtQrm0FNfp/k4dpysgB3fp0RUfmyTAMfXGgWH/+NFvFVQ1KGRKhn186SlEsy9Qj+LvkHZgn78A8AafnwpED9ebmA/psX60GDQlV2sE0TU2YqhhHjNmhAQBwWtxuQ58fKNLb6Xnad+TYajHOELvmTIjRuLgwjXQGa0Bw+/ct3W5Droo67TtSpZ355dp8sFQrMwq0MqNAzhC7po+N1oykaIUG+vbUJQGn5PFkhMPhkMPhUHl5uWpra1uXZPqmgoICScd6R3TExx9/rGuuuUYVFRWaMWOG/vWvf50ykdEbEg9Ab+F2G3r6oz3KL6vTHVOGaXwfSESgewyKCNRvrxqn1buP6O+fZ+vpD/doxbZ83XzeUI2LD+2RGPYfqdLfPsvWjrxyBfjZdNfU4Zo+NpqnOgAA6IesVouGx+Vo7b5muaumqMh3pdKy06iOAAB4LcMwtDGrRK9uPKjDJbWy+1g1faxTM5KjNTIquMMPA1qtFsWGBSg2LEAXjhoowzB0sLhGa/cc0do9R/Xqxhy9vumQLh3j1HVnDTppYgPoKR5PRlgsFiUlJWnjxo3avXu3UlNTj9tfUlIil8ul8PDwDjWvfu2113TzzTerqalJt99+u1588UX5+Hj8MoA+ZelXh/T1wVJNTYzSnPE8VYbjWSwWXTrWqbMSIvTal4f0wU6XfvH2diXFhujq1HidOSTcI4mBPa5KLfv6sL7KLpHFIs1IcurGc4coLNCv288FAAC8Q0FlgTLLVys6MklHSmM1Mmw01REAAK+VV1arF9ce0NbDZQrwtem7Z8bripQ4hQacfvWCxWJRwoAgJQxI0E3nDtEXWcV6Z2u+Ptjh0urdhbosOUbXnhnfDVcBdF2P3MW/7LLLtHHjRi1fvvyEZMTy5ctlGIZmzZp1ynFWrFih73//+2pubtavf/1rPfroo54KGeizNuwv0rJNhzUyKlh3TR1OcyO0KzTAVz++aLjmnhGrNzYd1to9R/X4e7vkDPHX9LFOXTR64Gkvm1RZ16gvDhTrgx2u1rLUScMj9b2zBythQFB3XAYAAPBiadlpKq4t1sQRVVq71ZCrcKzqnMupjgAAeBW329CKjHy9vOGgGpsNXTImSjefN1ThQZ55+M7HZtXkkQN1wYgBSj9Uqn9uPKR3t+Xrw50uTRsVppunhMrft3t7RAIdYTEMw/D0SXJzczV69GhZLBZ99NFHmjRpkiRpz549uuiii+RyubR161ZNmDBB0rFlm8rLyxUaGtq6dFNhYaHGjh2rkpIS/fKXv9Tjjz/u6bA9rqSkhHWEvUBfmqec4mrd/+Y2+fvatPi7KRro6Dslen1pnnqroqp6vbctX6szj6isplGSNGxgkM4cEq6xsSEa5XTI4X/ypzkKjhSp0rBre265MnLLtC23XM1uQ3Yfq6YmRunycTEaShLCVPxd8g7Mk3dgnuAp/eWzVVBZoEfSHpHNalN0cLQyDw3UvtyBGhS7SwMj8vTY1Md6dXVEf5knb8c8eQfmyTswT20rr23UMx/t0ZZDZXKG+OueS0cqOa5nlkFuYRiGvsgq1tKNh5R1pEJxEUH64YXDddZQ5qu36qt/n3qkMiI+Pl7PPfec7rjjDk2ZMkVTp06V3W7X6tWrVVdXp0WLFrUmIqRjzaZffvll3XzzzXrppZckSYsXL1ZJSYl8fHx04MAB3XjjjW2e6/zzz9ePf/zjnrgswKtU1jXqNyt3q6HZ0KNzEvtUIgI9Y0CwXbecn6Abzx2ir3NKteFAsTZll2jZ17mtx4QG+MoZ4q+QAB8F+fnIkKGmZkPltY0qqmpQfmmVbLZjP3psVosmxIfqgpEDdd7wSAXbWXIPAAD8V0tVxLiocZKkEXHFOlQYrsIjo+T23UV1BACg19vjqtSTq3arqKpBM5Kcuv2CYQrw6/mKBIvFoknDB+ichEj934a9emdHiR5bsUvnDY/UHZOHcY8IPabH7vzcfvvtio+P15NPPqmNGzfKZrMpNTVV9913n66++upTvn/VqlWSpKamJr3++usnPZZkBHA8t9vQ0x/ukau8Tj+6cHiPZ+DRt/jYrDp3WKTOHRapZreh7KJq7cwv18GiGuWW1qiwsl5ZRVVqaj5WeGexSA5/H4UF+umswaEaHRepsbEhSox2UBYKAADaVFBZoLSDaYoKimpdVtTH5taYIYXauj9ODVVn0jsCANCrfbrvqJ79eK8k6Z5LR+qSMU6TIzr2UOBlYwdqRspQ/fXTbH2+v0hbD5XptguGakZSNEt5w+N69DHUGTNmaMaMGac87qWXXmqtiGiRkZHhoaiAvu+VLw4q/VCZpo11ata4aLPDQR9is1o0IipYI6KCj9tuGIYamw1ZLZLVYmlteN1XywwBAED3+nZVRIv4geU66IpQUckQ2QK2Ux0BAOh1DMPQW+l5ennDQYUH+emR2WNP+M5stgHBdj18WaI255To+TX79ULaAW3MKtFPLh6hyGCqJOA5VrMDAOBZ6/ce1VvpeRod7dCPLqRhNXqGxWKRn49VPjar+MgBAIDOaKsqooXFIiUluGQYFlWVna20g2kqqCwwKVIAAI5nGIZe2nBQL284qCGRgXrm2gm9LhHxTROHROj5eam6ODFKm3NKdfdrW7R2zxH1QIth9FMkI4A+LOtolf539T6FBfpq/mWJ8vPhrzx6lqvKpcVfLJarymV2KAAAwEu0VEU4g9peziLCUau4AeWqqnLq4FGr0rLTejhCAABOZBiG/rQ+S/9Oz1NitENPfme8V/RiCLL76OfTRmnB5WPkY7PomY/26pmP9qqmocns0NAHcWcS6KMq6hr12/d3q9lt6BezxlBmB1OsyVqj9TnrtSZrjdmhAAAAL3CyqohvGjOkUDarWxUlZ2p19lqqIwAApjIMQ39en6WVGQUaHx+qx65MVrC9R1fHP23nDovU89en6uyECK3be1Q/+7+t2ldYaXZY6GNIRgB9ULPb0P98kKnCinr96MLhGhMTYnZI6IdabibYrDaWUAAAAB1yqqqIFgH2Jo2IK1JTY6gO5DuojgAAmKZlaab3MgqUHBeqX80eqwA/m9lhdUlooK9+efkY/WDKMBVV1ev+f2Xo3+m5crtZtgndg2QE0Af94/NsbTtcrpnJ0ZqZTMNqmKPlZkJyVLKKaoq4SQAAAE6qo1URLYbFFivA3qiKsvH6aP9nPPgAADDFv9PzWpdmemT2WPn7emciooXFYtGcCbF65toJignx1z8+P6iFK3aqrKbB7NDQB5CMAPqYtMwjemdrvsbEOPSDKcPMDgf91DdvJlgtVjmDnUo7mCZXJb0jAABA2zpaFdHCx2ZozJBCWYwA7T08kAcfAAA9bv3eo3ppw0ENjgjUo1ckeW1FRFuGDQzWku+l6NIxTqUfKtNPXt+iLYdKzQ4LXo5kBNCH7D9Sqd+v2afIYD/Nv2yMfG38FYc5vn0zwRnkVFFNkT479JnJkQEAgN6os1URLWIjKxQRUqPqypFaufsrqiMAAD1mR165nv1kryKC/PTrK5K8rkdER/j72vSzS0fq/hmjVd/o1iPv7NQ/Ps9WU7Pb7NDgpbhTCfQRZTUN+s3K3TIk/WLWGIUH+ZkdEvqptm4mWCwWOYOd+vTQp9wkAAAAJ+hsVUQLi0VKGuqSj9VXew7Fa00W1REAAM87XFKj36zcJV+rVY/OGauBDrvZIXnUhaMG6rnrz9BIZ7D+nZ6nB9/KUEF5rdlhwQuRjAD6gIYmt55YuVvFVQ26e+oIjXI6zA4J/Vh7NxOcQU6V1JawhAIAADhOV6siWoQF12lQVLka6mP074ztPPgAAPCospoGLVyxU7UNzXroskQNGxhsdkg9IjrUX099Z7yuTo3T/iNV+tnrW7Ums1CGQXNrdFzfqx8C+hnDMPTHtQeU6arU3DPidMmYzj1NBnSnk91MsFgsGhg0UGkH0zQ1YapiHDEmRQkAAHqTtOw05VXmaVDIILmqutZfKiKyWDlHztKeQ3H6eH+avn/GvG6OEgAAqbHZrUXvZ6qwol4/uXiEJg4JNzukHuVrs+rW8xN0xuBwPfvxXj378T5tzinVnReNUFAfXKYK3Y/KCMDLrcgo0Ce7CzVxSLhunTTU7HDQz51qiYWowCgV1RRRHQEAAFoVVhcqISxBPlYf2Sy2Lr0C/NwaHJ0rmxGhT/eybAQAwDP+9lm2dhVU6MqUWE1PijY7HNOkDArT7+edoXOHRWj93iL99PUt2pVfYXZY8AKkrAAvtvVwmf72aZZiw/x1/4zRslo7X9YOdJeOLLHQ0juC6ggAANDinnPvkaHTX+Khocmtn7y+RWWljSqtbqCHGgCgW32yq1ArMwqUHBeqW89PMDsc04X4++oXs8bow50u/eXTbM3/d4auO2uwrjtrkGzcn0I7qIwAvFR+Wa2eWpUpf1+bfjV7rIIph4PJOtp40hnkpDoCAPq5devWafr06YqKipLD4dCkSZO0bNmyTo1RUVGhX/7yl0pMTFRAQIDi4+P14x//WEeOHGn3PW+//bYuuOAChYeHKywsTNOmTdPq1atP93JwmiwWi6wW62m//H19dPsFw1TX6NYrX+SYfVkAgD5kX2Gl/rB2vwYE++mhmaO52f4fFotFM5NjtOS6FA2JDNLrXx3Sw29lqLCirstj0oOibyMZAXihmoYm/WblLtU0NOnBmaMVHx5odkjo5zrTePKb1RE0mASA/mfp0qWaOnWq1q5dq9TUVE2ZMkXp6em67rrr9Oijj3ZojMrKSk2dOlVPPPGEmpqaNHv2bAUHB+vFF19UamqqcnNzT3jPb3/7W1199dXaunWrJk+erDPPPFNpaWmaNm2a/v73v3f3ZcIk5yREaMKgUK3OLNS+wkqzwwEA9AHlNY367fu7JUm/mDVGYYFU3n3boIhAPX3tBF2ZEqtMV6V+8voWrd7d+ebWriqXFn+xuMs9pND7kYwAvIzbbejpD/fqcEmtbjl/qCYOiTA7JKDDVREtqI4AgP6psLBQd9xxh4KCgrRx40Z98MEHWrlypbZu3Sqn06nHH39c6enppxzn0UcfVXp6ur7//e8rMzNTb775pnbt2qWf//znysvL01133XXc8du2bdOCBQsUGxurHTt26N1339Unn3yitWvXKjAwUHfffbfy8/M9ddnoQRaLRXdMHiaLpL98msXTlQCA0+J2G/rdR5kqqmrQnReN0Einw+yQei0/H6v+3+Rh+vUVSfL3tWnJJ/v0+Hu7VVLd0OEx1mSt0fqc9VqTtcaDkcJMJCMAL/PXz7K06WCJpiZGaW5KnNnhAJ2qimhBdQQA9E8vvPCCamtrdffddys1NbV1e2JiohYtWiTDMLRkyZKTjlFRUaE///nPCgwM1JIlS+Tjc2ypSqvVqt/97ncaNmyY3n33XR04cKD1Pc8884wk6ZFHHtHQoUNbt19wwQW67777VFtbqxdffLH7LhSmGhIZpJnJMdpdUKlP9xWZHQ4AwIst+/qwth0u18zkaF06tmMP3/V3E4eE64V5Z2jq6IHadLBEdy1N19o9R075gEDLvQWb1ca9gj6MZATgRd7ZmqcV2wqUHBeiu6eO6PCNX8CTOlsV0YLqCADof1auXClJmjt37gn75s6dK4vFovfee++kY6xbt07V1dWaMmWKwsPDj9tns9k0Z86c484lSe+//367573qqqsk6ZTnhXe54dzBCrb76O+fZ6uusdnscAAAXmh7brle/+qQEgYE6f9NpmF1Zzj8fXXv9NH6xawx8rFZ9MxHe7VoVabKatqvkmi5t5Aclcy9gj6MZATgJTZmFetvn2UrPjxAv5g1Rn4+/PWF+bpSFdGC6ggA6F8Mw9CuXbskScnJySfsDw8PV3R0tEpLS5WXl9fuODt37mx3DEkaO3asJGn79u2SJJfLpeLiYg0YMEBO54mJ8zFjxshisWjXrl1qbuamdV8R4u+r688ZrOKqBv07vf3PEwAAbSmradD/fJgpu49ND12WKLuPzeyQvNJ5wyP1/LxUTR45QF8cKNZdr6Vr/d6jJ1RJfPPegtVi5V5BH8bdTMAL7Cus1O8+3KPQAF/9+ookOfx9zQ4JkNT1qogWVEcAQP9RWlqquro6ORwOBQUFtXlMTEyMpGO9JdrT0tuh5dhTjXGq4+12u8LDw1VfX6/y8vIOXAm8xazkaA2KCNBb6bk6WllvdjgAAC/hdht65qO9Kqtp1J1ThysuLMDskLxaaICvHpyZqIdmJsowpN99uEe/ff/4XhLfvrfAvYK+i2QE0MsVVtTpsfd2yTAM/fLysXKG+JsdEiDp9KoiWlAdAQD9R3V1tSQpMDCw3WMCAo592a+qquryON8eozvOW19fr4qKiuNe9fXc3O7tfGxW3X7BMDU0ufXqFwfNDgcA4CX+tTlXWw+XaUaSUxeNjjI7nD7jgpED9IcbjlVJbMwq0Z1LN2v17kLlV+SfcG+BewV9l4/ZAQBoX1lNg361fIfKaxv18GWJGh3tMDskoFVadpryKvM0KGSQXFWuDr2nrrpO/tbjE2qGYSivMk9p2WmaN36eJ0IFAPQCNtux5Q06ksB2u92nPU7LGN1x3kWLFmnhwoXHbXvooYf04IMPnnJMTygtLTXlvN4owSGNdQbok10FuighWEMje+7pVubJOzBP3oF58g59YZ52u6r08udZigvz1zXJ4SopKTE7pG5n9jz9v7OjNMFp1z++zNPTH+xWUEChKi1lmhCdoNqK2tbjQowQ7Ty6Uyu2rtA1SdeYGLE5zJiniIgIj5+DZATQS9U0NOnRd3eqoLxOd00drknDB5gdEnCcwupCJYR1romXzWKTzfKttTYtUkJYggqr21+SAwDg/YKDgyVJtbW17R7Tsq/l2K6M8+0xuuO88+fP17333nvcNrvdLrvd3u6YntYTXxb7ih9O9dM9b2zV2ztL9fjcuB49N/PkHZgn78A8eQdvnqeymgb9eeN+Bfr76VdXjFd0RPtVld7O7HmaERGhSWPi9b+rt+uN9Fz52OboSGiphjhL9c3nRwbZBmlT6SbN8Z2jGEfbS272ZWbPkyeQjAB6oYYmt36zcreyjlbrhnMGa2Zy//sHF73fPefeI0PGqQ/8hpKSknZ/mFrUtaWeAADeweFwyOFwqLy8XLW1ta1LI31TQcGxMvz2+jtIUlzcsZvJLlfbVXnfHuNUx9fX16u0tFR+fn7t/owyO/GA0zNsYLAuGh2ltMwjSj9UqtTB4WaHBADoZdxuQ89+vFcl1Q26d9ooDerDiYjewuHvq2GDMhXj+kzVpZO1PStG+UUhmjC8QEEBx/pJOIOcyijMYCWFPoSeEUAvc6xR0h5tzy3X5eNjdN1Zg8wOCWiTxWKR1WLttldX+04AALyDxWJRUlKSJGn37t0n7C8pKZHL5VJ4eHhrAqEtycnJkqRdu3a1uX/nzp2SpHHjxkmSIiMjFR0drcLCQhUXF59w/K5dx3pzJSUlyWrl61FfdeO5g+Vrs+ilzw/K7e7cwxQAgL7vX+m5Sj9UpkvHODU1kT4RPaGlD+Uwp3RRygENiS5RcUWQ1m0bpqz8CBkGvSP6In7bBnqQYZz8i4/bbej3a/Zrw4FiTR45QD+YPIwbtAAAoM+47LLLJEnLly8/Yd/y5ctlGIZmzZp10jEmT56soKAgrV27VuXl5cfta25u1ooVK2SxWDRz5swTzvvOO++cMN7bb78tSac8L7xblMNfcybEKruoWuv2HjU7HABAL7Ijr1xLN+ZocESgfnjhMLPD6TfSstNUXFssZ5BTvj5ujR/m0nlJB2X3a9LOg9H6fMdQVdb4yRnkVFFNkdKy08wOGd2AZATQQ1xVLi3+YnG7jX7dbkN/XHdAn+wu1MQh4fr5tFGyWklEAACAvuO2225TYGCgFi9erA0bNrRu37NnjxYsWCBJeuCBB1q3FxQUKDMzs3XpJUkKDAzU7bffrsrKSv3whz9UQ8OxMn7DMPTggw8qOztbc+fO1ahRo1rfc+edd8pqtWrBggXas2dP6/YNGzZo8eLF8vf3109+8hOPXTd6h2smxivY7qNXN+aooan9JukAgP6jvKZRT3+0R742qx6+LFH+vrZTvwmnraUqIioo6riHcAeE1ujCCQc0LLZYpZWBWr9tuPbnDdTAoGiqI/oIkhFAD1mTtUbrc9ZrTdaaE/YZhqE/rc/SBztcOmNwmH4xa4x8bfz1BAAAfUt8fLyee+451dTUaMqUKZo2bZpmz56tlJQUuVwuLVq0SBMmTGg9fv78+RozZozmz59/3DiPP/64kpOT9cYbb2jUqFG69tprNXbsWC1evFhDhw7VCy+8cNzxZ555phYsWCCXy6WUlBTNnj1b06ZN05QpU1RTU6O//e1vcjqdPfLfAOZx+PvqurMG6Whlvd7LyDc7HACAydxuQ89+slfFVQ26c+pw+kT0oG9WRXybj81Q0tBCnT8uW4H+Dco8FKV9Wecqt6SB6og+gLudQA9oyfjarLYTMrmGYeivn2br/e0FGh8fqgWXj5GfD381AQBA33T77bdr1apVmjx5sjZu3KjPPvtMqampeuutt/Twww93aIyQkBB9+umnuu+++yRJK1asUENDg+6880598cUXbTbAfuyxx7R06VKNHz9eaWlp2rp1q6ZOnao1a9Zo3jwaIvYXs8bFyBli17KvD6uyrtHscAAAJvr3ljxtzinVpWOcujiRhxJ6SntVEd8W4ajVlAlZGhl/VJU1/srPnaZXNmbrUFleD0aL7mYxTrWIPTympKREERERZoeBU+iOeXot4zX9a/e/lByVrO2F23Xt2Gs1b/w8GYahv39+UMu35Ck5LkSPzkmiJLCL+PvkHZin3o858g7Mk3dgnuApfLZOT9qeI1r80V7NPSNOt1+Q4LHzME/egXnyDsyTd/CmedqZX65f/Hu74sMD9cx3J/SrezFmz1PLPbJxUeM63Ce1vMpfW/fHylXu1vCBoXr2O5dqpNPh4UjNZfY8eQqPXwMe9s2Mr9VilTPYqbSDacqvyNcrX+Ro+ZY8jYlx6JHZJCIAAAAAT7tw5EANGxik9zLyVVhRZ3Y4AIAeVl7bqN99eKxPxEMz6RPRkzpaFfFtocF1mjw+S8Pj8nS4pEo/e+Nrvfn1YbndPGPvbUhGAB727XXwnEFOFdUU6amPN+hfm3M1OtqhX1+RpAA/fvgBAAAAnma1WnTr+Qlqajb0z405ZocDAOhBbrehZz8+1ifixxcN1+BI+kT0pJP1ijgVq1WaOLxBsfGfyOpTple+yNGC5Tt0tLLeA5HCU0hGAB7UVsbXYrGooeosrdlVp7gImxZekaRAPx+TIwUAAAD6j5RBYUodHKZ1e48q62iV2eEAAHrIv9JztTmnVBcnRumSMfSJ6EldrYr4JovFoiEDAhQV+4EmjwrSjrxy/fT1Ldqwv6ibo4WnkIwAPKitjO++3Ei5joyQj1+xzk7MVpCdRAQAAADQ074/aagMQ3rlC6ojAKA/2JxTqn9uzNGQyED9+KLhZofT76RlpymvMk+GYchV5eryyzAMuaoPKz5mt355+RhZrdKiVZn68/oDamx2m32ZOAWSEYCHtJXxPZAfqcxDToUE1il19D59kZemgsoCkyMFAAAA+p/hA4M1eeQAbc4p1Y68crPDAQB4UGFFnZ7+cI8C/WxacPkY+kSYoLC6UAlhCfKx+shmsXX55WP1UUJYggqrC3XOsEg9970zlBQbohXbCvTQWxk6Qj+oXo1HsgEPaamKGBc1TpKUlR+hXQedcgTW69ykHPn5RCqjMENp2WmaN36eydECAAAA/c+N5w7R5weK9fKGg/qfa8Z3edkIAEDvVdfYrCdW7lZ1Q5MenTNWMaEBZofUL91z7j0y1H0Npy069jM7MtiuJ64ap1e/OKi30vP0s//bqnunj9JZQyO67VzoPlRGAB7w7aqIg65w7TwYreCAep039qDsvs2yWCxyBjuVdpDqCAAAAMAMsWEBmpHkVKarUl9ll5gdDgCgmxmGoRfS9iu7qFrzzh6siUO4QW0Wi8Uiq8Xaba9vPkBgs1p0y/kJ+tXssZKkx1bs0v99dUiG0X3JD3QPkhGAB3yzV0SOK1zbs2IUFNCg85JyZPdrbj3OGeRUUU2R0rLTTIwWAAAA6L++d9Zg2X2seuWLHLnd3LQAgL7k3W35WrvnqM5OiNB3zxxkdjjwsLMTIrTkeylKGBCkpV8e0pMfZKqusfnUb0SPIRkBdLNvVkUcPhqujKwYBfo36LyxB+Xv13TcsVRHAAAAAOaKCPLTlSmxOlRSo7Q9R8wOBwDQTb7KLtHfP8tWXFiA7p02SlYrS/H1B84Qf/3PNeN1/ogB2rC/WA/8K0OF9JHoNUhGAN2spSqisWaUtu2PVaC9QeclHVSAvanN46mOAAAAAMx1VWq8gu0+WvrlITU0uc0OBwBwmrKOVul3H2Yq2N9Hj14xVkF22ub2J/6+Nj00c7RuPHewDhZV695lW7Ujr9zssCCSEUC3aqmKsDYkaduBOAXYG3VeUo4C20lESFRHAAAAAGYLtvvo2jPjdbSyXqt28Ds5AHizoqp6PfbeLjW5DS2YRcPq/spisei6swZrweVj1NhkaMHyHXp/ewF9JExGMgLoRmnZaTp41FcHc5Nl923SeUkHFejfeMr3UR0BAAAAmOvy8TGKDPbTsq8Pq6ah/YeJAAC9V21Dsx5/b5eKqxr0s0tGamxsiNkhwWTnDovU09dOUJTDrj+uPaA/rjugpmaqIM1CMgLoJgWVBVq+fYuOus6Tr0+zzh2bo6AOJCIkqiMAAAAAs9l9bJp39mBV1Dbp7S15ZocDAOikpma3nv5oj7KOVuv6swfrotFRZoeEXmJwZKAWf3eCxseHatV2lxau2KWqeh48MAPJCKCbvJa+TjuzEuXv46Nzx+bIEdjQqfdTHQEAAACY65IxTsWHB2j5ljyV1XTu93kAgHncbkPPrd6nr7JLNHX0QF1/9iCzQ0Iv4/D31cIrkjQjyamth8v0wJvbVFBea3ZY/Q7JCKAbbMjK0quf18vP5qtzxh5SaFB9p8egOgIAAAAwl81q0U3nDlFdo1tvbDpsdjgAgA4wDEN//jRLaXuO6uyECP30kpGyWCxmh4VeyMdm1V1TR+j/TU5QXlmt7lu2jcbWPYxW8sBpyimu1i/f2a76pkZNGLlX9ZYKuaq6NpZhGMqrzFNadprmjZ/XvYECAAAAOKXzhkdqZFSwVu1wae4ZcXKG+JsdEgDgJP65MUcrMwo0Lj5UD81MlI+NZ6/RPovFoitT4hQd4q9nPtqrXy7fobunjtClY51mh9Yv8LfTRHRv9355ZbX65fIdqmloVMrIbEWGVMtmsXX55WP1UUJYggqrC82+NAAAAKBfslgsunnSUDW7DS3dmGN2OACAk3hrc66WfZ2rUU6HfnX5WPn5cKsTHXPOsEg9dc14RQT56X9X79PLGw7K7eZeradRGWESV5VLf9n0F91xwR2KDo42Oxx0QUl1gx59Z4cqahv1zHcu1HnDr+62sS2inBAAAAAwy4RBYTpjcJjW7j2qq1LjlTAgyOyQAADf8tbmXL204aAGRwbq11eMVYCfzeyQ4GUSBgTpmWsn6Dcrd+tfm3OVX1arn08bJX9fPkueQrrQJGuy1mjD4Q1ak7XG7FDQBTUNTfr1uztVWFGvuy8eqfNHDJTVYu22F2sbAgAAAOb6/nlDZRjSq19QHQEAvYlhGHrty0OtiYgn5ibL4e9rdljwUuFBfvrt1cmaPHKANhwo1sNvZaioqvO9YNExJCNMUFBZoLSDabJZbTQr9kKNzW799v3dyi6q1k3nDtE01pQDAAAA+pwRUcGaPHKANh0s0c58mlsCQG9gGIb+8flBvf7VIY2MCtaiq8cpLNDP7LDg5ew+Nj0wY7SuP3uwDhyt1n3Ltmn/kS42hMVJ9VgyYt26dZo+fbqioqLkcDg0adIkLVu2rMvjGYahSy+9VPHx8d0YZc9Iy05TcW2xxgwYo6KaIqVlp5kdEjrI7Ta05JO92na4XLPGxejaM73v8wcAAACgY244d4isVote3nCQnn8AYDK329Af1x3Q21vyNCbGocfnJiuEigh0E4vFonnnDNb9M0arsq5RD7+VoQ37i8wOq8/pkWTE0qVLNXXqVK1du1apqamaMmWK0tPTdd111+nRRx/t0pj333+/Vq9e3c2Rel5LVURUUJSsFqucwU6qI7zIPzYc1Pq9RZo0PFI/nDKM5ZQAAACAPiwuLEDTxzq1u6BSX2WXmB0OAPQb304A1zc166kPM7Vqu0sTBoXqsSuTFWSnFS6634WjBuq3V49TgJ9Ni1Zl6s2vD/NAQjfyeDKisLBQd9xxh4KCgrRx40Z98MEHWrlypbZu3Sqn06nHH39c6enpHR6vpqZGt9xyixYvXuzBqD2npSrCGXRsaR9nkJPqCC+xanuBlm/J09iYEN07fZSsVhIRAAAAQF/3vbMGyc/Hqlc25sjt5mYEAHiaq8qlxV8slqvKJUkqq2nQgrd3aMP+Yp0/YoAemZ1Eg2F4VGJ0iJ65doIGRwbqlS9ytOSTfWpocpsdVp/g8WTECy+8oNraWt19991KTU1t3Z6YmKhFixbJMAwtWbKkQ2O98847SklJ0csvv6xhw4Z5KGLP+WZVRMsT9RaLheoIL5B+qFQvrjug6FB//eLyMbL78EMPAAAA6A8ig+26MiVWh4prtG7vUbPDAYA+b03WGq3PWa81WWt0uKRG97+5TXtclbpmYrwenDFafj60wIXnRYX46+lrJujMoeFak3lEj7yzQ+W1jWaH5fU8/rd35cqVkqS5c+eesG/u3LmyWCx67733TjlOWVmZ5s6dq6ysLP3sZz/r0Ht6m29XRbSgOqJ3yy2r05OrMhXgZ9Ojc8YqNID1CAEAAID+5OrUeAXbffTPjTk8GQkAHtTyIK/NatPy7Vv0szc26Whlve6aOkI3TxrKKhXoUQF+Nv3q8rG6MiVWO/MrdN+ybTpcUmN2WF7No8kIwzC0a9cuSVJycvIJ+8PDwxUdHa3S0lLl5eWddCyr1ap58+YpIyNDS5YsUUBAgEdi9pS2qiJaUB3Re5XVNOjp1QdV3+TW/MvGKD480OyQAAAAAPSwYLuPrpkYryOV9fpgp8vscACgz0rLTlNRTbH8Gy/SjgNjVVJbpl9fkaSZydFmh4Z+ymq16P9NHqa7pg7X0ap63f/mNn2ZVWx2WF7Lo8mI0tJS1dXVyeFwKCgoqM1jYmJiJB3rLXEyISEhWrp0qcaOHdvtcfaE9qoiWlAd0fs0NLn1xMrdOlrVoDsvGq4Jg8LMDgkAAACASWZPiFFEkJ+WbTqs2oZms8MBgD6noLJAHx9Yr9IjF2nPoWiFBDYqftBqRYfXmR0aoJnJMVp4RZKsFot+s3K3/vpplhqbqZbsLI8mI6qrqyVJgYHtP03eUuFQVVXlyVBUX1+vioqK41719fUePWeLk1VFtKA6oncxDEMvrjugTFelLk8aqBlJZOABAACA/szuY9P1Zw9WeW2j3t5y8sp+AEDn/StjndIzx6myKlpxA8p1SUq+qpsP8+Aueo2UQWF67vozlBjt0Dtb8/XwW9tVWEGyrDN8PDm4zXasyW97N+C/ye32bCZp0aJFWrhw4XHbHnroIT344IMePa8krdi5QgVHCzR2wFjVVtS2bq+trD3uuBAjRDuP7tSKrSt0TdI1Ho8L7Vu9t1irMvI0LjZYM4bZVVJSYnZIOIXS0lKzQ0AHME+9H3PkHZgn72DWPEVERJhyXqA/mDbWqbe35Gr5ljzNGhetsEA/s0MCAK9nGIaWbtqtv65tkIxQJQ93aWh0iSwWtT64OzVhqmIcMWaHCmigw65FV4/TPzfm6K30PP3s/7bo7qkjdcHIAWaH5hU8mowIDg6WJNXW1rZ7TMu+lmM9Zf78+br33nuP22a322W32z163oLKAm0q3aR4Z7wCg0+sEAkIOb73xSDbIG0q3aQ5vnP4R9YkuwsqtHTzEcVFBOlXV05QY00lX+q9BPPkHZin3o858g7Mk3dgnoC+xWa16PvnDdWTqzL15te5umPKMLNDAgCvVl7TqOfW7NMHu/ZJlipNGVehyJD/PmnuDHIqozBDadlpmjd+nomRAv/lY7PqlvMTNC4+VIs/3qunPsjU5wcG6EcXDldogK/Z4fVqHl2myeFwyOFwqLy8vN2EREHBsSWJWnpHeIrdbldISMhxL08nIqRT94r4NnpHmKukukGLVmXKx2rRgsvHyuHPPyAAAAAA/mvS8EiNjArW+zsKWJoBAE7DlkOluvv1dH2+v1B+gQd0VtLO4xIREsuao3ebOCRCL8xL1XnDI/XZviLd/Vq6NuwvMjusXs2jyQiLxaKkpCRJ0u7du0/YX1JSIpfLpfDwcMXFxXkyFFN0pFfEt/GPrHkam91a9P5ulVY36KeXjFTCgLabrgMAAADovywWi74/aaiamg0t/fKQ2eEAgNepbWjWH9ce0CPv7FR9o1vnjD6q8IGfKS6k7WVueHAXvVlYoJ/mX5ao+2eMVlOzoUWrMvXkqkwdreyZXsXexqPJCEm67LLLJEnLly8/Yd/y5ctlGIZmzZrl6TBM0dmqiBb8I2uOv3yapUxXpa46I05TRg00OxwAAAAAvVTKoDClDArT2j1HlFNcbXY4AOA1tueW6yevp+v97QVKjgvRgjmxym/8+KQP8vLgLno7i8WiC0cN1B9uSNW5wyL0+f4i/fifm/XGpkNqaPJsn2Rv4/FkxG233abAwEAtXrxYGzZsaN2+Z88eLViwQJL0wAMPtG4vKChQZmZm6/JN3upUVRH78yJVWRvQxjv5R9YMH+10adV2lyYM+v/s3Xd8VFX+//H3zCSZJJNJI6SQBEhooUMQCwoIitJUbIu9ruta1rUL1lW/mt2fK7qufdV13UXXjlIUFQKCiCCdhFBDMSSB9F7n/v5gE41JSCZkMpnk9Xw85qHce+bM5+bce5Mzn3vOCdK14/q6OxwAAAAAndw1p/WRYUhvr9nv7lAAoNOrqK7Vqyv36sFPtym/rFq/mxCvp2YNV0rud616kJcHd+EJQmw+emjGEP3p/CEKC7DqP2sP6tb5G7RmT44Mw3B3eJ2Cy5MRMTExeuGFF1RWVqYJEyZoypQpmjlzpkaNGqWsrCwlJSVp5MiR9eXnzp2rwYMHa+7cua4OzaWONyqipNxHOw5E6PtdCfpue18dzgmU41dJMm6yHWdXdrFeWblX4Xar7js3QRZz66bUAgAAANB9DYiwa8LAMP24P18bDuS5Oxx0UXx5ha5ge0ahbn93kxZvzdTQXoH6++Wjdd7IXsouzWr19OY8uAtPMqZPqP5+xWjdcEZfFZXXKOmLNN31/mb9sC+3xft6V7/vuzwZIUk33nijvvjiC40fP15r167V6tWrlZiYqI8//lhz5szpiBA6VEujIgL8qjR+xD71CslTQbGfNuyK0bKNA7Qno4dqao81CTfZjlFQVqWnl+yQSdKDMwaz4j0AAACAVrv+9DhZvcx6/dt9TMOAdpdVkqV5389TVkmWu0MB2qSsqkavrNiruZ9sU35ZlX47Pk5PXzhcvYKPzRTi7PTmPLgLT+JtMevC0TF6/ZoxumBULx3MK9P/Ld6hO9/frLX7cuVwNE46dIf7vldHfdC5556rc889t8Vyb7/9tt5+++0Wy/Xt27fTZoqS05OVUZyh2MDY4548PXtUKCLariO5kcrK7aWt6aHacciuXuE/KTLssMwmQxnFGUpOT9YVI67owCPoHmpqHfrzF2nKLanS3VMGql/PAHeHBAAAAMCDhAVY9ZuTYvXvtQe0cMthXTwmxt0hoQtZvm+5vj3wraICovhOAB5n7b5cvbpyr3JLqjS0V6D+cNYARQf/PF15Sw/yNuWXD+5OipukKHuUq8IH2k2wv49+Oz5eFyXG6JONP2nJtkw9tXiHooJ8NX14lM4aHC6777GHo395358aM9XNkbtGh4yM6G6yS7MVFxwnL7OXLCbLcV9+Pg71iTqssUM2qH/sPplNhg4ejtem1JOVnROrPoHxyi7NdvchdUlvrk5XyuEinT+ylyYlhLs7HAAAAAAeaNboaEUG+er99YeUW1Lp7nDQRdR9UWsxW5gxAR4lr7RKSUt26KnFO1ReVavbJvXT0xcOb5CIkJwfFVGH0RHwVKG2Y0mJN64dq0vGxKi0qkZvrk7XtW+t09++2a1Ve/ZqefrP9/2s4q45OqLDRkZ0J3eeeqcMtTxqIy8vT6GhoQ22VdU49HVqtj788SfllfSTw8tPE0fEuyrUbmt5WrYWbc3UsOhAXX96X3eHAwAAAMBD+XiZddP4eD25KFVvr9mve84Z5O6Q0AXUfVE7LHyYtmVvY8YEdHoOh6Flu3L10ZadKquq1bh+PXTzxH4Ktfk0KtuWURF1GB0BTxdq89G14/rq8pN767s9OVq8LVPf7MjWBxtzVVI7WsN7VyunbIVWH1ytIX2GuDvcdsfICBcwmUwym8xtevl6e+m8kdH6x7Un6apT+iinpEqPfZ6q/1uUqqzCCncfWpew50ixXly+Rz0CfPTA1AR5WbgMAAAAALTd2L4hGtMnRCt2HlXK4UJ3hwMP98svas0mM+tJotM7lFemBz/dpre+z5Cfj0UPzRisudMHN5mIkNo+KqIOoyPQFfh4mTUpIVx/vXSkHj4vWrbAHfI2+ams0qqIgAitOriqS973+Ra2k7J6WXTZyb31ylWJOmNAmH5Iz9Ot8zfoow0/qbaJBU7QOoVl1Xp6SZok6aHpgxXs3/QvRgAAALS/lStX6pxzzlF4eLjsdrvGjRunDz74wOl6ioqK9PDDDyshIUF+fn6KiYnRLbfcoiNHjjRZ/rnnnpPJZGr2NWfOnBM9NHRzJpNJN02Il8Vs0msr99Fnwwn59Re1fPGKzqq61qH31h3UHf/dpNTMIk1J6KGXr0zUqfE9mn3PiYyKqPPL0RFd8ctadD97i75XQMg6TT95vwb3PqIIW4TyyvO65H2faZo6uXC7rx6YmqBpwwr0cvJe/WvNfq3Zk6M7zx6o3j383R2eR6l1GPrL0jQdLa7UH88aoAERdneHBAAA0G3Mnz9fV199tby8vDR58mRZLBYtW7ZMs2fPVkpKih5//PFW1VNcXKxJkyZp48aN6tevn2bOnKlt27bp1Vdf1cKFC7V27VrFxDRcRHjjxo2SpBkzZig4OLhRnaNHjz7h4wOig/10UWK0PvzxJ322OUNn9qW/Buc19UUt09KgM9qRWaQXl+/Rwbwy9Q711+2T+yvCWiN/n+N/1ZicnqyM4gzFBsYqq6Ttc+IbhqGM4gymMIPHazAazmyS2eyQZFJPW88ued8nGeEhRsQE62+Xj9K7PxzUgk0Z+uP7m3T5yb11cWKMLOa2ZZK7m39+l65tPxVqxogonT2kbUMBAQAA4Lzs7GzddNNNstlsWrlypRITEyVJaWlpOvPMM/Xkk0/qggsuqN9+PI899pg2btyoa665Rm+++aa8vLzkcDh077336rnnntNtt92mzz77rMF7Nm3aJJPJpPfee092Ow+kwHVmj43Vd3ty9J+1B5QQGq9fLREItKhuVMTw8OENtkfYIrQ1eytfvMLtiiuq9a81+7U0JVteFpOuPKW3Lh4TI2+LWXl5eS2+P7s0W3HBcSceiEmKC45Tdmn2idcFuFFz9/1w/3DtLtvd5e77JCM8iNXLoutPj9Np/Xrob9/s1r+/P6D16Xm679xBCg/0dXd4ndrytGx9tvmwhkQF6sYz2uGXHgAAAFrtpZdeUnl5uebMmdMg4ZCQkKCkpCTdcMMNev755/XOO+8ct56ioiK9/vrr8vf31/PPPy8vr2PdGbPZrGeeeUafffaZPv/8c+3du1f9+vWTJJWXlystLU0DBw4kEQGXs3pZ9IfJAzT3k236x5qf9GzvSJl5eAytdLzpaxgdAXczDEPLdhzRP9ekq6i8RsNjgnTLxH6KDXVuFNidp94pQ+03lZ1J3GPhubrjfZ81IzxQQmSg/nbZaF0wqpfSsop1x383ac2eHHeH1WntzDq2YHVYgI/mTk+QNwtWAwAAdKjFixdLkmbNmtVo36xZs2QymbRo0aIW61m5cqVKS0s1YcIEhYSENNhnsVh03nnnNfg8Sdq6datqa2s1ZsyYEzgCoPWGRQdp6rBIpWWX6qvUtk9Bgu6npUV9u+PaEYbB+iudwYHcUs35eJv+tmy3zCaT7j5noJ6aNczpRIR07AtWs8ncbq+2rjsBdAbd8b7Pt7IeysfLrN+Oj9dj5w2RxWxS0hdpeil5jyprat0dWqeSW1Kpp5bskMlk0kMzhrBgNQAAQAczDEOpqamSpGHDhjXaHxISosjISOXn5ysjI+O4daWkpDRbjyQNGTJEkrRt27b6bXXrRYSEhOjmm29WfHy8fH19lZCQoCeffFIVFRXOHxTQgutP76tQm7fe+m6/ckoq3R0OPEBrFvXtbov2ZpVkad73805oXQGcmPKqWv3zu3Td8d/N2pFVpOnDo/TKVWM0aVDbF58GcEx3ve+TjPBwJ/UN1QuXjdaImCB9uT1L93ywRYcLyt0dVqdQVePQU0t2KL+0Sn88a4D6hwe4OyQAAIBuJz8/XxUVFbLb7bLZbE2WiYo6Nuw8O/v48z4fPny4QfnW1FOXjHjppZf06aefasSIERo7dqwOHjyoRx99VJMmTVJpaalzBwW0wN/HS9efEq3yqlq9smIvT3ejRS09HVunKz4l25zl+5br2wPfavm+5e4OpdsxDENr9ubo1vkb9MnGDMWH2fTspSN1y5n9FGBlxnegPXTX+z53kC6gR4BVT14wTB9uOKT5PxzUXe9v1j3nDNLJcd13tTTDMPRi8h7tzi7RpSfFaMLAnu4OCQAAoMu48sortWHDhhbLnXzyyXrqqackSf7+zU/l4OfnJ0kqKSk5bn11SYPm6mqqnk2bNkmSbrjhBr388suyWq2SpPT0dF144YVau3at5syZo7///e/Nfm5lZaUqKxs+3W61WuvrApqSGBuoCQPD9O2uHK3YeVSTEsLdHRI6qdY8HVun7inZ5enJGhc7UYE+PVVaWaOyqlpV1TjkMH6ejd8kyeptlq+XRb7eFvl6m2X39ZbFA9YxqfuZWMwWJe9P1qigUQplRfgOcSC3VG+uTtemgwXy87Ho5onxmj4sivVvgHbUlvt+V1k7gmREF2E2mzR7bG8NiLDrmS936slFqZo9NlZXnNy7W/7C+ODHQ0pOO6KT+oboqlP6uDscAACALuXAgQPauXNni+UiIyNlsVgkqVXTOTgcjuPub21dv6zn22+/VXp6uhISEurfL0lxcXF6++23lZiYqDfeeEPPPPOMfH19m6wvKSlJjz/+eINtDzzwgO6///7jxuEq+fn5bvlcOCc/P1+/GR6qjftz9MI3aerl51BPO9PGdjad4XpamLJQmUczNSRsiMqLfp7pwDCkympvFZX7q6TCV+VVVlVUe6u8qq/yymp0Weo6hfiGHKfmxkwmyW71UqCvl4L9vBTo56UgXy/1sHkrPMBHPQN81NPuIz9vS8uVuVDdz2Rw2GClHknV0u1LFWmPdGtMXV1RRY0+2pyt5F25chjSGf1CdPmYSAX7eaugoHXXSWe4ntAy2sn9mrvv/1J58c/bA41ApRxN0cLNC3XJ0EtcFldHJH1JRnQxib1D9Pxlo5S0ZIfeX39Ie46U6J5zBsru6+3u0DrM8rRs/WftQcWF2XT/uQndMhkDAADgSqtXr2512aKiIklSeXnzU4nW7QsIOP60mnX7m6urqXr8/f01dOjQJsuPGjVKMTExOnTokFJSUppd5Hru3Lm6++67G2xz98gInhD2DKGhobpvmrce+yxFb67PVtJFIzziqfTuxp3XU2Zxptbnr1dMRIys/jblF/spp9CmvGJ/FZX6qrqmYVLAYnbIz1qtniElslj2afLAyYq0h8jfx0tWb7NMMslkOjYqwmFIlTW1qqh2qLKmVmVVtSoqr1ZBWbUKyqt0oLBa5UebXjcnyM9b4YFWRQb6KjrET72C/BQd4qeoIF+Xf7/wy5+JLcCmWEusNhZs1GXel3n8E8GdUVWNQwu3HNb7Px5SeVWthsWE6rfj4zQgwt6m+vj95BloJ/f55T3OP+D4i8D7BfrV/3+sJVbr89frPO/zPPpeSDKiC4oI9NVfLhmhV1fs0zc7snXX+5s1d/pg9evZ9ddM2HyoQH9btkdhAT567Lwh8vNx79McAAAA3Z3dbpfdbldhYaHKy8vrp1L6pczMYwvyNbcWRJ3o6GhJUlZW04uZtraeX4qMjNShQ4dUVlbWbBl3Jx7g2RJ7h+j8kb30+ZbD+u/6g7qSkdv4hc9SVmhXRoistYO1ocRfDsexZJXF4lCQf4UCbRUK9K9UoK1C/r5V8vGqlcl0bGrirdlbFR0ZpStGXNHmz6+sqVVBWbWOFlcqu6hCWUUVyi489t+sokrtzm48fZ7d10vRwX6KCvZTTLCfooJ9j/07yK9d+uB186gPDx8u6dh86T9m/ajk9OQTOlY05HAYWrM3V2+v2a/sogpFBFr1x7MGaFy/HixODbjQr+9xrRVhi9DW7K0efy8kGdFFWb0suuOs/hoUaddr3+7VfR9u0e2T+2tywvEXRfFkB3JL9fSSHfL1Muvx84epRwAdRgAAAHczmUwaOnSo1q5dqx07digxMbHB/ry8PGVlZSkkJKQ+2dCcYcOGSZJSU1Ob3J+SkiJJGj78WOfu8OHDevTRR1VZWal///vfTb5n3759kqSYmJjWHxTgpGvH9dX2w4V6f/0hDYkK1Ojezk2tg64lu6hCq3bn6Jsdh/Rdeq1MppHy9bKoR2CpegSWKiyoVEEBFTreIJr2mkPc6mVRRKBFEYG+GhYd1Gh/RXWtDheUK7OwQhn55TpcWK6M/GP/TssqblQ+xOaj6GBf9QryU6/gY6/oYD9FBvnKx8vcYjxNzaNuMpnU09azy8yX7m6GYeiH9Dy9+8NBpeeUys/bomvH9dX5I3u1qo0AtJ0za0X8WldZO4JkRBdmMpk0dVik4nva9PSSHXru693alV2iG8+Ik7ela/2CySmp1J8+T1FVjUOPnz9UvXscf5gTAAAAOs60adO0du1aLViwoFEyYsGCBTIMQ9OnT2+xnvHjx8tms2nFihUqLCxUUNDPX5zV1tZq4cKFx/4GnjpVkhQYGKj//Oc/qqys1D333KNRo0Y1qG/x4sXKzc3VwIEDFRcXd+IHCjTDx8usB6Ym6M73N+vZr3bp+ctGKYyHp7qVmlqH1qXn6cuULG06WCBJKq7Olc2+XyNjfRUeUiqL2Th+Jb/SEU/J+npbFN8zQPFNzLRQUlmjzIJyZRSU63BBhQ4XlOtwQbn2HS3V9oyiBmVNJqlngLU+QdGrbjRFsJ8i7FZ5/e87iuaeGA73D9fust0e/0SwOxmGoQ0H8vXuDwe1+0iJvC0mzRodrYsToxXsz3o2QEdo66iIOl1hdATJiG5gYIRdz88epb98uVOLt2Zq75ESzZmW0GVGDuSWVOrBT7Ypp6RKd08ZqJGxwe4OCQAAAL9www036C9/+YvmzZunqVOnaty4cZKknTt36qGHHpIk3XfffQ3ek5mZWZ9wqJt2yd/fXzfeeKNeeOEF3XzzzXrnnXfk4+MjwzB0//33Kz09XRdeeKEGDhwo6djaEVdeeaXeeust3XTTTVqyZIl69uwpSdq9e7duu+02SdKjjz7aIT8HdG+9gv1051kDlPRFmpKWpCnpouE8hdwNFJRVadHWTH2Vmq380iqZzSaN69dDo/t66cNd78jby6zIgLYtzOzup2QDrF4aEGFvtLaAYRgqLK/+OUFR+HPCIjWzSJsPFTQobzabFBloVZC/Q5uPHpS/70jlFPrL5lslP2v1sTUwusgTwe5QVePQt7uO6tPNGTqYWyYvi0kzR0Tp0pNiFWojCQF0lBMZFVGnK9wLTYZhOJd6R7vJy8vr0AVjah2G3l6zXws2ZSjY31tzpiVoaK/GwzA9SX5pleZ+sk0ZBeW69cx+mja8/S/Cjm4ntA3t5Blop86PNvIMtJNnoJ0aevPNN3XTTTfJbDZr0qRJslqtWrZsmSoqKpSUlKQ5c+Y0KH/dddfpX//6l6699lq9/fbb9duLiop0+umna/v27erTp4/Gjh2r7du3Ky0tTX379tWaNWsarBmRn5+vM888U1u3blVQUJBOP/101dbWasWKFaqsrNRdd92lefPmddSPoV1wbnmG5trp7e/S9fHGDJ01OFx/PGsAc7O7mauup+yiCn26KUNfpWSputZQuN2qc4dG6uwhEQq1+ejdre/qP9v+o9jA2BM6BwzD0KGiQ7pq+FUe8ZSsw2Eor6yqfhRFxi9GVGzP/Em5Zfny8/55pgOz2ZDNt0pWc6mCAh06WrFL0xNO1Y1jL1KIv3eXv34Mw2jzMRaUVemr1Gwt2pqp/NIq+XlbdM7QCM0aHe2ykVn8fvIMtJN7OHvfryiukK/dt9F2T7vv/xojI7oRi9mkG8+I04DwAL2wbLce/HS7bhofpxnDozzyF3hBWZUeXrBdGQXlunlivEsSEQAAAGgfN954o2JiYvTnP/9Za9eulcViUWJiou655x5ddNFFra4nMDBQq1at0v/93//po48+0sKFCxUdHa1bb71VjzzyiCIjGz5hHBISojVr1uivf/2r3n//fS1btky+vr467bTTdMcdd+jCCy9s70MFjuua0/pqf26Zlu04orgwmy4Ydfy1UuBZMgrK9f66g1q566gchtQ/PECXjonRqfE9ZP7FIhDZpdmKC26H6eFMUlxwnLJLs0+8rg5gNpsUFmBVWIBVI2KC67dnFmfqkeWvqqbGJn9ztEorfFRaYVVpuY9KKnx0tCRIOSUWVTuC9M6qGi3b/J0CfX0VGeT7v3UpfBUV9PMUUEF+np+oyCrJ0vyt83XliCtbPXrG4TC06VC+vkrJ1tr0PDkchnoE+Oi6cX117rBIBVj5GhBwF2fv+xaTRRaTpfEOD7vv/xojI9zInZnI/TnHFnvOLKzQ+AFhun1yf/n7eM4vpcLyaj306TYdyC3Tb8fHufQPeDLGnoF28gy0U+dHG3kG2skz0E5wFc4tz3C8diqprNF9H25RRkG55kxN0Lj+YR0cHeq01/WUW1Kp/64/pK9SsuQwpOExQbp0TIxGxQY3+aW4YRgy1H5fx5hk8ugv39/d+q4+2vGRhocPb/I4SgsqZFgDVVzmo7TsLA0KOU09fQfqcEG5ckqqGpX387EcW5OiPlnhp6j/rVNh9/XuiEM6Ye9ufVfvp7yv2UNnH/fpZ4fD0M7sYn2/N1ff7j6q3P/9PEbGBumcIZE6rV+PDls3lN9PnoF2cg9n7/sttZOn3vc959tntKu+YTbNmz1Kz3+9S6t252j3kRLdf+6gRnM9dkZHiiv02Gcp+im/XNeN68uTRAAAAAA8SoDVS386f6ju/XCL/vrVTv2fv4+G9Ap0d1hog5LKGn284Sd9vuWwqmocGhxl17Xj+rY4JbLJZJJJnvclkiu0Zh51s9mQn1+VAvyqZLKWqNbxpW6fNE5R9ihV1tQqu7Dyf+tSlCuz8Nj0T5mF5dpzpKRRXXZfr/rFs2N+saB2r2A/+Xo38RSyG9T9TCxmS5Nzw5dX1So1s1Dr0vP1/b5c5ZceS0CEBfho9thYTRkSoYjAxtO7AHAfZ+/7ZpNZZlPXW1uKZEQ3FmD10kMzBmvR1ky99V267vtoq64/va/OH9mr02bWDuaW6dHPtyu3pMrlIyIAAAAAwFUiAn312HlDNPeTbXpiUYqevnC44nsGuDsstJLDYeibHdn61/f7VVReo949/HXtaX01tm9Ip+1Pd1bJ6cnKLc/V8PDhrSofYYvQ1uytSk5P1hUjrpDVy6LePfzVu4d/o7IV1bXKLKxQZsHPi2jXLaqdllXcqHyPAJ/6kRS9gn3VK8hP0SF+igj07bDRBdLPP5Nh4cO0NWubFu5YodHh5yolo1DbMoq050ixHP97wLpXsK/OGhOjcf16qH94AOcfgE6NZEQ3ZzKZdN7IXhrSK1D/78s0vbEqXZsOFujWSf0U3sQiKe60Lj1Pf126U1W1Dt1zzkCdOSjc3SEBAAAAQJv1D7froRlD9PjCFD3y2Xb9+aIRig1t/IUqOpfd2cV6ZeVe7c4uUbC/t/541gBNTghvsCYEWqc1oyJ+zWQyKSIgoskRA7/m621RXJhNcWG2RvtKK2v+t4j2L5IUBcdGU2z7qbBBWbNJCg/0VWSgr8ICrAoN8FHPAB+F2qwKC/BRqM1Hdl9vWU7gHCivqtXR4kqlZmfovR93qrr6VP2Q01M5RfH6+55aRdu3yNviLZvVorF9QzU8JkijY0MUG+pHAgKAxyAZAUlSv54Ben72aL327V4t23FEt8/fpBvO6KtzhkS6/Q8qh8PQRxt/0n/WHpDNx0tPzBzaYKErAAAAAPBUo2KDNWdqgp7+Ik0PfrpNT80a3uQT3nC/wrJqvfP9fn29I1smSReM6qXLT+4tG4sCt5mzoyLq/Hp0RFvYrF4aEGFvNF21YRgqLK+uT1Jk5JfpcGGFMvLLlZZVpIpqR7N1+vlYZLd6yWb1ktXLLB8vs7wtx/5rklTjMFTrMOQwDFVWO1RSWaPiyhqVVFSruvbYUIe88lzll8fJz9tfFrNDwbYKlRl7NLqfj357ynTF9bC5/XsaAGgrfmOinp+PRXeePVDjB4TpxeV79FLyXq3claM7zuqvqCA/t8RUUFalZ7/apc2HCtQ71F+PzByiyKDONWIDAAAAAE7EKfE99MC5g/T/lu7UnE+26okLhql/OFM2dRa1DkNfbM/Uf9YeUGllrYbHBOn3E/qRNDpBbRkVUceZ0RHOMplMCvb3UbC/T6O1PwzDUFlVrXJLqpRTWqnckirlllQqt7TqWGKholqllbUqrqhRQXm1qmscqqp1qLrWIcOQLGZT/cvqZZbNx0vhdqviw2wK9PWS1adSX+1fqlhrtWJD7PK3VstkkrJKspTjyJC/73iZzdwbAHgukhFoZEyfUL10ZaL++d1+fbk9S7fN36gLR0frkjGx8vPpmMWcDMPQyl1H9Y9V+1RUXqNzhkTopgnxnWYxKQAAAABoT+P6h+lhL7OeXrJDD366TQ9OH6xRscHuDqvbSzlcqNdW7lN6Tql6BPjotkn9dUb/MKbFaQdtHRVRpz1GRzjLZDLJ9r+RD84kowzDaNU58+7Wd2XJTtGg8OEymarrt7vjWAHAFbrektxoF/4+XrptUn8lXTRcvYL99MGPP+l3//5Rn285rKqa5ocktoeDuWX60+cpevarXZKkB6Ym6A9nDSARAQAAAKBLO6lvqB4/f5hMkv70eYqS0464O6RuK6+0SvO+2qk5H2/TofwyXXpSjF65cozGD+hJIqIdnMioiDq/HB2RWZzZzhG2r9Yc4/F+Jp50rABwPIyMwHENiw7SC5eN1lepWXp33SH949t9+njDT5o5IkpTh0XK7uvdbp+VUVCuj378ScvTsuUwpEkJ4brxjDgF+bXfZwAAAABAZzY8JkjPXDJSj32+XfO+3qX9uaW65rS+J7QwLlqvptahhVsP670fDqm8ulZj+oTopgnxig52z9TFXVVyerIyijMUGxirrJKsFstXlFbI19x4ymbDMJRRnNElRgy0NFKE0REAugKSEWiR2WzS1GFRmpQQri+3Z+njjRl65/sD+u/6QxrXr4fOHBSukTFB8rI4P9CmqsahDQfy9c2ObK3fnyfDOJYAueH0vo0WkQIAAACA7qB3D389+5tRSlqyQ59szNDeoyW695xBCvb3cXdoXdrGg/l6Y9U+HcorV0SgVfecM1Anx4UyEsIFskuzFRcc1+ryFpNFFlMTsyWYpLjgOGWXZrdjdB2vNSNFXLlOBgB0FJIRaDWrl0UXjIrW9OFRWr07R4u3ZWrFzqNasfOo/HwsGh0brCG9AtWvZ4BiQvwU5Ofd6JdoSWWNDuWVae/REm37qVCbDxWorKpWJpN0ct9QzRodraG9AvljDwAAAEC3Fmrz0VMXDtc/Vu3Tl9sz9Yf3NumuKQOV2DvE3aF1OT/ll+nN1en6cX++vC0mXXFKb12UGC2rF1MFu8qdp94pQ0ary+fl5Sk0NLTZ/SZ59ncIrV0/g9ERADwdyQg4zdti1qSEcE1KCNfhgnJ9u+uofjyQr+/35WrN3txflDMpwNdbPhaTahyGSitrVFH983oTZpPUr2eATu8fpgkDe6qn3eqOwwEAAACATsnHy6yLxwZoZ2GKDh0erMc+S9H04VG6blxf+fnwRfmJKq6o1n/WH9ayPYVyOAxNHNhT14zro3B74+mA0L5MJpNTCQSzySyzqWsue+rM+hmMjgDg6UhG4IT0CvbTZSf31mUn91ZJZY32HCnR3iMlyiqq0NHiSpVW1qjGYchkkvqE+ivI30exIX7q08OmwVH2dl1zAgAAAAC6muX7lutA+Realhik/RlDtWRbptbvz9NN4+N1ajxTCLVFrcPQF9szNX/tQRWUVmhwdLBuGh+vwVGB7g4N3VBrR0XUYXQEAE9GMgLtJsDqpVGxwRoVG+zuUAAAAADA49U9MW0xW7ThyHL9acpEbd7fU/9as19PL9mhYdGBuvGMePUPD3B3qB7B4TC0ek+O3v3hoDI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",
       "text/plain": [
        "<Figure size 1600x800 with 4 Axes>"
       ]
@@ -7506,7 +1067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 24,
    "id": "sought-sessions",
    "metadata": {
     "ExecuteTime": {
@@ -7514,15 +1075,7 @@
      "start_time": "2021-09-03T01:37:13.828642Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:root:Using 501 steps, 1000 samples, 0.2 learning rate and 100 particles for SVI.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ktrx_pos = KTR(\n",
     "    response_col='y',\n",
@@ -7555,7 +1108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 25,
    "id": "after-improvement",
    "metadata": {
     "ExecuteTime": {
@@ -7568,44 +1121,41 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:root:Guessed max_plate_nesting = 1\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial log joint probability = -1746.54\n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      19      -510.885     0.0488094       32.4314      0.2891      0.2891       23   \n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      39      -508.614   0.000254171       33.3664      0.2697      0.8316       50   \n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      59      -508.605   5.50184e-07       32.6815      0.3379           1       78   \n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      70      -508.605   3.22758e-08       33.2243      0.1599      0.1599       94   \n",
-      "Optimization terminated normally: \n",
-      "  Convergence detected: relative gradient magnitude is below tolerance\n",
-      "step    0 loss = 2305.2, scale = 0.10699\n",
-      "step   50 loss = 441.04, scale = 0.28282\n",
-      "step  100 loss = 403.93, scale = 0.44586\n",
-      "step  150 loss = 401.88, scale = 0.47063\n",
-      "step  200 loss = 402.26, scale = 0.45344\n",
-      "step  250 loss = 401.5, scale = 0.46129\n",
-      "step  300 loss = 401.04, scale = 0.46727\n",
-      "step  350 loss = 401.11, scale = 0.46234\n",
-      "step  400 loss = 401, scale = 0.45844\n",
-      "step  450 loss = 401.02, scale = 0.46248\n",
-      "step  500 loss = 400.94, scale = 0.45978\n"
+      "2024-01-21 17:24:22 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "INFO:orbit:Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 17:24:22 - orbit - INFO - Using SVI (Pyro) with steps: 501, samples: 1000, learning rate: 0.2, learning_rate_total_decay: 0.05 and particles: 100.\n",
+      "INFO:orbit:Using SVI (Pyro) with steps: 501, samples: 1000, learning rate: 0.2, learning_rate_total_decay: 0.05 and particles: 100.\n",
+      "2024-01-21 17:24:22 - orbit - INFO - step    0 loss = 2305.6, scale = 0.10649\n",
+      "INFO:orbit:step    0 loss = 2305.6, scale = 0.10649\n",
+      "2024-01-21 17:24:23 - orbit - INFO - step   50 loss = 442.26, scale = 0.27467\n",
+      "INFO:orbit:step   50 loss = 442.26, scale = 0.27467\n",
+      "2024-01-21 17:24:23 - orbit - INFO - step  100 loss = 404.1, scale = 0.44794\n",
+      "INFO:orbit:step  100 loss = 404.1, scale = 0.44794\n",
+      "2024-01-21 17:24:23 - orbit - INFO - step  150 loss = 402.01, scale = 0.46575\n",
+      "INFO:orbit:step  150 loss = 402.01, scale = 0.46575\n",
+      "2024-01-21 17:24:24 - orbit - INFO - step  200 loss = 402.14, scale = 0.45338\n",
+      "INFO:orbit:step  200 loss = 402.14, scale = 0.45338\n",
+      "2024-01-21 17:24:24 - orbit - INFO - step  250 loss = 401.45, scale = 0.4605\n",
+      "INFO:orbit:step  250 loss = 401.45, scale = 0.4605\n",
+      "2024-01-21 17:24:24 - orbit - INFO - step  300 loss = 401.14, scale = 0.46732\n",
+      "INFO:orbit:step  300 loss = 401.14, scale = 0.46732\n",
+      "2024-01-21 17:24:25 - orbit - INFO - step  350 loss = 401, scale = 0.46458\n",
+      "INFO:orbit:step  350 loss = 401, scale = 0.46458\n",
+      "2024-01-21 17:24:25 - orbit - INFO - step  400 loss = 401.22, scale = 0.45747\n",
+      "INFO:orbit:step  400 loss = 401.22, scale = 0.45747\n",
+      "2024-01-21 17:24:25 - orbit - INFO - step  450 loss = 401.2, scale = 0.46391\n",
+      "INFO:orbit:step  450 loss = 401.2, scale = 0.46391\n",
+      "2024-01-21 17:24:26 - orbit - INFO - step  500 loss = 401.11, scale = 0.4587\n",
+      "INFO:orbit:step  500 loss = 401.11, scale = 0.4587\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x153490f70>"
+       "<orbit.forecaster.svi.SVIForecaster at 0x2a31bffa0>"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7616,7 +1166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 26,
    "id": "434c702d-df65-4a22-928e-45757097d138",
    "metadata": {
     "ExecuteTime": {
@@ -7631,7 +1181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 27,
    "id": "09addef4-b5cd-4acf-bace-985ce4045bef",
    "metadata": {
     "ExecuteTime": {
@@ -7642,7 +1192,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -7665,7 +1215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 28,
    "id": "60661136-8f8b-445c-8c41-0a4124abddf9",
    "metadata": {
     "ExecuteTime": {
@@ -7676,7 +1226,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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rG6effrrWrl2rq666Si6XS/PmzVN2drZ+97vf6YsvvtCUKVMqvOf+++/XnDlz1KNHDy1dulRr167V4MGDtWTJEl166aU12RUAABAgd6nRX98t1q48nyYPcWpQ14YfjDhS+1S7/nJxjK4f4ZSM9MSHbj34rlt5h37N+DxWdkQZsiQAAAAAAJHEMoHMzIhalZeXp8aNG4e6GqgG2ip80Fbhg7YKH0e2lc9n9Og8t1Zt8Wj8qdG69MzoENcutA4VG72wxK3lWR7FOy1NGRKtk9od0v3fvSW7Zal5bKNjbiPXtV9eY3Rvz9+pRVzKcdWH8yo80E7hg7ZCoDhmECiOGQSC4wWB4phBfVSjDAkAABCZXv28VKu2eHRGZ4cuPiOyMiMqkxBj6aZRMZo6NkYO++Fsielv5yn3UMkxsyPKkCUBAAAAAIgUBCQAAEC1fL7Jo/e+KlGnFnbdMNIpm63yuREiUb8THfr7VXHq2dGj9dvt2rN6mPbnJVXrvcwlAQAAAACIFAQkAADAMf34i0///MitRvGWbj/fqWgHwYjfSoy1dFLPDWrS5StZ3hh9uaKrtv/QQtUZHJMsCQAAAABAJCAgAQAAjspVIj38frE8PqPbz4tR4wR+PlQmuyhfy3OzdGLbAvUfuF7xCS5tzmyrb7/upNJS+1HfS5YEAAAAACAScEcBAAAc1X9W2JS936crBjmV3vLoN9Yj2bKcTO0rOajUmGQlJLl0+sD1Smu5T3uyG2vVp91VVOg86vvJkgAAAAAANHQEJAAAQJU+2+jRF1ss9eng0KjejlBXp94qy45IdSbJsg4PZ+Vw+NSzzxald/tRhYUxWvVZNxXkx1e5DbIkAAAAAAANHXcWAABApXILfHrmE7cSY42uG+7032hHRctyMrXblaeWcY2V49pfbl3MCfvV1r5PW7/rrs8/66wOPdcrJfWXSrdjZLTbladlOZm6pMOAOqg5AAAAAAB1hwwJAABQgddn9PcFbrlKjH4/yKdG8QQjjmaPu0DtElIVZXPIYbNXeDVrsU9dT1kju8OnrWt7ae9PbSotF2VzqF1Cqva4C0K9SwAAAAAA1DoyJAAAQAVvryrV5t1ejekTpe6tS0JdnXrvpvRRMjLHLJdzik8PvutW9o9NNbR9lMb0iaq0nCUCQAAAAACAhocMCQAAUM7mXV69/UWJ2jWz6bIzo0NdnbBgWZZslu2YrxNSHPrLxXFq3cSml5aVauEab6XlGB4LAAAAANAQEZAAAAB+JR6jpxa5ZbNZumV0jKId3BivbY3iLd13UaxaNbHpxaVuLVxTGuoqAfXG8uXLdc455yg1NVWJiYnq37+/3nzzzYC2kZ2drf/7v/9TmzZtFB0draZNm+r888/XqlWrglRrAAAAANVFQAIAAPi9/UWpduX5dNHpUWrdlJ8JwXI4KBGjlo1ten6JWx9+S1ACmDNnjgYPHqxly5YpIyNDAwcO1Jo1azRhwgTde++91drGjh07lJGRoWeeeUYOh0NjxoxR27Zt9cEHH2jAgAF66623grwXAAAAAI6GOw0AAECStH2PV+99dXiopvP7VT63AWpPSrxNMy6K0QkpNj232K0l6wlKIHLl5ubq6quvVnx8vFatWqWPPvpICxYs0Nq1a9W8eXM98MADWrNmzTG3c/vttysnJ0fXXXedtmzZonfffVerV6/W888/L6/Xqz/84Q8qLi6ugz0CAAAAUBkCEgAAQF6f0VMfuSVJ149wymFnqKa6kJJg04wJMUpNtulfH7u1Zpsn1FUCQuKpp56Sy+XSDTfcoIyMDP/y9PR0zZw5U8YYPf7448fczqJFiyRJ9957r+x2u3/55MmT1alTJ+Xn52v9+vW1Xn8AAAAA1UNAAgAA6P2vS7V9j0/jTolWh+b2Y78BtaZxgk13j49RvNPSIx+49UOON9RVAurcggULJEljx46tsG7s2LGyLEvz588/5nZstsPdm59//rnc8tLSUhUUFEiSGjdufJy1BQAAAFBTBCQAAIhwu/J8enNlqVo2tunC0xmqKRRaNrZp2gUx8hnp/811K7/QF+oqAXXGGKOsrCxJUrdu3SqsT0lJUVpamvLz87Vr166jbmvkyJGSpEmTJumzzz5TUVGRtmzZoksuuUS5ubkaO3asOnbsWPs7AQAAAKBaCEgAABDBjDF6+mO3PF6j64Y7Fe1gqKZQ6XyCXdeeE619h3x65H23Sr0m1FUC6kR+fr6Ki4uVmJio+Pj4Ssu0aNFC0uG5Jo7mH//4hwYMGKCsrCwNHDhQ8fHx6tSpk959911Nnz5db7zxRq3XHwAAAED1EZAAACCCfZrlUdbPXg3tEaX0lgzVFGpndY3SuX2itGm3V88vLgl1dYA6UVhYKEmKi4urskxsbKwk6dChQ0fdVuPGjXXFFVcoJSVFHTp00Pnnn68ePXrIGKMXX3xRS5curfK9brdbBw4cKPdyu9012CMAAAAAVXGEugIAACA0DhUbvbS8REmxliYOjA51dfA/k86K1s69Pn3yXanap9o0vBfDaKFhK5t82rKOnaHl8x19OLPLLrtMr7/+uh544AFNnz7dv813331XF198scaOHavVq1fr5JNPrvDemTNnasaMGeWW3Xzzzfrzn/9c3V0BlJ+fH+oqIMxwzCAQHC8IFMcMAlUX860RkAAAIEK9saJEBUVG1w93KiGGoZrqC7vN0q3nxuiOV1x6folbrZvadHIrslfQcCUkJEiSXC5XlWXK1pWVrczHH3+s119/XYMHD9bdd99dbt0FF1yg2267TTNnztSjjz6q559/vsL7p02bpltvvbXcssLCQibBRsA4ZhAojhkEguMFgeKYQX3DkE0AAESgbblefbS2VJ1PsGtQV55PqG8SYixNHRejKLulx+e7ddDFfBJouBITE5WYmKiCgoIqgxLZ2dmSfp1LojJLliyRJJ1zzjmVrh81apQk6dtvv610vdPpVFJSUrmX0+ms9n4AAAAAODYCEgAARBifz+jZ/x6en+DqodGy2ciOqI/aNLXp92cfnuT6Xx+7ZQxBCTRMlmWpa9eukqSNGzdWWJ+Xl6ecnBylpKSoZcuWVW5n//79kiSHo/Iga9nykhLmZwEAAABChYAEAAARZskGj7ZkezWyd5TapzIUUH02qKtDZ3R26MstHn3ynSfU1QGCZuTIkZKkuXPnVlg3d+5cGWP8GQ5V6dKliyRp4cKFla7/5JNPJEm9evWqeUUBAAAAHBcCEgAARJCDLqP/fFqilHhLF5/BRNb1nWVZumaYU82SbHpxaYl+3nf0CX2BcDV58mTFxcVp1qxZWrlypX/55s2bNX36dEnS7bff7l+enZ2tTZs2+YdykqRLLrlEiYmJWrp0qf7f//t/5bKKPv74Y82cOVOWZenGG2+sgz0CAAAAUBkCEgAARJA5n5XoYLHRFYOcinMyVFM4SIixdPMopzxeo8fmF6vEw9BNaHhatWqlJ554QkVFRRo4cKCGDRumMWPGqFevXsrJydHMmTPVs2dPf/lp06apS5cumjZtmn9ZamqqXn31VcXExOjOO+/USSedpPHjx6tv374aPny4iouLNWvWLJ166qmh2EUAAAAAIiABAEDE2Lzbq0++K1W31nYNSGeopnDSpZVd40+L1o5ffJrzGePfo2GaMmWKPvzwQ5155platWqVPv/8c2VkZOidd97R1KlTq7WNMWPGaPXq1br88stVXFysefPmaceOHRozZoyWLFmiW265Jbg7AQAAAOCoKp/xDQAANCg+n9G//+uWw2bp6qFOWRbZEeHmd6dH6bsfvVqwulSnneRQl1YEldDwDB8+XMOHDz9mudmzZ2v27NmVrjv55JP10ksv1XLNAAAAANQGMiQAAIgAi9Z5tH2PT+f2jVKrJnz9hyO7zdL1I5xy2C39c5GboZsAAAAAAGGHOxIAADRw+YU+vfp5iZom2nThaVGhrg6OQ8vGNl3UP0q78316+4vSUFcHAAAAAICAEJAAAKCBe2V5iYrcRlcNiVZMNEM1hbvz+kapfapN731Vou17vKGuDgAAAAAA1UZAAgCABizzJ6+WZ3nUu71Dp57InAMNgcN+eOgmSXrqI7e8PoZuAgAAAACEBwISAAA0UB7v4Ymso+yWfn92NBNZNyDtU+0ad0q0tu/x6YNvGLoJAAAAABAeCEgAANBALVhTqp/2+TTu1CilNeIrv6G58PQonZBi0xsrSrU7zxfq6gAAAAAAcEzcnQAAoAHad9CnN1eWKq2RTeNOYSLrhijaYena4U6Veo2eX+KWMQzdBAAAAACo3whIIGS4cQIAwfP8khIVlxpNOTta0Q6GamqoTm5l11knO7R2h1dfbmGCawAAAABA/UZAAiGR49qvxzcuUI5rf6irAgANzlc/ePTlFo/6d3Yoo70j1NVBkF1+llNxTksvLi2Rm+kkAAAAAAD1GAEJhMTS7A36LHejlmZvCHVVAKBBcZUYPb+4RLHRliYPjg51dVAHGsVbunRAtPYe9Gn+t2TDAAAAAADqrxoHJJYvX65zzjlHqampSkxMVP/+/fXmm28GtI3vv/9eV111ldq0aaPo6Gg1btxYw4cP16JFiyotf/PNN8uyrCpfTz/9dE13B3Uouyhfy3OzZLdsWp6bpeyi/FBXCQAajDdWlGjvQZ8mnhmtlASeO4gU5/R0qF0zmz76zqZdTHANAAAAAKinajSOw5w5czRp0iQ5HA4NGTJEdrtdixcv1oQJE5SZmakZM2YccxsrVqzQ8OHDVVhYqJNOOkmjR4/Wrl279PHHH+vjjz/Www8/rNtuu63ce9asWSNJuuiiixQVVXGCzk6dOtVkd1DHluVkal/JQXVt1EqZBT9pWU6mLukwINTVAoCwty3XqwVrSnVSC7vO6clQTZHEbrN0zVCn7nylVM/9160//y5GlkW2BAAAAACgfgn4bkVubq6uvvpqxcfHa/ny5crIyJAkbdq0SYMGDdIDDzyg888/37+8Mh6PR5MmTVJhYaEeeugh3XHHHf5O8yeffKIxY8bozjvv1IgRI9StWzdJhydAXrdunZo2bao33nijJvuKeqAsOyLVmSSbZVOqM1nLc7M0KK2rWsSlhLp6ABC2fD6jZz4pkST9YVi0bDZuRkeazi3tGtDZpy+2evXF917170xQCgAAAABQvwQ8lsNTTz0ll8ulG264oVzQIT09XTNnzpQxRo8//vhRt7Fs2TJt375d/fr105133lnuCb5hw4bpmmuukc/nKxd42LJliw4ePKg+ffoEWmXUI2XZEakxyZKk1Jhk7Ss5oGU5mSGuGQCEtw/XevRDjlfn9o1S+1R7qKuDELnwFKN4p6WXlpXIXWpCXR0AAAAAAMoJOCCxYMECSdLYsWMrrBs7dqwsy9L8+fOPuo2DBw+qX79+GjlyZKXry4Ze2r17t3/Zt99+K0kEJMLYkdkRZUEoy7L8WRLMJQEANZOd79OcT0uUmmzTRaczkXUkS4qVLup/eILruV+Xhro6AAAAAACUE1BAwhijrKwsSfIPpXSklJQUpaWlKT8/X7t27apyO+PGjdNXX31V5VwTX331lSSpVatW/mVl80dERUXp0ksvVevWrRUbG6tevXrpH//4h3w+JnCs736bHVGGLAkAqDmfz+ifi9xye4yuH+FUTDRDNUW6Eb0cat3Epve+LNWeAn4fAQAAAADqj4ACEvn5+SouLlZiYqLi4+MrLdOiRQtJh+eaqIn169frtddek2VZuuCCC/zLywISM2bM0Oeff65+/fqpR48eysrK0k033aQLLrhAXq+3Rp+J4KssO6IMWRIAUHMLv/Uo62evRvaOUrfWDNUEyWG3dNXgaJV6jV5ZXhLq6gAAAAAA4BdQQKKwsFCSFBcXV2WZ2NhYSdKhQ4cCrsyePXs0fvx4eb1eXXnllerZs6d/XdmQTffcc4+2b9+ud999V19++aW++uortWnTRu+///5R565wu906cOBAuZfb7Q64jqiZqrIjypAlAQCB253n05zPStQ82aaJZzJUE37Vs51Dp5zo0MrvPVq/kwc2AAAAAAD1gyOQwnb74Scvf/uEe2UCHUJp9+7dGjZsmLZs2aK+ffvqySefLLd+27Zt+umnn9S1a9dyy3v16qUnnnhCY8eO1ZNPPqk//elPlW5/5syZFYaIuvPOO3XHHXcEVM/alJ8fGdkAOUX7tWjL10qSXS6rsMpyScUOLdrytXpGn6C0uEZ1V8FqiJS2aghoq/BBWx0fn5FmfWCTq9jSTcO8KjpUrKIgfRZtFT6ObKuxvaSvt9j19EcHdd8FPtkDnjkMwRKqc6px48Yh+VwAAAAAKBNQQCIhIUGS5HK5qixTtq6sbHVs2LBBY8aM0Y8//qh+/fpp0aJFFbIwkpKSKgQjyowaNUp2u107duzQvn371KRJkwplpk2bpltvvbXcMqfTKafTWe16BkMkdAwX7c9SYYxP3ZJbHzWY1SYpXhsKdmpdyW6d3KpDHdaweiKhrRoK2ip80FY1N++bUm3f59Z5p0Tp9K7B/y6jrcJHWVs1bixdcFqJ3vmyRKt/cmpE76gQ1wxH4pwCAAAAEIkCelYuMTFRiYmJKigoqDIokZ2dLenXuSSO5ZNPPtEZZ5yhH3/8UcOHD9eSJUuUkpISSLUUFRXl79QVFVX+fKjT6VRSUlK5V6iDEZHgaHNH/BZzSQBA9WzL9eo/n5WoRSObLmOoJhzFuFOj1CTBptdWlOigy4S6OgAAAACACBdQQMKyLH+WwsaNGyusz8vLU05OjlJSUtSyZctjbu/VV1/VqFGjdODAAU2ZMkXz58+vNLNi/fr1uuqqqypkOJQ5dOiQfvnlF0VFRSk1NTWQXUKQHWvuiN9iLgkAOLoit9Gj89ySkW4Z45Qz6tjDKCJyxUZbmnRWtA4VG72+ggmuAQAAAAChFfBowiNHjpQkzZ07t8K6uXPnyhijUaNGHXM78+bN0+WXXy6Px6P77rtPzz33nByOykeQiomJ0ezZs/Xkk0/ql19+qbD+pZdekiQNGjSIrId6JJDsiDJkSQBA1Ywxevpjt3L2+3T5oGidmGYPdZUQBgak25V+gl0fryvVjj1McA0AAAAACJ2AAxKTJ09WXFycZs2apZUrV/qXb968WdOnT5ck3X777f7l2dnZ2rRpk38oJ0nKzc3VlVdeKa/Xq7vvvlv33nvvUT/zpJNO0pAhQ1RaWqorr7xShw4d8q/76quvdM8998iyLN1zzz2B7g6CKNDsiDJkSQBA5T75zqMVmz069SSHRvUOaBooRDDLsjTl7GgZI72wtETGMHQTAAAAACA0Ar6b0apVKz3xxBO6+uqrNXDgQA0ePFhOp1OLFy9WcXGxZs6cqZ49e/rLT5s2TS+99JKuuOIKzZ49W5I0a9Ys5eXlyeFwaOvWrZo4cWKln3XGGWfo2muvlSS98MILOvPMM7Vw4UJ17NhRp512mg4cOKDPPvtMPp9Pjz32mM4888wa/AkQDDXJjihzZJbEoLSuahEX2JwiANAQ7djj1QtLSpSabNN1w50BX1sR2To0t2tojyh98l2pVn3v1emdCWgBAAAAAOpejXqjU6ZMUatWrfTQQw9p1apVstvtysjI0J/+9CddcMEFx3z/hx9+KEnyeDx67bXXjlq2LCDRtm1brVmzRg8++KDef/99ffjhh0pMTNSIESN0++2366yzzqrJriBIyrIjuiW3rtH7U2OStaFgp5blZOqSDgNquXYAEF7K5o3wGaM/jo5RQgzBCATukgHRWrHZo9nLSpTRwc78IwAAAACAOmcZ8vZDJi8vT40bNw51NWpddlG+Zqx7S3bLUvPYRjXeTq5rv7zG6N6evwt5lkRDbauGiLYKH7RV9fh8Rg/NdWv1No+uHOTUuX2j6rwOtFX4OFZbzfumVLOXuXVR/2hN6B9dhzXDkTinwgdthUBxzCBQHDMIBMcLAsUxg/qIfH3UumU5mdrtylPLuMbKce2v8XaMjHa78siSABDR5nxeqtXbPDrrZIfG9OFrG8dnZG+H/ru+VHO/KtWQbg41Swp4OjEAAAAAAGqMXihq3R53gdolpCrK5pDDZq/xK8rmULuEVO1xF4R6lwAgJJasL9Xcr0rUqYVd/3cO80bg+DnsliYPjlaJx+jl5SWhrg4AAAAAIMLwqCVq3U3po2RUeyOBWeIGHIDIs3qrR//62K3UZJvuGOtUtINrIWpHz3YO9evo0MrNHg3v5VW31vZQVwkAAAAAECHIkECtsyxLNstWay+eCAYQaTbv9urReW7FOy3dPT5GKfF8XaN2XTk4Wg67pReWuOX1MZ0YAAAAAKBucIcDAIB6ZGuOV395p1iSNG1cjFo25qsatS+tkU3n943Sj7/49Ml3nlBXBwAAAAAQIbjLAQBAPbF9j1f3v12sUs/hYETnlgylg+AZd2qUGidYeu3zEh0qJksCAAAAABB8BCQAACFhDDdAj7R5l1f3vlGs4lLpjrFOdW9LMALBFRttadLAaB0qNnp9BRNcAwAAAACCj4AEAKDO5bj261+bP1aOa3+oq1IvrNvh0f1vF8vjlaZfEKOM9o5QVwkR4swuDnU+wa5Fa0v14y++UFcHAAAAANDAEZAAANS5pdkbtOqX77U0e0OoqxJyn6wr1V/fLZbdJt37uxj1IDMCdciyLE0ZEi1jpBeWuMlcAgAAAAAEFQEJAECdyi7K1/LcLNktm5bnZim7KD/UVQoJj9do9lK3nv7ErSaJNj14aSxzRiAkOqbZdXb3KG34yasvt3hDXR0AAAAAQANGQAIAUKeW5WRqX8lBpSefoH0lB7QsJzPUVapz+w76dN+bxZq3ulTpJ9j10GWxatWEr2SEziUDohXntDR7WYlKPGRJAAAAAACCg7sfAIA6U5YdkepMkmXZlOpMjrgsiS82e3Tbyy5t3OXVmD5RmjEhRslxVqirhQjXKN7SRadH65cDPr2zqjTU1QEAAAAANFAEJAAAdaYsOyI1JlmSlBqTHDFZEvsLjR6bX6xH5hXLGOn282N01WCnHHaCEagfRvZ2qF0zm977qkTb9zB0E0Jj+fLlOuecc5SamqrExET1799fb775ZsDbmTNnjs4880wlJycrNjZWGRkZevbZZ5knBQAAAAgxR6grgMjm8Rr9tM+n3AKj/ENGdpsU7ZBapNjUpqlNsdHcqAMaivLZEYfPbcuy/FkSg9K6qkVcSohrWfs8XqOP13n02ooSFbmN+nZ06NpznGoUz/UN9YvDbun6EU7d+R+XnvrIrYcuiyVghjo1Z84cTZo0SQ6HQ0OGDJHdbtfixYs1YcIEZWZmasaMGdXazuTJk/Xiiy8qJiZGQ4YMUXFxsT7//HP94Q9/0A8//KD/9//+X5D3BAAAAEBVCEigzvl8Rl9t9erzjR6t+9GrInflT6pZkk5qYdfpnewa0MWhxgkk9ADhrCw7olty63LLU2OStaFgp5blZOqSDgNCVLva5/MZrfzeq9c/L1H2fp+aJtp0/QinTulok83GTV7UTx2a2zXulGi982WJ3v+6VONPiw51lRoUY4w/IIvycnNzdfXVVys+Pl7Lly9XRkaGJGnTpk0aNGiQHnjgAZ1//vn+5VV5+eWX9eKLL6pz585atGiR2rZtK0nKzMzUwIED9fDDD+vSSy9Vr169gr1LAAAAACpBQAJ1xuczWpbp0Ttflipnv0+WpBNb2NWjjV0nNLbUJMEmI6moxGjXPp+25fq07kevvl/u1ZzPSzXoZIfGnhKlFikEJoBwU1l2RJmGliVR4jH6NMuj978u1e58n2KjLV18RrTO7ROl/d4C/X3T57qk/QClxTYKdVWBSl14epRWbfHorS9KdepJDiZcryU5rv16bfvh858wT0VPPfWUXC6Xpk6dWi7okJ6erpkzZ2ry5Ml6/PHH9fLLLx91Ow888IDsdrveeOMNfzBCkrp27arbbrtN//rXv/TNN98QkAAAAABChIAE6sSOPV49+98Sbd7tVWKMpQtPi9aIXg6lVJX1cNLh/5R4jNZs8+qDb0r13/WlWpbl0fl9ozT+tCg5o3jCEAgXVWVHlAn3LAljjLbm+rQ8y6PlmR4Vuo3inZYuODVa5/WNUmLs4evV0m0b9FnuRqXFNArL/URkiHZYun64U9Nfc+mfi9z6y8UxZPXUgqXZv57/wxudHOrq1DsLFiyQJI0dO7bCurFjx2rKlCmaP3/+Ubexbt06/fDDDxo2bJh69uxZYf20adM0bdq0WqkvAAAAgJohIIGgMsbok+88en5JiXw+o9EZUbr4jGjFOat3YyPaYem0Tg6depJdmT/79MISt975skSfb/LoltFOdTrBHuQ9AHC8jpYdUSYcsyS8PqOsn3z68gePvtri1b5DPklS6yY2TTgjWkO6OcrNg1P2d7BbtrDaT0Smzi3tGt0nSvNXl2rBGo/O7RsV6iqFtd+e/z2jT1Djxo1DXa16wxijrKwsSVK3bt0qrE9JSVFaWpqys7O1a9cutWzZstLtrF69WpJ0yimnyBijRYsW6ZNPPtGBAwfUvXt3TZo0SSkpXHcBAACAUCIggaAp9Ro9+0mJlmwoVWqyTX86N0YnptUsgGBZlrq1tuvhSbFauMajOZ+V6O7XXbp0QLTO7xfFeMxAPXas7Igy9T1L4lCx0ffZXm3e5dP3u73akuOTq+TwHDjNk206r2+UTjvJoU4n2Cq9JpX9Hbo2aqXMgp/q7X4CZS45I1rfbPVqzmcl6tnOrjZNGbqppn57/q/Ys0knt+oQ6mrVG/n5+SouLlZiYqLi4+MrLdOiRQtlZ2crNze3yoDEDz/8IElKSkrSiBEj9PHHH5db/5e//EVz585V//79a3cHAAAAAFQbAQkERYnH6OH33Vqz3aOM9g7dPNqphJjjDxrYbZbO7Rul7m1semyBW698WqKde336v3OcinYQlADqm+pkR5QJdZaEz2dUUGS075BR3iGjXw4cns/m5zyfduX5tL/Q+MtGOyydmGZTtzZ2nXqiXW2bVR6EKHPk38Fm2cIuGwSRKSba0k2jnLr7NZcen1+shybG8l1bA5Wd/yt+2awxRadz/v9PYWGhJCkuLq7KMrGxsZKkQ4cOVVmmoKBAkjRz5kzZ7XbNmTNHI0aMUH5+vh5++GE988wzOu+887R+/Xq1aNGiwvvdbrfcbneFZQAAAABqDwEJ1LriEqO/vlusrJ+9GtItSv93TrTstTz2dLtUu/52WaweX+DW8iyP9hQYTR0XUytBDwC1p7rZEWWClSXh8xnlFxrtPWD0y0GjvQd8hwMPB43/v/mFPvlMxffGRltq2dimnm0tnZhmV6cTbGrXzCaHvfrXm9/+Hep7NghQpvMJdl3UP1qvryjRK5+WaMoQZ6irFHYqO//X/JLN+X8Eu/1wBm11Ml59Pl+V68qCB/v379fSpUs1aNAgSVLjxo319NNPa/fu3Zo3b56eeOIJzZw5s8L7Z86cqRkzZpRbdvPNN+vPf/5zdXcFUH5+fqirgDDDMYNAcLwgUBwzCFRdDC1LQAK1yuM1emSeW1k/ezUqI0pXDYoO2kSYMdGW7jjfqZeXW5q3ulT3vuHSPRfGqlE8QQmgPggkO6LM8WZJHCgy+nGvTz/t9WnnXp9+3ufTLweM8g5VHmywJDWKt9Qk0aYOzR1qkmipcYKlJomWmiTY1LKJpZR467iGhavs7xDqbBAgEONPjdK6HV4tXFOqrq3sOq0TPx+rq6rzv2l0Euf/ERISEiRJLperyjJl68rKVqYsw6Jr167+YMSRrr32Ws2bN09Lliyp9P3Tpk3TrbfeWm5ZYWEh830gYBwzCBTHDALB8YJAccygvqFHiVrj8xn9c5Fb3273aEi3KE0eHB30uR1sNktXDnYqMdbSq5+X6M9vuDTjohilJDDONRBqgWZHlAkkeyDvkE8bdnq14Sef1u/0ak9B+SdnE2IspSbZ1D7VoWZJlpomWWqaaFPTxMNBh0bxVkCZDjVR1d+BLAmEC5vN0h/HOHX7yy49+ZFbbZvZ1CKF79nqqOr8bxqTpG0leZz//5OYmKjExEQVFBTI5XL5h2c6UnZ2tiRVOtRSmWbNmkmS2rdvX+n6du3aSZL27t1b6Xqn0ymns3wWkMfjOWb9AQAAAFQfAQnUmrdXlWp5lkd9Ozr0f+cEPxhxpPGnRSvaYWn2MrdmvFWsBy6OVWIsmRJAqNQkO6LMsbIHft7n04rNHn2x2aOf9v0agEhrZNOgrg61a2ZXm6aW2jSzqVHc8WU3HK+j/R3IkkA4aZJo0y1jYnT/Wy498kGx/npJrGKi+Z49mmOe/9Gc/2Usy1LXrl21atUqbdy4URkZGeXW5+XlKScnRykpKVVOaC1J3bt3lyTt2rWr0vU5OTmSpNTU1FqqOQAAAIBA8XgbasXqrR69ubJE7ZrZdOsYZ63PGVEd5/aN0hVnReunfT795Z1iFbkrGZ8FQJ0oeyo4NSa5Ru9PjUnWvpIDWpaTKUkq8Rgt3VCqu1516eYXi/TmyhIVFBmddbJDN4xw6ulr4vTU7+N048gYnds3Sj3bOZQSf/SJpuvCsf4Ov91PoD7r0daui8+I1o5ffHrqI7eM4Xv2aDj/AzNy5EhJ0ty5cyusmzt3rowxGjVq1FG3MWTIEMXExGjt2rXatGlThfUffvihJGngwIHHX2EAAAAANUJAAsctO9+nxxe6FR9j6Y6xMXJGhe4G4Hn9ovW706L1Q45Xj3xQLG9lg8YDCKrjyY4oU5Y9sGTXZr28Yr+uf65IT37k1rZcn8462aF7fxer566N002jYjS4W5SaJdW/r7Pq/B2OzJLILmKyMdR/40+LUv9ODq383qO3vigNdXXqLc7/wE2ePFlxcXGaNWuWVq5c6V++efNmTZ8+XZJ0++23+5dnZ2dr06ZN/qGcJCkpKUnXXHONjDGaOHGi9uzZ41/38ccf64knnlBsbKyuueaaOtgjAAAAAJVhyCYcF4/X6PEFbrncRtPHx6h5cuhvCk44I0r7i4w++a5Uzy0u0TVD63b4KCDSLcvJ1G5XnlrGNVaOa3+V5YpdhYqJrnxsbuOz9MuuE7Tt+7b6QcXq2DhKkwZGa2iPKCXEhMf5XN05NJhLAuHEsizdMNKpnP1Gb6wsUfNkS2d1jQp1teqdqs5/Y6Qjf5Jw/v+qVatWeuKJJ3T11Vdr4MCBGjx4sJxOpxYvXqzi4mLNnDlTPXv29JefNm2aXnrpJV1xxRWaPXu2f/mDDz6otWvX6tNPP1XHjh01aNAg7du3T19++aUsy9Kzzz6rjh07hmAPAQAAAEgEJHCc3v6iVD/keDX2lGj1bl8/DifLsvT7s6OVu9+nj9eV6oQUm87ty80SoK7scReoXcKxx+d22Gxy2OwVlhcWJGrbhnQVHkhUTHSpunX7UY+OPEXRjvAIREiBZYkwlwTCjTPK0tRxTt31arGeWuRWYpyljHryG6A+KDv/m0Un6dDBOBXsT9CB/Qk6UBCngwfiNOicNf6ynP/lTZkyRa1atdJDDz2kVatWyW63KyMjQ3/60590wQUXVGsb8fHx+u9//6unnnpKL730khYvXqzY2FgNHz5cU6dOZbgmAAAAIMQswwDAIZOXl6fGjRuHuho1tnmXV3e/7lLbZjbNvCxWUfb6dbPwULHRtDkuZef7NHVcjPp2rPnNknBvq0hCW4WeMUZGx/5q+W1bebxGb64s1ftfH86aGNPHoQtPj1JcdOjnggjUa9s+17s/faluya2rVXdjjDYU7NQFrU+rl09Jc16Fj7psq5/3+TT9NZc8XunPF8aoc8uKAcZI4iox2rTLq5fXbtGqHQfkcDWX1/Pr3yQq2qPEpCJ1671VKt2nuOQESfX//I90XP8QKI4ZBIpjBoHgeEGgOGZQH/E4G2qkxGP05Edu2W2WbhkdU++CEZKUEGNp+vgYTf2PS4/Nd+svl1hqnxrZN0uAumBZliwd+5pgs2yyWYeHecst8Omx+SXaku1Vx+Z2XTs8OmzP15rMocFT0ghHrZrYdNcFMbr/rWI98E6x7hkfWUEJr89o826f1m73av1Or7bmelXs8Wp3UawcjiglNy5UcsohJTc6/IqJLfEP11RU8Ot2OP8BAAAARBICEqiRt78o1e58nyYOjFarJqGfN6IqaY1sumNsjGa8WayH3nPrkctjlRhb/4InQCT77kevHp1XrEPFRuf3i9IlA6LrZZCzuqo7d8RvMZY8wlHnE+y6e3yM/vLO4aDEtHEx6tq64QYlDhUbff2DR2u2e7Vuh1eF7sPZYHFOS73bO3TQuUPyrVJGyyTZbNW/jnH+AwAAAIgU9fdOMuqtH3/xae7XJWrXzKZz+9T/uRlObmXX1UOjtfegT48vKJbPxyhlQH2xaG2pHnjbJa9Pmj4+Rpef5QzrYERNsiPKHPmUdHZRfpBqCNS+Lq3smj4+RpL0wNvFWvV95ZPVhytXidGnWaV68N1iTf5noZ78yK0vNnvUIsWmi/pH66HLYvXS9XG6crhLeSkr1bqpCSgYIXH+AwAAAIgcZEggID6f0TOfuGWMdO1wpxxhcuNwaI8obd7t05INpXprVakm9I8OdZWAiGaM0furLc1f51ZaI5umjYup19lW1VXT7IgyPCWNcHVyK7vun3A4U+KRD4p1xSCnxvRxhN38L2VKPEbfbvfq800efbPVqxKPkc2SerS164zODmV0cKhRfPl94/wHAAAAgGMjIIGAfLrRo827vRrVO0onpoXXkAxXD43Wtlyv3lpZok4tbOrdnsMfCAVjjF5aVqL3V9t04gk23XNhrJLjwvOm5ZGOJzuiDGPJI5x1aG7Xg5fG6q/vFGv2Mre27/HqD8OcckaFx/nt9Rmt3+nV5xu9+vIHj4r+NxxTl5Z2DUh36LROFYMQZTj/AQAAAKB6uCOLaityG73yaYmSYi1NOCP8MgyiHZbuOD9Gt7/i0uML3Hp4kk2pyeH/RDYQTow5fB2Zt7pUJ6UZ3TchVvHO8LhZeSzLcjK125WnlnGNlePaX+PtGBntduXxlDTCUlojmx6aGKsnFrq1PMujbbk+3TzaWW8nqff9b2LqFZs9WrnZo4Kiw0GIDql2DehyOBuiadKxfysEev4XuwoVE11xaCvOfwAAAAANHQEJVNvbq0q0v9Do/4Y5lRATnjcQmzey6caRTj00t1iPznPrgYtjFO0Iz30BwtG7X5bq/a9L1fkEu244u6TBBCMkaY+7QO0SUmtlW+0SUrXHXVAr2wLqWrzT0p3nO/XeVza9sbJEU/9TrAtPj9L5/aLqxXeuMUbb9/j0+SaPVmzyau9BnyTphBSbhveK0oB0h1o2DuyBhUDPf4fNJoet8iAN5z8AAACAhoyABKolt8CnBas96pBq19ndw/uw6XeiQxecGq13vyzRK5+WaMoQZ6irBESEJetL9ernJWrXzKa7LohRSZEr1FWqVTelj5KRqbXtWQr9jVugpmw2S+NPi1avdnY98aFbr68o0fJMj64YFK2+He0hmVvi532HMyE+3+jR7vzDQYimiTad3y9KZ3ZxqF0zW43rFej5n5eXp8aNG1e5nvMfAAAAQENV4/Fqli9frnPOOUepqalKTExU//799eabbwa0je+//15XXXWV2rRpo+joaDVu3FjDhw/XokWLKi3v8/n0wgsvqG/fvkpKSlLTpk01duxYrV69uqa7gWp69bMSeXxGlw+Kls0W/p3ki8+I0smt7Fq4plSrvq84ZAKA2rV+p1dPf+JWavLhOSPCNcvqaCzLks2y1dorXCcDBo7UMc2uhyfFauLAaOUXGj00t1hT5xTrm60e+Xy1F8CrjDFG23K9evWzEt30QpFufrFIb64sUaHbaGTvKP31klj96+pYXX7W4SGljuec4/wHAAAAgOqp0aPuc+bM0aRJk+RwODRkyBDZ7XYtXrxYEyZMUGZmpmbMmHHMbaxYsULDhw9XYWGhTjrpJI0ePVq7du3Sxx9/rI8//lgPP/ywbrvttnLvufbaa/Xss88qJSVFQ4cOVW5urt5//30tXLhQ8+fP1znnnFOT3cEx/JDj1eebPOrTwaHubernGNCBstss/XGMU7e95NKTH7nVPtWm5o2YTwIIht15Pj3yQbGiHZbuGhdT5aSwABqmaIelcadEa2AXh977qlSffOfRzPeKlZps07AeDp3R2VFr38H7Cw9PTP3dj16t2+HVvkOHMyGS4ywN6xGl0zvZ1a2NXfYG8HAFAAAAAIQjyxgT0ONpubm5at++vex2u5YvX66MjAxJ0qZNmzRo0CDt2bNH33zzjX95ZTwejzp16qTt27froYce0h133OF/EuyTTz7RmDFj5PF4tG7dOnXr1k2S9MEHH+j8889X9+7dtXTpUjVp0kSS9M4772jChAlKTU3VDz/8oLi4uBr9IULhWOn69YExRve9Waysn7169Io4tWnasG7ar93h0V/eLlaH5nb95ZKq55MIh7bCYbRV/eIuNZr2qks/7fVp2gUxymj/axyctgoftFX4CIe22nfQp0VrPfrv+lL/JNJtm9nUvY1dXVra1T7VpmZJ1lEzMo0xOuCSfvzFpx17vNrxi0/bcn36aZ/PX+aEFJt6t7frtE4OpZ9gq1cZnuHQTjiMtkKgOGYQKI4ZBILjBYHimEF9FHCGxFNPPSWXy6WpU6eWCzqkp6dr5syZmjx5sh5//HG9/PLLVW5j2bJl2r59u/r166c777yz3Lphw4bpmmuu0ZNPPqk33njDH5B45JFHJEkPP/ywPxghSePHj9dll12ml19+Wa+//romT54c6C7hKNbv9GnDT14N6RbV4IIRktSrnUPjT4vW26tK9PLyEv3+bOaTAGrTc4tL9OMvPl1yRnS5YASAyNUk0aZLz4zW7/pHae12r77c4tU3Wz2av7pU81eXSpKi7JYaJ1hKirMUGyXZbJLPJ7lKpcJio30Hjdye8s/UNE206ayTD2dz9mhrV5PEhve7BQAAAADCXcB3hxYsWCBJGjt2bIV1Y8eO1ZQpUzR//vyjbuPgwYPq16+fRo4cWen6Tp06SZJ2794tSSooKNCKFSuUkJCgs88+u0L5cePG6eWXX9b8+fMJSNQiY4xeX1Eiu0268PSoUFcnaC7qH6Wsn7368NtSdW1l1+mduWkK1IalG0q1ZEOperd36IJTG+41BED1GWP8WbFRdkv9TnSo34kO+XzR2p1vtGmXVz/t82lXnlH+IZ/2HfSpuFQqy+eNi7YU55TSW9nULNGmNs1satfMprbNbA1ybhoAAAAAaGgCuvNqjFFWVpYk+TMXjpSSkqK0tDRlZ2dr165datmyZaXbGTdunMaNG1fl53z11VeSpFatWkmSNm7cKJ/Pp/T0dDkcFat88sknS5LWr18fyO7gGL770avNu70a1iNKzZMb7lOGR84n8dQit9o3tymN+SSA45Jb4NPzS0rUOMHSTSOd9WqoFAChkePar9e2f65L2g9QWmyjcutsNkutmlhq1YTvXwAAAABoyALq9eXn56u4uFiJiYmKj4+vtEyLFi0kHZ5roibWr1+v1157TZZl6YILLpD0a6ZE2bZr+zNRkTFGb6wslcNmafxpDf/J5sYJNt082qniEqNHP3CrxBPQ1CoAjuDzGT35oVuuEqPrRziVFEcwAoC0NHuDPsvdqKXZG0JdFQAAAABAiAQUkCgsLJSko04cHRsbK0k6dOhQwJXZs2ePxo8fL6/XqyuvvFI9e/as1ueWfWZZucq43W4dOHCg3Mvtdgdcx0ixdsfh7Igh3R1qlhQZTyv2bOfQhadFa9ser15eXhLq6gBha97qUmX97NXI3lHq1Y4h0ABI2UX5Wp6bJbtl0/LcLGUX5Ye6SgAAAACAEAjoTpHdbpck/9i/R+Pz+QKqyO7duzVs2DBt2bJFffv21ZNPPhnw5/p8vnJjEx9p5syZmjFjRrlld955p+64446A6lmb8vPrZ2fcGOmVpTbJZ2lI5xLl5VUd6GlohnaRvt1q0/yvvWqdXKR+HQ5nStTXtkJFtFVo7Tkg/We5XU3jjUZ3P/r1g7YKH7RV+KivbTV/xypl781VetIJ2rR3l+Zv+kLj250W6mqFTKjaqXHjxiH5XAAAAAAoE1BAIiEhQZLkcrmqLFO2rqxsdWzYsEFjxozRjz/+qH79+mnRokXlsiGO9blly+Pj46sMWkybNk233npruWVOp1NOp7Pa9QyG+tgxXLPdox/zijW8d5ROahPav08oTB3v059edumVlVKPjrFqkXI4Q6Q+thUqR1uFhjFG//hvsXzy6sYxsWqRaj/me2ir8EFbhY/61lbZRfla7f5ZrZqlKT42Sa2cPq12/6wxMZZaxKWEunohU9/aCQAAAADqQkBj8SQmJioxMVEFBQVVBgeys7MlVT3fw2998sknOuOMM/Tjjz9q+PDhWrJkiVJSyndOyybHzsnJqfFnOp1OJSUllXuFOhhRHxlj9MaKUjnskTF3RGVSEmy6ZXTM4fkk5hUznwRQTZ9mefTdTq+GdItSt9bHDkYAiAzLcjK1r+SgUmOSJUmpMcnaV3JAy3IyQ1wzAAAAAEBdCyggYVmWunbtKknauHFjhfV5eXnKyclRSkqKP4hwNK+++qpGjRqlAwcOaMqUKZo/f36lmRVdunSRzWbTpk2bKh0KKjPzcIe2e/fugewOKvHtDq9+yPFqWA+HmiRGxtwRlenR1q7f9Y/W9j0+vbSM+SSAYzlUbPTSshIlx1q6YlB0qKsDoJ4omzsi1Znkz2K1LEupzmTmkgAAAACACBTwHeeRI0dKkubOnVth3dy5c2WM0ahRo465nXnz5unyyy+Xx+PRfffdp+eee04OR+UjSMXFxemss85SQUGBli1bVmH9e++9J0nV+lwc3dyvSmW3SWP7RWZ2xJF+d9rhp7w/WluqL7cee94UIJK99UWJClxGEwdGKyGG8wXAYb/NjihDlgQAAAAARKaAAxKTJ09WXFycZs2apZUrV/qXb968WdOnT5ck3X777f7l2dnZ2rRpk39YJUnKzc3VlVdeKa/Xq7vvvlv33nvvMT/3xhtvlCTdcMMN5YZuevfdd/Xqq6+qRYsWmjRpUqC7gyNsyfYq8yevBqQ71DQpcrMjythslm4Z41RynKXZn9q0Oy+widqBSPHzPp8+/LZUJ6bZNahrQFMTAWjAKsuOKEOWBAAAAABEpoDvHLVq1UpPPPGErr76ag0cOFCDBw+W0+nU4sWLVVxcrJkzZ6pnz57+8tOmTdNLL72kK664QrNnz5YkzZo1S3l5eXI4HNq6dasmTpxY6WedccYZuvbaayVJ48aN06RJk/TKK6+oc+fOGjJkiPbu3asVK1YoOjpar776KnNCHKf3vy6VJJ3fj+FWyqTE2/TH0TG65/VDenResWZeFqtoB09/A0d6calbXp80eUi0bDbODwCHlWVHdEtuXen61JhkbSjYqWU5mbqkw4A6rh0AAAAAIBRq9CjrlClT1KpVKz300ENatWqV7Ha7MjIy9Kc//UkXXHDBMd//4YcfSpI8Ho9ee+21o5YtC0hI0uzZs3XKKafo3//+tz766CM1atRI5513nu677z716tWrJruC/8nO9+nLLR71bu9Q22ZkRxype1u7zsvwacE6n15YUqL/O4fAF1Bm3Q6P1u7w6qyTHep8AhNZAzjsaNkRZY7MkhiU1lUt4lLquJYAAAAAgLpW47E1hg8fruHDhx+z3OzZs/2ZEWW+++67Gn2mzWbTDTfcoBtuuKFG70fV5n1TKp+Rxp7C3BGVOS/D6Kd8uz75rlQdmtt0Tk/+ToDPZ/TKpyVy2C1dfAaZVQB+dazsiDJkSQAAAABAZOFReGh/odGSDR6dmGZX11YcEpWxWdKt58YoNdmm5xa7lfmTN9RVAkJuxWavtu/xaVRvh1KTuXYAOKw62RFlmEsCAAAAACILd5Cgj74tVanXaGy/qGPeOIhkibGWpo2LUbTD0iMfFCu3gEmuEblKvUavfl6iOKelC04lOwLAr8qyI1JjkqtVPjUmWftKDmhZTmaQawYAAAAACDUCEhHOVWL04dpSpTWy6dSTGP/9WNo0tenmUU4ddBn9bW6xXCUm1FUCQmLpBo/2FPg0tl+UEmMJZAI4LJDsiDJkSQAAAABA5CAgEeGWbPDoULHRuX2jZLNxU7E6+p3o0CUDovXjLz7940O3fD6CEogsJR6jd1aVKinW0qgM5lMB8KtAsyPKkCUBAAAAAJGBgEQE8/qM5n1TquRYS0O61Xh+84h0walRGpDu0JdbPHp5eUmoqwPUqSUbPNp70Kfz+0UpNppAJoDDapIdUYYsCQAAAACIDAQkItjKzV79csCnkRlRinZwUzEQlmXp+hFOdWlp17zVpZr3TWmoqwTUiRKP0burDgcyR/QiOwLAr2qaHVGGLAkAAAAAaPgISEQoY4zmflUip4ObijUV7bA0dVyMWjWxafYytz7f5Al1lYCgW5bp0b5DPp1/SpRiyI4A8D/Hkx1RhiwJAAAAAGj4GKcnQn33o1c7fvFpVAYT0h6PhBhLd4+P0fRXi/WPhW4lx1rq3pbJwdEweX1Gc78qVUKMpXN6EsgE8KtlOZna7cpTy7jGynHtr/F2jIx2u/K0LCdTl3QYUHsVBAAAAADUC2RIRKi5X5XKZklj+nBT8Xg1S7Jp+vgYRUdJf3u/WNv3eENdJSAoVm72KrfgcCCTuSMAHGmPu0DtElIVZXPIYbPX+BVlc6hdQqr2uAtCvUsAAAAAgCAgQyICbcv16rudXp3ZxaHmycSkakPbZjZNHRujB94u1v1vFWvGhFi1acrfFg2Hz2f07pcliomyNDqDQCaA8m5KHyUjU2vbs0TQEwAAAAAaIu6YRqD3vz48AfP5/bipWJu6trbrjvOdKnJL973h0k97faGuElBr1mz3auden4b3dCghhhuFAMqzLEs2y1Zrr5rOQwEAAAAAqN8ISESY3AKfVm72qGdbu9qnMtdBbcvo4NBt5zl1yG1075su7cojKIGGYd43pbLbpNEM8wYAAAAAAIAaIiARYeZ/UyqfkcaeEh3qqjRY/U506NYxMTroMrr3DZd2E5RAmNu+x6sNP3l1RmeHmiTytQEAAAAAAICa4c5SBDlQZPTf9R61T7WpexuaPphO6+TQzaNjVFBk9Oc3XNpZyfBNxtTeWNtAMM1ffXiYtzFkRwAAAAAAAOA4cFc6gny0tlQlHqOxp0QzNnMdGJDu0B/HxOigS7rndZc27/b61+W49uvxjQuU49ofugoC1ZBf6NPnm7w6uZVdHdMY5g0AEFzLly/XOeeco9TUVCUmJqp///568803j2ubr776qizL0sSJE2uplgAAAABqioBEhHCXGn34balSk206vRM3FetK/84OTR3nVKlHuv+tYq3b4ZEkLc3eoM9yN2pp9oYQ1xA4ukXfeuTxGuaOAAAE3Zw5czR48GAtW7ZMGRkZGjhwoNasWaMJEybo3nvvrdE2f/rpJ11//fW1XFMAAAAANUVAIkIs3eDRAZfRuX2iZLeRHVGXerd36N7fxchukx5816353+VreW6W7JZNy3OzlF2UH+oqApUq8RgtWleq5sk2ndKRQCYAIHhyc3N19dVXKz4+XqtWrdJHH32kBQsWaO3atWrevLkeeOABrVmzJqBtGmN0xRVXaP/+/cGpNAAAAICAEZCIAF6f0QfflCohxtKQbo5QVycidW5p1/0TYhQfI82a59bWrak6ObmV9pUc0LKczFBXD6jUp1mHA5mjMqJkI5AJAAiip556Si6XSzfccIMyMjL8y9PT0zVz5kwZY/T4448HtM1Zs2Zp6dKlGjhwYC3XFgAAAEBNEZCIAF9871VugU8je0cpJpqbiqHSLtWuW8aVqCR6n/Zv7aXNGzqqWVQjsiRQLxljNH91qWKjLZ3dnUAmACC4FixYIEkaO3ZshXVjx46VZVmaP39+tbe3fv16TZ8+Xeeee66uuuqq2qomAAAAgONEQKKBM8bovS9LFO2wNKo3Y8CHWqYrU6m9F6t5s0Paub25fv7uVP1SVEiWBOqddT969dM+n4Z2dyiWQCYAIIiMMcrKypIkdevWrcL6lJQUpaWlKT8/X7t27Trm9txuty677DIlJibq3//+d63XFwAAAEDNEZBo4Nbu8GrHLz4N7eFQUhw3FUMpu+jw3BEtEmLV9/RNatnmF+3d00h71w7Vxzu2kSWBeuXDNR7ZLGlkBoFMAEBw5efnq7i4WImJiYqPj6+0TIsWLSQdnmviWO666y6tX79eTz/9tJo3b16rdQUAAABwfAhINHDvfVUqu006tw83FUNtWU6m9pUcVGpMsmw2o269tuqkLjtVWthI363spzlrt4W6ioAk6ZcDPq3Z7lFGB4eaJ/M1AQAIrsLCQklSXFxclWViY2MlSYcOHTrqtpYsWaLHHntMEydO1Pjx4wOqh9vt1oEDB8q93G53QNsAAAAAcHQMDN6Afb/bq8yfvDrrZIdSuakYUmXZEanOJFnW4UwVy5I6dtqtxCSXvvm6nd74pLFSzX5N6p/sLwOEwuLvPPIZ6ZwefEUAAILPbrdLUrV+//h8virX7d+/X1deeaVatmypf/zjHwHXY+bMmZoxY0a5ZTfffLP+/Oc/B7wtRK78fLKeERiOGQSC4wWB4phBoBo3bhz0z+BuUwM29+tSSdK4U6JDXBOUZUd0S25dYV1qWr4GDirSshVt9OKyWO3Lj9F1w51yRhGUQN3zeI3+u75UTRNt6t3eHurqAAAiQEJCgiTJ5XJVWaZsXVnZylx33XX6+eef9fHHH6tRo0YB12PatGm69dZbyy0rLCysk04ZGhaOGQSKYwaB4HhBoDhmUN8QkGigft7n05dbPOrb0aHWTcmOCKXKsiN+KyHRrYwz1uiH77ppaVasft7n0x1jYxguB3Xum21e5RcaXXJGlGw2gmIAgOBLTExUYmKiCgoK5HK5/MMzHSk7O1vSr3NJ/NY333yj1157TY0bN9bs2bM1e/Zs/7pt2w4Pi7ly5UpNnDhRXbp00fTp0ytsw+l0yul0llvm8XhqulsAAAAAKkFAooF6/3/ZERecwtwRoXa07IgjtUhI0N6TP1WHg/Hatrmt7njFpVtGO9W7Pacp6s4n60pls6Szu3PcAQDqhmVZ6tq1q1atWqWNGzcqIyOj3Pq8vDzl5OQoJSVFLVu2rHQbZXNL5OXlac6cOZWW2b59u7Zv366zzjqr0oAEAAAAgODj8esGaO8Bnz7N8ujkVnZ1bsmQK6FUneyIMpZlqXlMsg40XaFrRpXKZ6S/vlOsN1aWyOczdVRjRLLc/T6t2+HVKSc6lJLA1wMAoO6MHDlSkjR37twK6+bOnStjjEaNGlXl+wcNGiRjTKWvF198UZJ02WWXyRijZcuWBWMXAAAAAFQDd5waoLlfl8rjMxp3KtkRoVaWHZEak1yt8qkxydpXckD7YjL18KRYtW1m05srS/SXd4t1oIigBILrk+88MpKG9SQ7AgBQtyZPnqy4uDjNmjVLK1eu9C/fvHmzP5vh9ttv9y/Pzs7Wpk2b/EM5AQAAAAgPBCQamH0HffrkO49OTLOrdzuyI0IpkOyIMpZlKdWZrOW5WTLRBZp5WayGdo/Suh1e3fayS5t3e4Nca0SqUq/Rkg2lap5sU482XDsAAHWrVatWeuKJJ1RUVKSBAwdq2LBhGjNmjHr16qWcnBzNnDlTPXv29JefNm2aunTpomnTpoWw1gAAAAACRUCigXnvq1J5vEYTzoiq9k1wBEeg2RFlyrIkluVkKtph6drhTl0/wqkDLqN7XndpwZpSGUO2BGrX1z94VVBkNKyng8msAQAhMWXKFH344Yc688wztWrVKn3++efKyMjQO++8o6lTp4a6egAAAABqAeNyNCBkR9QfNcmOKHNklsSgtK5qEZeiId2i1CHVpkc+cOuFJW5t+tmra4c7FefkxjFqx8frSuWwWRrclaHeAAChM3z4cA0fPvyY5WbPnq3Zs2dXa5tXXnmlrrzyyuOrGAAAAIBaQYZEA0J2RP1R0+yIMkdmSZRpl2rX3ybF6rSTHFr5vUd3/selH3/x1VaVEcF25/m0fqdXp55kV6N4rh0AAAAAAAAIDgISDQTZEfXH8WRHlDkySyK7KN+/PN5p6bbznLpykFO5BT5NnePS8szS2qo6ItQn3x0+hob1JDsCAAAAAAAAwcOQTQ0E2RH1x7KcTO125allXGPluPbXeDtGRrtdeVqWk6lLOgzwL7csS+f2jVKnFjY9Os+tJz50a+MunyYPiVa0g7ZHYEo8Rks3eHRCik3dWhOjBgAAAAAAQPBw96kBIDuiftnjLlC7hFRF2Rxy2Ow1fkXZHGqXkKo97oJKP6dzS7v+36RY9Whj1yfflWr6q8XKLWAIJwTmyy1eHSw2OqcnwUwAAAAAAAAEFxkSDcBbX/wvO6I/NxTrg5vSR8nI1Nr2LFXdpo3iLd1zYYze/KJUb39RottfdunGkU71O5FTG9Xz8bpSRdktDerKMQMAAAAAAIDgqnGGxPLly3XOOecoNTVViYmJ6t+/v958880aV8QYo6FDh6pVq1ZVlnnvvfdkWVaVr4svvrjGnx+uft7n0+L1perS0q7e7cmOqA8sy5LNstXa61hBJpvN0sVnROuu8TGyLOmhucV65dMSeX21FxRBw/TTXp+yfvaqf2e7EmMJZgIAAAAAACC4avRI7Jw5czRp0iQ5HA4NGTJEdrtdixcv1oQJE5SZmakZM2YEvM3bbrtNixcvVsuWLasss2bNGknSWWedVWng4vTTTw/4c8PdK5+WyGeky8+KJjsiwmW0d+iRy2165AO35n5Vou93e3XruU6lxDMyGyrnn8y6B5NZAwAAAAAAIPgCDkjk5ubq6quvVnx8vJYvX66MjAxJ0qZNmzRo0CA98MADOv/88/3Lj6WoqEjXXXedXnrppWOW/fbbbyVJTz31lLp27Rpo1RucDT959c1Wj87o7FCnE8iOgNQsyaa/XBKjl5aV6MNvS3Xbyy7dOiZGXVtzfKC8Eo/Rsky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",
       "text/plain": [
        "<Figure size 1600x800 with 4 Axes>"
       ]
@@ -7709,7 +1259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 29,
    "id": "single-demand",
    "metadata": {
     "ExecuteTime": {
@@ -7726,17 +1276,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 30,
    "id": "fc4d7ee4-0ce8-4e83-a7c0-05aaf08016cc",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-09-03T01:37:34.957181Z",
      "start_time": "2021-09-03T01:37:23.599176Z"
     },
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    },
     "tags": []
    },
    "outputs": [
@@ -7744,48 +1290,49 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:root:Using 501 steps, 1000 samples, 0.2 learning rate and 100 particles for SVI.\n",
-      "INFO:root:Guessed max_plate_nesting = 1\n"
+      "2024-01-21 17:24:26 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "INFO:orbit:Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 17:24:27 - orbit - INFO - Using SVI (Pyro) with steps: 501, samples: 1000, learning rate: 0.2, learning_rate_total_decay: 0.05 and particles: 100.\n",
+      "INFO:orbit:Using SVI (Pyro) with steps: 501, samples: 1000, learning rate: 0.2, learning_rate_total_decay: 0.05 and particles: 100.\n",
+      "2024-01-21 17:24:27 - orbit - INFO - step    0 loss = 9426.8, scale = 0.12361\n",
+      "INFO:orbit:step    0 loss = 9426.8, scale = 0.12361\n",
+      "2024-01-21 17:24:27 - orbit - INFO - step   50 loss = 4039.9, scale = 0.30712\n",
+      "INFO:orbit:step   50 loss = 4039.9, scale = 0.30712\n",
+      "2024-01-21 17:24:28 - orbit - INFO - step  100 loss = 3901.3, scale = 0.34743\n",
+      "INFO:orbit:step  100 loss = 3901.3, scale = 0.34743\n",
+      "2024-01-21 17:24:28 - orbit - INFO - step  150 loss = 2484.2, scale = 0.18519\n",
+      "INFO:orbit:step  150 loss = 2484.2, scale = 0.18519\n",
+      "2024-01-21 17:24:29 - orbit - INFO - step  200 loss = 1791.8, scale = 0.15611\n",
+      "INFO:orbit:step  200 loss = 1791.8, scale = 0.15611\n",
+      "2024-01-21 17:24:30 - orbit - INFO - step  250 loss = 1786.2, scale = 0.15746\n",
+      "INFO:orbit:step  250 loss = 1786.2, scale = 0.15746\n",
+      "2024-01-21 17:24:30 - orbit - INFO - step  300 loss = 1784.6, scale = 0.15489\n",
+      "INFO:orbit:step  300 loss = 1784.6, scale = 0.15489\n",
+      "2024-01-21 17:24:31 - orbit - INFO - step  350 loss = 1783.8, scale = 0.15357\n",
+      "INFO:orbit:step  350 loss = 1783.8, scale = 0.15357\n",
+      "2024-01-21 17:24:31 - orbit - INFO - step  400 loss = 1783.4, scale = 0.15639\n",
+      "INFO:orbit:step  400 loss = 1783.4, scale = 0.15639\n",
+      "2024-01-21 17:24:32 - orbit - INFO - step  450 loss = 1783.5, scale = 0.15531\n",
+      "INFO:orbit:step  450 loss = 1783.5, scale = 0.15531\n",
+      "2024-01-21 17:24:33 - orbit - INFO - step  500 loss = 1783.4, scale = 0.15623\n",
+      "INFO:orbit:step  500 loss = 1783.4, scale = 0.15623\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Initial log joint probability = -3825.39\n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      19      -3515.31     0.0334474       33.1976           1           1       30   \n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      29      -3512.45     0.0327342       33.4794       0.001       0.001       81  LS failed, Hessian reset \n",
-      "      39      -3511.95   9.99099e-05       33.5189    0.009193           1      100   \n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      59      -3511.93   1.15041e-06       32.3198      0.3778     0.03778      132   \n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      68      -3511.93   2.00515e-07       32.8999      0.1865           1      146   \n",
-      "Optimization terminated normally: \n",
-      "  Convergence detected: relative gradient magnitude is below tolerance\n",
-      "step    0 loss = 9428, scale = 0.12439\n",
-      "step   50 loss = 4039.9, scale = 0.30986\n",
-      "step  100 loss = 4078, scale = 0.43249\n",
-      "step  150 loss = 3773.9, scale = 0.39285\n",
-      "step  200 loss = 3433.2, scale = 0.3148\n",
-      "step  250 loss = 2077.2, scale = 0.16773\n",
-      "step  300 loss = 1787.3, scale = 0.15371\n",
-      "step  350 loss = 1786.4, scale = 0.1519\n",
-      "step  400 loss = 1785.1, scale = 0.15628\n",
-      "step  450 loss = 1785.2, scale = 0.15517\n",
-      "step  500 loss = 1785.1, scale = 0.15593\n",
-      "CPU times: user 14.3 s, sys: 2.59 s, total: 16.8 s\n",
-      "Wall time: 10 s\n"
+      "CPU times: user 9.51 s, sys: 2.64 s, total: 12.1 s\n",
+      "Wall time: 6.27 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x15395ef10>"
+       "<orbit.forecaster.svi.SVIForecaster at 0x2a33f8700>"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7823,7 +1370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 31,
    "id": "02faea11-6721-440a-b3d5-185863b6e3a9",
    "metadata": {
     "ExecuteTime": {
@@ -7838,7 +1385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 32,
    "id": "e5430ac2-6752-41f5-8dc1-96ff764661e6",
    "metadata": {
     "ExecuteTime": {
@@ -7849,7 +1396,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -7873,7 +1420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 33,
    "id": "2b71431a-b79a-4718-94b3-ef294974ff48",
    "metadata": {
     "ExecuteTime": {
@@ -7884,7 +1431,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1600x800 with 4 Axes>"
       ]
@@ -7909,7 +1456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 34,
    "id": "alike-ordinance",
    "metadata": {
     "ExecuteTime": {
@@ -7922,48 +1469,49 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:root:Using 501 steps, 1000 samples, 0.2 learning rate and 100 particles for SVI.\n",
-      "INFO:root:Guessed max_plate_nesting = 1\n"
+      "2024-01-21 17:24:33 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "INFO:orbit:Optimizing (CmdStanPy) with algorithm: LBFGS.\n",
+      "2024-01-21 17:24:34 - orbit - INFO - Using SVI (Pyro) with steps: 501, samples: 1000, learning rate: 0.2, learning_rate_total_decay: 0.05 and particles: 100.\n",
+      "INFO:orbit:Using SVI (Pyro) with steps: 501, samples: 1000, learning rate: 0.2, learning_rate_total_decay: 0.05 and particles: 100.\n",
+      "2024-01-21 17:24:34 - orbit - INFO - step    0 loss = 14607, scale = 0.090058\n",
+      "INFO:orbit:step    0 loss = 14607, scale = 0.090058\n",
+      "2024-01-21 17:24:34 - orbit - INFO - step   50 loss = 2421, scale = 0.24169\n",
+      "INFO:orbit:step   50 loss = 2421, scale = 0.24169\n",
+      "2024-01-21 17:24:35 - orbit - INFO - step  100 loss = 1910.1, scale = 0.34806\n",
+      "INFO:orbit:step  100 loss = 1910.1, scale = 0.34806\n",
+      "2024-01-21 17:24:35 - orbit - INFO - step  150 loss = 1892.5, scale = 0.32626\n",
+      "INFO:orbit:step  150 loss = 1892.5, scale = 0.32626\n",
+      "2024-01-21 17:24:36 - orbit - INFO - step  200 loss = 1879.5, scale = 0.32868\n",
+      "INFO:orbit:step  200 loss = 1879.5, scale = 0.32868\n",
+      "2024-01-21 17:24:37 - orbit - INFO - step  250 loss = 1877.2, scale = 0.33043\n",
+      "INFO:orbit:step  250 loss = 1877.2, scale = 0.33043\n",
+      "2024-01-21 17:24:37 - orbit - INFO - step  300 loss = 1875.1, scale = 0.33518\n",
+      "INFO:orbit:step  300 loss = 1875.1, scale = 0.33518\n",
+      "2024-01-21 17:24:38 - orbit - INFO - step  350 loss = 1873.6, scale = 0.32972\n",
+      "INFO:orbit:step  350 loss = 1873.6, scale = 0.32972\n",
+      "2024-01-21 17:24:38 - orbit - INFO - step  400 loss = 1873.6, scale = 0.33425\n",
+      "INFO:orbit:step  400 loss = 1873.6, scale = 0.33425\n",
+      "2024-01-21 17:24:39 - orbit - INFO - step  450 loss = 1873.9, scale = 0.33057\n",
+      "INFO:orbit:step  450 loss = 1873.9, scale = 0.33057\n",
+      "2024-01-21 17:24:39 - orbit - INFO - step  500 loss = 1874, scale = 0.33288\n",
+      "INFO:orbit:step  500 loss = 1874, scale = 0.33288\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Initial log joint probability = -3825.39\n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      19      -3515.31     0.0334474       33.1976           1           1       30   \n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      29      -3512.45     0.0327342       33.4794       0.001       0.001       81  LS failed, Hessian reset \n",
-      "      39      -3511.95   9.99099e-05       33.5189    0.009193           1      100   \n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      59      -3511.93   1.15041e-06       32.3198      0.3778     0.03778      132   \n",
-      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
-      "      68      -3511.93   2.00515e-07       32.8999      0.1865           1      146   \n",
-      "Optimization terminated normally: \n",
-      "  Convergence detected: relative gradient magnitude is below tolerance\n",
-      "step    0 loss = 14607, scale = 0.089832\n",
-      "step   50 loss = 2412.2, scale = 0.25205\n",
-      "step  100 loss = 1898.9, scale = 0.33786\n",
-      "step  150 loss = 1887.2, scale = 0.32232\n",
-      "step  200 loss = 1887.3, scale = 0.32843\n",
-      "step  250 loss = 1877.8, scale = 0.32869\n",
-      "step  300 loss = 1875.5, scale = 0.33468\n",
-      "step  350 loss = 1874.9, scale = 0.3348\n",
-      "step  400 loss = 1874.4, scale = 0.33362\n",
-      "step  450 loss = 1875.3, scale = 0.32995\n",
-      "step  500 loss = 1874.1, scale = 0.33238\n",
-      "CPU times: user 13.3 s, sys: 1.64 s, total: 14.9 s\n",
-      "Wall time: 8.37 s\n"
+      "CPU times: user 9.4 s, sys: 2.62 s, total: 12 s\n",
+      "Wall time: 6.16 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<orbit.forecaster.svi.SVIForecaster at 0x14e19fb50>"
+       "<orbit.forecaster.svi.SVIForecaster at 0x2a34cd940>"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8003,7 +1551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 35,
    "id": "4f3d0153-751e-4ab4-943b-15676aa3dc4b",
    "metadata": {
     "ExecuteTime": {
@@ -8018,7 +1566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 36,
    "id": "00c06923-4296-4fde-9589-1aa2f11e9a6f",
    "metadata": {
     "ExecuteTime": {
@@ -8029,7 +1577,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -8053,7 +1601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 37,
    "id": "2745e014-3687-4e07-a134-b82152cf3bfc",
    "metadata": {
     "ExecuteTime": {
@@ -8064,7 +1612,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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rwwIAYEwiIQEAAIDj1tHl0Nf5VbrrrS+1fsdcFZWkq6bRT7ERdTphcpHOmb1LGSllCglolcl04CyJyuZK2Qpsrg4dgJvrchj6w6o8/XdziVKjA/XY5dMUFejj6rAwzCxmk26aN063nDVRVU3tuvPNbfoqv8rVYQEAMOawZRMAAAAGxTAM7alo1Mrscq3dVaHqphaVNDQrNLBVk2LrFRNWL08PR6/vNZlMsgZYZSu0aUHKAsUEctAogKHX2tGl363I1YbCas1OCtXd502Wjyfn1Yxl56RbFRPso0c/zNZvPsjWdScl6fLZ8T1nHgEAAOciIQEAAIABaWzr1JrcCn20s6xny4uEMF/FRBTLs+lDzYqb0K+JHau/VZn2TNkKbFoybYmzwwYwxjS0dujBZVnKKWvQgslRuuXMCfLgMGNIyogL1pNXztCDy7P00pd7VVzdrJvPnCgvD9oHAADORkICAAAAx2QYhrJK6/XRTrs+312p9k6HfD0tOjcjWgvTrfL3bdD9n/5FcSGB/V5lSpUEAGepaGjT0vd2qqi6WZfOitP1pySzAh6HsAb56PeXT9fvP86VLbdC++ta9cvz0xTqz2HXAAA4EwkJAAAA9KmlvUurcuz6YHupiqtbJEmTowO1cEq05k2M6Nn65LXMZapqqdLUqKkDej5VEgCGWnF1s3797g5VNrbrhtOS9a2Z8a4OCSOUr5dFvzw/TS99Waj/bi7R7W9s1a8uTNf4yABXhwYAgNsiIQEAAIAjlNa16P3MUn2cZVdLe5f8vS26aHqsFk6xKinc/9B7G0plK7Qpyj9qwCuQqZIAMJSyS+v14LIsNXd06fZzJmnB5ChXh4QRzmw26fpTU5QY7qc/rd6tu97M1O3nTNIpEyJcHRoAAG6JhAQAAAAkHdiWaWtxrZZtK9XGvdUyDCkxzE+Lp8fojNSoPg+CtRXYBlUd0Y0qCQBDYWNhtR79MEcmSb++ME2zk8JcHRJGkTMnWxUT7KvffJCtRz/M0bUnJerKOQls9QUAwBAjIQEAADDGdTkMrc2r0Jub9qmoqlkmkzQ3OUyLp8dqenzwUSdjjqc6ohtVEgCO1+ocu/6wMk/+3h66f/EUpUYHujokjEJpMUF64srpenh5tl75qkh7q5r1s7Mnytuj94Q8AAAYOBISAAAAY1RbZ5dWZpXr7S37ZK9vk4+nWRfPiNWF02IVHezTr2fYCmwqaShRQlCCyhrLBh2LYRgqaSihSgLAgL21eZ/+8XmhIgK89ODFGUoI83N1SBjFogJ99Nhl0/TUyl1al1epsrpW/fKCNIUHeLs6NAAA3AIJCQAAgDGmqa1TH2wv1Xvb9qu2uUOBPh5acmKiLpgWoyAfzwE9y95kV0pIyvEHZZJSQlJkb7If/7MAjAkOh6EXvyjU21tKlBjmpwcunqIIJo0xBHy9LLr73Ml69eu9emPjPt3+xjb96oI0TbRSeQMAwPEiIQEAADBG1Da3671t+7U8s1Qt7V0KD/DSTfNStGhKdJ/nQxzLrSfdKkPGkMVoEnt1Azi2zi6H/rh6t2w55UqLCdR9F6YrcIAJVeBozGaTrjs5WQlhfvrjqjzd/dZ23Xr2RM2bGOnq0AAAGNVISAAAALg5e32r3t5Soo93lqmjy1BciK8umxevM1Ij5WkxH9ezTSYTSQQAw6q1o0u//TBHm/bW6ISUMN2xKHXQSVXgWM5IjVJsiK8eWp6l363IVVF1s749N1FmM999AAAMBgkJAAAAN1VU1aw3NxVrza4KOQxpQlSArpgdr5PGhTORAmBUqm1u14PLs5Rnb9TZaVbdfOYEWejP4GSTrIF66qoZenh5lv61vlhF1c267exJJMIAABgEEhIAAABuJqesXv/ZuE/rC6olSdPig3XFnARNjw+WycTEHYDRqbi6WQ8s2yl7fZuunBOva09Kok/DsIkI8NZvL5ump1fm6fPdlbLXZepXF6YP+twSwxi67Q4BABhNSEgAAAC4AcMwtLmoRm9u2qcdJfWSpJPGheny2QlKjeYQTgAjg2EYg0oi7Cip08PvZ6mlvUs/PXOCFk6JdkJ0wNH5eFp017mpen29n15fX6Tb/r1Vv7ogfcDfs2WNZXo181WdH3++wsLCnBQtAAAjEwkJAACAUazLYeiz3ZX676Z9Kqhsktls0llpUbp0ZrwSw/1cHR4A9OiehL1m2jWKDuh/QsGWW64/rMyTl8Ws+y+aolmJoU6MEjg6k8mkJScmKjHMT0+t3KV73srULWdN1BmpUf1+xur81Vq7d60CuwKVlpjmxGgBABh5SEgAAAA4kbO2ZGjr7NLKrHK9vWWf7PVt8vE06+IZsbp4RpwiAwe3fQQAOFP3JGxMQIyWTFtyzPsNw9AbG4v1yldFCg/w0tLFU5Qc4T8MkQLHdtrECEUHe+uh5dl64uNdKq5u1jUnJh3zjKbShlLZCm2ymC1aV7ROixsWKyYwZpiiBgDA9cyuDgAAAMBdlTWW6ckvn5S90T5kz6xuatcrX+3VjS9u1F/W7FFze5euOTFRf79+rm6aN45kBIAR6eBJWFuhTaUNpUe9v7PLoT+u2q1XvipSSoS/nrhiOskIjDgTogL15JXTNTEqQG9s3KdHP8xWS3vXUd9jK7CpqqVKGVEZqm6plq3ANkzRAgAwMpCQAAAAcJLu1cDr9q477mfl2Rv05Me5uuHFDfr3hmL5eJr1/fnj9Pfr5+rqExIV6OM5BBEDgHMcPAlb2Vx51EnYprZOLV22Uyuz7ZqdFKrHLpum8EEeHAw4W3iAtx69bKpOnxSpr/Kr9Yv/bFNJbUuv93Yn5qL8o2Q2mRXpH9mvBB0AAO6EhAQAAIATHL4lw2AmGzq7HFqXV6E7/rNNt7+xTbbcCqXFBOne89P01+vmaPH0WPl4WpwQPQAMncMnYa0B1j4nYcsbWnXnm5naVlynczOidd+F6fL1op/DyObtYdHPF07S905N1r6aZt3+763aUFh9xH3diTmrv1WSFOUXdcwEHQAA7oaEBAAAgBMcz5YMe6ua9MJnBfreixv0uxW52lPRqLPTrPrD1TP06KVTdfL48GPuUQ0AI8Xhk7BWf2uvk7C7yxv1i/9kqqi6WdefkqwfnzFeFvo6jBImk0mXzorX0oumyGwy6aHlWfr3hiI5HAfOkjo4MWcymXrec7QEHQAA7ohDrQEAAIZYX1syLEhZ0OfBlfWtHVq7q0Krssu1u7xRkhQd7KPrTorVoinRCvZjSyYAo8+xJmG7+8Wv86v0+Ee5chiG7jw3VfMmRro4cmBwZiaG6umrZ+jh97P1yldF2lPRpNvOntSTmJsaNfWQ+63+VmXaM2UrsPXrsHcAAEY7EhIAAABD7PBJhyi/KOU15x0x2dDZ5dCmvTVanVOurwuq1eUw5Otp0TnpVp2VFqX0mKCeCTwAGI36MwkbaJyh59flK8DHQ7+6IF1pMUEuihYYGtYgHz1++TT9aXWe1u6qVF75V+rw/+qQxFy33hJ0AAC4MxISAAAAQ6g/q4Fb24K0KtuuNbsqVNvcIZNJmhYfrLPSrDp5XDjnQgBwC731h91MJpOi/K36x+fFsrTlKik8UEsvmqKYYF8XRQsMLR9Pi36xMFUTogL0xCebVNs+Tael10hqOuJeqiQAAGMJCQkAAIAh1Ndq4FCvWH25p04/fOUrOToPbEUSG+KjxdNidcbkSEUF+rgiXABwmr76Q0nq7DKpeN9c7Su3KCO2Vr+/4jQF+rA1HdyLyWTSSRMsis36TM1FGdqQk6TUhHJNjK/UwTk6qiQAAGMJCQkAAIAhcvhqYIdDKq8NVMG+aFU1haq9q1O15npdNTNJl8yYoMnRgWzJBMAtHa06orXdQ+uzE1XX5KPYiBIFRX6hxo6TFOjDJCzcj63Apk6PPTprRrA27kpUbnGU6pp8NWNCySH3USUBABgrzK4OAAAAwF10rwb2UZJ2Flj1yaZJ2pCToPK6EEUEN+mEVLuSkt9TUlyO0jgfAoAb6+4Prf7WQ16vb/LWZ9tTVNfko0kJFTo1rVbVrRWyFdhcFCngPAcn5vx9O3VqRoHiIupUVh2oz7anqKnVu+feg6skShtKXRg1AADORUICAABgCOSUF+u1DXkq3bdI6zLHK780XJ4eDk1OLNf8tB06Kb1I8ZH1igmKZLIBgFvrqzqiss5fn+9IVmu7h2ZMKFFqQoXMZiZh4b4OT8x5WAzNnFii9OQyNbV66evdqSqrDui53+pvVWVzJQk6AIBbIyEBAAAwSJ1dDn2VX6WHl2fppn9uVv6+JDk6g5RkrdFpUwu0YMZuTYyvlI9XR897mGwA4O56q47YXxmkr7MSJUknpe9VQlRdzzX6RbijvhJzJpM0PrZaJ6YVyWQytCEnUfn7w2QYVEkAAMYGEhIAAAADVFzdrH98XqDvvbhBj7yfrc/32GXy3Kv0cXu0cO4uTRtfqtDAFvW2IxOTDQDcWW+TsIVlodq0K16eHl06NaNQEcHNh7yHfhHuqK9ty7pFhjTppIm5CvBt087CaO0oiJZhkKADALg/EhIAAAD90NLepU+y7LrjP9v041c3663NJfLxtOi6k5L0rZPLFBnzqabEm2UxG8d8FpMNANzVwZOwhiHlFEVqe36M/H3bddrUAgX5t/X6PvpFuJOjHep+MF+vdp06tUDhQU0qLAvThpwEdTnMJOgAAG6NhAQAAMBR5Fc06pnVefrO37/WH1flKb+ySQtSI/XopVP1/66drXmTPbShbPUxJx0OxmpgAO7o4ElYQyZl5scob1+kQgJadGpGgfx8Ovp8L/0i3MmxqiMO5uXh+N85U7Wy1wTqi53JCvaMI0EHAHBbJCQAAAAO09Hl0NpdFbrrzUz97F9b9dFOuxJC/fSTBeP10g0n6PaFqcqIC5bZbBrQpMPBWA2MsWTNmjUym816/vnnB/S+iy++WCaTqc8/K1ascFLEGIzu/jDS16otu+JVZA9VZEijTp6yV96eXcd8P/0i3EF/qyMOZjYbmjFhvyYlVKiu0Vdf7ExRsGcSCToAgFvyGIqHrFmzRgsWLNBf//pX3XTTTf1+38UXX6z33nuvz+sffvihzj333KEIEQAA4JiqGtu0YmeZVuwoU21zhzwtJp05OUoXTovRRGvgEfcPZtKh28GrgRekLFBMYMxQfQxgRMnNzdW3v/1tGcaxtzM73ObNm+Xl5aUrrrii1+uxsbHHGx6GSHd/GOFr1ea8RJVVByomvF6zJpbI3I+t7CT6RbgHW4FNJQ0lSghKUFlj2VHvbW1qlY/Zp+fvwaFlSnREa0/xRG3Pm65Q60eyFdi0ZNoSZ4cNAMCwOe6EBAMMAAAw2uVXNOqdLSVak1cph8NQZKC3vntKss5JtyrY17PP93WvBp4aNXVQP9fqb1WmPZPJBrit1atX69vf/rbKy8sH/N7Kykrt27dPc+bM0SuvvOKE6DCUbAU27asrVVP1ItU2+CgiZL/iYnNU3nzs9x7MMAyVNJTQL2LUsjfZlRKS0q97LSaLLCbLIa/FRlTIy+JQ7t5Jqik7T1mlNdI0Z0QKAIBrHFdCggEGAAAYrQzD0NbiWr29pURbimolSRlxwbpkRqzmJofJbD56xcPxVEd0YzUw3FV5ebmWLl2q//f//p/MZrMSExNVVFQ0oGds3rxZkjR79mxnhIghVlJfrubq81XXECJrmF0TE3fLdNhEa7+YpJSQFNmb7EMfJDAMbj3pVhnq34LN6upqhYWF9Xpt094a/fbDHOUWmLR9X52mxgcPZZgAALjMoBISDDAAAMBo1dnl0Lq8Sr29pUQFlU0ym6R5EyP0rZlxvW7L1Jfj2ZLhYKwGhjv6zW9+o+eee06TJk3S888/rxdeeEH//Oc/B/SMLVu2SGK8MBq0dnSppvxMBZrrdPkp0frR6eOOmdQ9FpOO7/2Aq5hMpn63X7PJLLOp96M95yaH66GLp2rpsp0H/iyeQlICAOAWBnWodfcAY8KECVq9erUWLFgw4GcwwAAAAMOpub1Tb2/Zp5te2qgnP9ml0roWLZ4eo79+Z47uPHfygJIR0jdbMniYPXq2XBjMHw+zB6uB4XbGjRunZ599Vjt27NC8efMG9YzuBUwtLS266KKLFB0dLX9/f5100kl69dVXhzJcHIf2Toce/SBb20vqdeG0WN28YII8LJaeidbB/hls5RngTtJjg/TQxRmymE16YNlO7Sipc3VIAAAct0FVSHQPMG666SZ5enrqhRdeGPAzDh9grF+/Xg0NDZo6dap++tOf6pprrhlMaAAAAIeobmrXe1tL9OGOMjW3dynEz1PXnZSk86ZGK9Cn7/MhjmWotmToxmpguJNbbrnluJ/RPV742c9+pkmTJunUU09VQUGB1q9fr2uvvVZff/21/vjHPx73z8HgdXY59PhHOdpcVKuF6Vb9YP44EgnAEEuNDtSDF0/Rr9/ZqQeW7dT9i6coI45KCQDA6DWohAQDDAAAMNKV1Lbo7c37tCqnXJ1dhuJCfHXjaXE6IzVKXh6DKhI9xFBtyQDgSA0NDdqzZ49MJpP+8pe/6Pvf/37PtdWrV+vSSy/Vn/70J51++um67LLLen1GW1ub2traDnnN29tb3t7eTo19rHA4DD21cpe+yq/W6ZMi9ZMFE0hGAE4yOTpID1w8Rfe/eyAp8eDFGUqLCXJ1WAAADMpxHWo9WAwwAACAs+yyN+i/m/bpy/wqGYY0OTpQl82O1wn9OKgawMgQGBioyspKVVRUKDU19ZBrZ555ph544AHdeuuteuaZZ/ocLzz66KN64IEHDnntrrvu0p133um0uI+lpqbGZT97KBmGoRe+KpFtV7XmJAbpO7MjVFvrHp9tuLhLW8DxGUg7sHpLt58Rr9+uLNB9b23Tr84dp8RQXydGh+FCfwCJdoBvuLotHKuyfyi4JCHBAAPujHaAbrQFSLSD4WIYhjL3N2rZjnJllzVJkmbGB+nCjEilRvnJZDK5dLKMdgDJ9e1gOAYXQyksLKzPmBcvXqxbb71VGzdu7PP999xzj26//fZDXhsJC5hG23+H3rz69V6ty6/XCeMi9MsL0oek6mwscoe2gOM3kHZwUliY7vcL0APLsvTkmhI9fvk0WYN8nBgdhgv9ASTaAb7h7m3BJQkJiQEG3BvtAN1oC5BoB87U0eXQZ3mVemtLiQorm2Q2m3RORqwumxWnpHB/V4d3CNoBJNrBUImOjpZ04Dy6voyEsYE7WrGjVP9aX6wJUQG6+7w0khHAMJuZGKpfLEzV7z7K0a/e2aHHL5+mED8vV4cFAEC/uSwhcTQMMAAAcA+GYThlT/Ha5nat2FGmD3aUqaapXT6eZl08I1YXz4hTZCC/HwCj3Zo1a/TCCy8oLS1N99xzzxHX8/PzJUlxcXHDHdqYtr6gWs99ukfWIB/dvzhdvl4WV4cEjEmnTYxQY9t4/dm2R79+d6cevXSq/L1H5PQOAABHcMk3FgMMAADcX1ljmV7NfFXXTLtG0QHRQ/LM/IpGLdtWqjW7ytXRZSgiwEvfPSVZi6ZYFejjOSQ/A4Drtbe36+WXX5bVatXtt99+xEKkf/7zn5KkRYsWuSK8MSm3rEGPrchRgI+HHrh4CiuyARc7NyNG9S2devmrvfrthzm6f3G6PCxULAEARj6XJCQYYAAA4P5W56/W2r1rFRMQoyXTlgz6OR1dDn2dX633t+/XjpJ6SVJaTKAumh6nk8eHy8JB1cCoVllZqcrKSvn5+SkxMVHSgXPlJk2apF27dunmm2/Ws88+K0/PA0nH999/X3/84x/l6+vr0vPjxpL9tS16cPlOSdKvL5yiuBAO0gVGgivmxKuisU0rdpTpL2v26CcLJjilMhUAgKHk9PR5ZWWlcnJyVFRU1PNa9wDDbrfr5ptvVkdHR881BhgAAIx+pQ2lshXaZDFbZCu0qbShdMDPKKlt0T8+L9D3/rFBj63IUXZpgxakRurJK6frd5dP12kTI0hGAG7gmWeeUVpamr7zne/0vGaxWPT6668rJCREzz//vCZMmKBLL71UJ598si688EI5HA69/PLLmjBhggsjHxsa2zr14LIsNbZ26s5FqUqNDnR1SAD+x2Qy6Qfzx2lmYog+2mnXW5tLXB0SAADH5PSEBAMMAADGHluBTVUtVcqIylBlc6VsBbZ+va+1o0uf5pbrnre264cvb9Jbm0vk52XR9ack68XvzdXtC1M10cpkGDAWzJo1S1u3btVNN90kh8Oh5cuXKz8/X1deeaU2btyoyy67zNUhur0uh6HHV+SopLZFN5yWohPHhbs6JACH8bCYdde5k5UY7qcXvyjUF7srXR0SAABH5bJTj7oHGA8//LBWrFih5cuXKzQ0VFdeeaXuvfdeTZ8+3VWhAQCA49BdHRHlHyWzySxrgFW2QpsWpCxQTGDMEfe3dnRpY2GNPttdqY2F1WrrdMjDYtK8iRE6NyNaGbHBMlMJAYxqL774ol588cVery1dulRLly7t9VpSUpL+9re/OS8wHNXfPyvQ5qJaLUy36qLpsa4OB0Af/L09dP/idP38jW36/ce5eizQmwUcAIARy2QYhuHqINxFdXW1wsLCXB0GXIx2gG60BUhjsx28lvma3sx+U1OjpspkMskwDGXaM3VF+hU9Z0m0tHdp497q/yUhatTe6ZDJJKVFB+nUiRE6fVKkgn3d55DqsdgOcCTaAUZTG1ixo0x/tu1WRlyQHrw4Q54cljukRlNbgPMMdTvIszforv9mKsjXU09dOUOh/hw+PxrQH0CiHeAbY6EtuKxCAgAAuJ+DqyO6D1U0mUyyBli1cs9a+RmzlV3i0Ma93yQh0mOCdOqECJ0yPlzhAd4u/gQAgB0ldXpuzR5Zg7x193lpJCOAUWKiNVA/PXOinvxklx79MFuPfGsq//8CAEYcEhIAAGDIdJ8dMTVqqiSps8sse3WA9lfFq7DcrNy8HIX7hWtK7IEkxMnjSEIAwEhSXt+q33yQLW+LWfddmO5W1WrAWLBgcpT2VDTq3a379de1+frJAs7mBACMLCQkAADAkOiujgj3iVFJZbBKq4JVXhsgh+NApURoYKVCgzfrsQtuUpo1wcXRAgAO197p0G8/zFFDa6d+dUGaksL9XR0SgEH43qkp2lvVrBU7yjQuwl/nTT3yDC8AAFyFhAQAADhuze2d+svn67Qtb7yMjmQ5HGaZTIbCApsVG1Gv6LB6eXt2KtOeqS32dUqzLnF1yACAw/y/NXuUV96oK+fE68Rx4a4OB8AgWcwm3Xluqm779zb9ZW2+EsL8lBEX7OqwAACQREICAAAMUlNbp9YXHDiY+uuCcu2tbZfZFC9raItiw+sVE1Yvb6+ug95x4CwJW6FNC1IWKCaQ1XoAMFJ8tLNMH2fZNTMxRNecmOTqcAAcp0AfT913YZp+8Z9temxFjp6+agbbZAIARgQSEgAAoN8a2zq1vqBKn+VVaUtxjTq7DJlNkp9vtSK8NuvE5BD5eDv6fL/V36pMe6ZsBTYtmUaVBACMBHn2Bv2/NXsUFeitXyxKldlscnVIAIZAUri/fnbWJD22Ike/W5GrR76VIQ8OuQYAuBgJCQAAcFQt7V1aX1ittbsqtLnomyTE1PhgnTYhQslRnXriq38qzGyRj3fQUZ9lMlElAQAjSV1Lhx79MEeSdM/5kxXkwyHWgDs5bWKEcspi9e7W/Xrxi0LdNG+cq0MCAIxxJCQAAMAR2jq7tKmwRmvzKrWhsFrtnY5DkhAnj4tQsN+BSavXMl9TVUuVpkZN7dezqZIAgJHB4TD0+49yVdHQplvOmqgJUYGuDgmAE1x/SrJyyxr07tb9SosJ0qkTIlwdEgBgDCMhAQAAJEldDkNbimq0dleFvsqvVkvHgfMfpsQG6bSJETptQoRC/LwOeU9pQ6lshTZF+UfJZOrfFh9USQDAyPDq13u1tbhWi6ZYdU661dXhAHASD4tZd503Wbf+a6v+sDJPyRH+igvxdXVYAIAxioQEAABjXHF1s1Zm22XLrVBNU7skaaI1QPMnRurUCRGKDOz7AERbgW1A1RHdqJIAANf6Kr9Kb2zcp4lRAfr+/PGuDgeAk0UEeOuORan69bs79JsPsvXEFdPl42lxdVgAgDGIhAQAAGNQU1un1uVV6JOscu2yN0iSrEHe+vYJiTpzcpSig32O+YzBVEd0o0oCAFynpLZFT36yS4E+Hrr7/Mny8uCQW2AsmJ4QomtOStLLX+7Vs7bduu2cSQP+HQ4AgONFQgIAgDHC4TCUWVKnlVl2fbGnUh1dhrw9zFowOUrnpFk1JTZIZnP/B6WDrY7oRpUEAAy/1o4uPfpBtlo7uvTARVMUFXjsBDQA93H5rHjllDbIlluhtJggnTeVRSEAgOFFQgIAADdXVteqldl2rc4pV0VDmyQpPSZIZ6VFad7ESPl6Dbxc/3iqI7pRJQEAw8swDD1r2629Vc267qQkzUwMdXVIAIaZ2WzSbedM1G3/3qq/rsvXhKgATbRyoD0AYPiQkAAAwA21dnTp892VWplt146SeklSeICXrpwTrzPTrMd9kKGtwKaShhIlBCWorLFs0M8xDEMlDSVUSQDAMPhwR5lsuRWamxymy2fHuzocAC4S6OOpe85P0x3/2aZHP8zR01fPUJCPp6vDAgCMESQkAABwEsMwhv3nZZXWa2VWuT7fXamWji55WkyaNzFCZ6VZNTMhZEBbMh2NvcmulJCU43+QSUoJSZG9yX78zwIA9GmXvUF/XZsva5CPbjtn4pB9HwAYncZHBuiHp4/Xn1bv1pMf79KvL0ynXwAADAsSEgAAOEFZY5lezXxV58efr7CwMKf+rPKGVtlyyrUqu1ylda2SpIlRATorzar5kyIU6IQVb7eedKsMDV3CxSQGwADgLHUtHXr0g2yZTdK95092yvcCgNFn4ZRoZZc2aGW2Xf/eWKxvn5Do6pAAAGMACQkAAJxgdf5qrd27VoFdgUpLTBvy57d2dOnLPVVamW3X9pI6GYYU4uepS2bG6ey0KCWF+w/5zzyYyWQiiQAAo4DDYej3H+WqsrFdt5w1UeMiA1wdEoAR5IdnjNOeika9vr5Ik6yBmp3E2TIAAOciIQEAwBDrPvDZYrZoXdE6LW5YPCQHNjscB7ZkWpX9zZZMHhaTTh4frrMmWzUrMUQeFvMQfAIAgLt4dX2RthbXatEUq85Jt7o6HAAjjLeHRfecP1m3/murnvg4V09fPUNRgT6uDgsA4MZISAAAMMRsBTZVtVQpIypDm/ZsOu4Dm4uqmrVud4VsOeWy17dJOrAl05lpUZo/KZJDCAEAvdpQWK03NhRrQlSAvj9/vKvDATBCxQT76vZzJunh97P12w9z9NtLp8nLg0UuAADnICEBAMAQ6q6OiPKPktlkVqR/pGyFNi1IWTCgKoni6mZ9trtSn+VVqqi6WdKBLZkunRWnsyZblRju56yPAABwA2V1rXri41wFeHvo7vMmM7kI4KhOHBeuy2fH681N+/T3zwv0w9NJYgIAnIOEBAAAQ6i7OmJq1FRJUpRflPKa8/pVJbGvplmf767UurxK7a06kIQI9PHQoilWnTYxUlPjgmUxc24DAODoWtq79NDyLDW3d+n+xemyBrH9CoBju/akJOXaG/R+ZqkmRwfqjNQoV4cEAHBDJCQAABgiB1dHmEwHEgcmk0nWAGufVRIltS36PK9S63ZXqrCySZIU4O2hhelWnTYxQlPjgjkXAgDQbw6HoSc+zlVRdbO+e0qyZieFuTokAKOExWzSnYtSdcu/tuqZ1bs1LiKAqlwAwJAjIQEAwBA5vDqim9Xfqkx7Zk+VxP7alp7tmAoOSkKck27VqRMiND2eJAQAYHBeW1+krwuqdfqkSF02K87V4QAYZUL8vHTXuam6963t+s0H2Xrqqhny9bK4OiwAgBshIQEAwBDorTqim8lkUrBnkl5dv1trMr/Q/hqHJMnf26Kz06w6bWK4pseHkIQAAByXz/Iq9e//HWL907MmHPF9BAD9MSU2WDeclqLn1xXoD6vydNe5qfQnAIAhQ0ICAIAh0Ft1RHOrpwrLA1VZGKG6Rl81dzSrObBCF09L07yJEZoWHyJPkhAAgCGQX9Gop1fuUoifp355QZq8PVjRDGDwLpoeq6zSen2+u1LvbA3Qt2bGuzokAICbICEBAMBxOrg6orXdU/urgrS/Mli1jb5ydDnk5WUoPrJWPn6FCvCv1FUnnqCYQPb0BgAMjarGNj38fra6DEP3np+miABvV4cEYJQzmUz62VkTVVzdrBc/L1RSuL9mJYa6OiwAgBtgWSYAAMfp/ZxPtXt/kPYUnKiVmyYpqzBajS3eio+s1cyUPVo4d5dmTtyvybGeqm6tkK3A5uqQAQBuoqW9Sw8sy1JFQ5tuXjBBaTFBrg4JgJvw8/LQLy9Il6+XRb9bkaP9tS2uDgkA4AZISAAAMAgt7V36JMuu29/4Wn/8qEtVlbNU1+Sr2PB6zUkt1sK5uZo5cb8ig+plMRuSDqw0swZYZSu0qbSh1MWfAAAw2nV2OfTbD7NVUNmka05M1FlpVleHBMDNxIX46o5Fk9XS3qWH389Sc3unq0MCAIxybNkEAEA/ORyGduyv08rscn2xu1JtnQ7VtlbJx3efpiV6KTqsUR4Wx1GfYfW3KtOeKVuBTUumLRmmyAEA7sYwDP3Ztkebi2q1MN2qq+YmuDokAG5qdlKorj81WX//rFBPfLxLvzw/TWYzh1wDAAaHhAQAAMdQ3tCqlVnlWp1jl72+TZI0yRqo2Sme+qT4VXl7GYoOiO7Xsw6ukliQskAxgTHODB0A4KZeX1+sldl2zU4K1Y/OGC+TiclBAM5zyYw4FVQ0yZZboVe/3qvrTk52dUgAgFGKhAQAAL1wOAxt3Ver9zNLtbGwWg5DCvX30mWz4nRWmlUJYX56LfM11XXYNTV06oCeTZUEAOB4vLu1RK+vL9L4SH/dde5keVjYiReAc5lMJv3kzAnaV9OiNzbuU3KEv+ZNjHR1WACAUYiEBAAAB2lo7dDqnHJ9sL1U+2tbZTJJc5LCdP7UaM1MDJXlf+XppQ2lshXaFOUfNeBVqVRJAAAGa8WOUj2/rkCJYX564KIM+XpZXB0SgDHC28Oiey9I023/3qqnPtmlyEBvTY4OcnVYAIBRhqU0AABIKq9v1V/W7NH1/9ig59cVqKG1U5fNitPfvjNHv16crjnJYT3JCEmyFdhU1VIlq//gDhC1+ltV2VwpW4FtqD4CAMDN2XLK9eynexQd7KOHLslQsJ+nq0MCMMZEBHjr/sXpMptMenh5tsrqWl0dEgBglKFCAgAwphVXN+u/m/fJllshh8PQ+Eh/XTQjVqdNiJSXR+95++OpjuhGlQQAYCBWZdv1x1V5Cvf30iOXZCjM38vVIQEYoyZEBeqORal65INsLX1vpx6/YpoCfUiQAgD6h4QEAGBM2l3eqP9sKtaXe6pkGFJGXJCumJOgmQkhx0wy2ApsKmkoUUJQgsoay456b2tTq3zMPr1eMwxDJQ0lnCUBADiq9zNL9Zc1exQZ6K2HL8lQVFDv3ysAMFxOHBeum+aN09/W5us3H2TrgYsy+lzMAwDAwUhIAADGlB0ldXpz0z5t2lsjSZqTHKorZicoPbb/+9/am+xKCUnp170Wk0UWUx/7e5uklJAU2Zvs/f7ZAICx5b+b9unFLwoVG3Jgm6aoQJIRAEaGi6bHqqyuRcu2leqZ1Xm67ZxJg64eBgCMHSQkAABuzzAMbS6q0Rsb9imrtF4mkzRvYoQunx2vcZEBA37erSfdKkNGv+6trq5WWFjYUe8xiYEbAOBQDoehf3xRqHe2lCgx3E8PX5yhULZpAjDC3HTaONnr22TLrZA12EfXnJjk6pAAACMcCQkAgNtyOAx9sadKb2wsVkFlkyxmk85Jt+qy2fGKC/Ed9HNNJlO/kwhmk1lmE+XrAID+a+3o0hMf5+qr/GpNjg7UfYvTFcT+7ABGILPZpDsWperet7brX+uLFebnpfOmcjYaAKBvJCQAAG6no8uhT3Mr9OamYu2vbZWXh1kXTY/VJTPjFBno7erwAADoU3VTux5enqW88kbNnxShn501iX3ZAYxoPp4W/Xpxuu58M1PPrdmjIF9PnTohwtVhAQBGKBISAAC30drRpU+y7Hpr8z5VNrbLz8uiK+fE66LpcQr2Y2UpAGBky9xXq8c/ylVtc4eumpuga05MZD92AKNCiJ+XHrokQ3e8manff5wrf28PzUgIOep7DMOgjwOAgxhG/7aGHu1YagMAGPWa2jr1xsZi3fTPjfrr2nx1dBn6zslJ+vv1c3XdyckkIwAAI5rDYeiNDcW6750dautw6I5Fqbr2pCQm6gCMKtYgHz140RR5e5j1m/ezlWdv6PPessYyPfnlkyprLBvGCAFg5OruF+2NdleH4nRUSAAARq3a5nYt27ZfyzJL1dLepYgAL/3g9HE6O80qH0+Lq8MDAOCYKhra9MdVedpaXKukcD/dfd5kxYf6uTosABiU5Ah/3Xdhuu57Z4eWLtup310+vdez21bnr9bavWsVExCjJdOWuCBSABhZuvvFwK5ApSWmuTocpyIhAQAYdcrqWvXWln1amWVXR5eh2BAffX/eOJ2eGilPC8V/AICRzzAMfZJl1/OfFailvUvnpFv1/fnjSKgDGPWmxAbrrnMn6zcfZOvX7+zQY5dPU0TAN+e4lTaUylZok8Vska3QpgUpCxQTyEHYAMaug/vFdUXrtLhhsVv3i8zaAACcwhl7H+ZXNOp3K3L0g5c36sPtZUoO99c9503Wc9fM1tnpVpIRAIBRoaS2Rfe/t1N/Wr1bfl4W3b84XbecNZFkBAC3ceK4cN1y1kSVN7Tp/nd3qr61o+earcCmqpYqZURlqLK5UrYCmwsjBQDXO7hfrG6pdvt+kQoJAMCQK2ss06uZr+qaadcoOiD6uJ5lGIa2FtfqnS0l2lxUK0mamRiiy2fHa2pcMPtrAwBGjeb2Lr39WYHe27ZfDoehs9OsunFeigK8GZYBcD9npVlV39qhv39WqKXv7dQjl0xVbVu5bIU2RflHyWwyyxpgpUoCwJjWXR3R3S9G+ke6fb/IUlIAwJDr3vtwdf7qQT+jpb1L72eW6ievbdav392prcW1mjcxQk9fPUMPXpyhafEhJCMAYJRYs2aNzGaznn/++QG9r62tTU888YSmTp0qf39/Wa1WXXvttdqzZ4+TInWeDYXV+vnbuXpnS4nGRfjrscum6WdnTyQZAcCtfWtmvK6cE688e6Me+SBLn+w+sArY6m+VJFn9rVRJABjTuqsjuvvFKL8ot+8XjzshweACAHCww/eELW0oHdj761r0/Lp8Xf+P9frLmj2qbmrXt2bG6a/fmaM7z52s8ZEBToocAOAMubm5+va3vz3grfw6Ozt16aWX6he/+IWqq6t13nnnKS4uTq+++qpmzpypzMxMJ0XsHA5TvUoainTdqRF64orpSo8NcnVIADAsrj0pSedmRGtjYaVeWFeuSL+onoVFJpOpp0pioOMGABjtDq6OGEv94nElJBhcAAAON5g9Yds7Hfosr1K/fneHfvDyJr27db8iArz1kwXj9eL3TtANp6XIGuQzDNEDAIbS6tWrNX/+fJWWDnww9eyzz+qDDz7Q2Wefrby8PL355pvavHmznnrqKTU0NOi73/2uU84rcpY9dV8oIORVdXpsltlMhR+AscNkMulHp4+XNaxaFTUhspfN1MHdN1USAMaqw6sjurl7vzjohASDCwDA4Q7f+/BYWf29VU091RCPrcjRtn11OmlcuB66JEPPLJmpczNiOOATAEah8vJy/fjHP9Y555yj6upqJSYmDuj9hmHoiSeekCQ988wz8vPz67l26623av78+dq6datsttExSOv+fvSwmNx6tRsA9MXeVCaPoI8UEdKg4opQZe219iQlxsJqYAA4XG/VEd3cvV8ccEKCwQUAoC+HZ/d7y+rXt3ZoxY5S/fyNbbr5tS16d+t+Bfl46nunJuuf35ure89P04wEzocAgNHsN7/5jZ577jlNmDBBq1ev1oIFCwb0/h07dqioqEiTJ09WamrqEde/9a1vSZKWL18+JPE6W/f3Y1pEmluvdgOAvtgKbKppq9T8KVUKC2xW/v5w7S6J6Lnu7quBAeBwfVVHdHPnfnHACQkGFwCA3hxt78OVe9bqrS05emDZTl33wnr92bZHe6uadHaaVY9dNk3PXTtLl86KV4ifl4s/BQBgKIwbN07PPvusduzYoXnz5g34/Tt37pQkZWRk9Ho9PT1dkrR9+/bBBzlMBlo9CADu5uB+0MNDOiGtSEH+rcopilJhWagk918NDAAHO1p1RDd37hcHnJBgcAEA6M3h2f0uh0mlVYHaVzJbX+84QU98kqvNe2s0MyFEt50zUS/deIJ+dvZEpccGUQ0BAG7mlltu0Y9+9CN5enoO6v379++XJMXExPR6vft1u90+uACHUX+qBwHAnR3eD3p6OHRS2l75+7Rre36MSiqDJNE/Ahg7jlUd0c1d+0WPgb7hlltuOa4f6E6DCwDAAd3Z/Ui/KFXWBaikMlilVYHq7Dpw/kNoQKXCgjfrdxfepNSoBBdHCwAY6ZqamiTpkO1dD+br6ytJamxs7PMZbW1tamtrO+Q1b29veXt7D1GUx3a06kFboU0LUhYoJrD3cREAuIO+VgF7e3XppPS9+nxHsrbkxcnD4pA1tJH+EYDb6091RDd3/b1xwAmJ4zUUgwtpZAwwAAAHzgZ6bfMaZRXGytGWqvaOA18twf6tio2oVGxEnXy9OpRpz9SmsnVKjVri4ogBACOdxXIgoX2sQZrD4ejz2qOPPqoHHnjgkNfuuusu3XnnnccfYD8t27lMpRWlSo9IV0t9i1oaWiRJQUaQdlbs1LKty3T5lMuHLR6MHDU1Na4OASPAWGgHh/eDBzOpRTMSd2nD7klanxWr2eN2K8Rv7PWPY6Ed4NhoB2PH0fpFST2/L3Yb7t8bw8LCnP4zhj0hMRSDC2lkDDAOR+cBiXaAb7h7WyiuadUXBbX6NM+uneUdkiYo2LdDyRHligmplr/P/5LGbVJrmxRsBOvD7R9qRvAMRQdGuzT24eTu7QD9QzuA5Pp2MByDi6ESEBAgSWppOXKQdvDr3ff15p577tHtt99+yGvDuYCptKFUG2o2KN4aL7+AbxZj+QYdWICVYEnQhpoNWuy52G1Wu2FgRtP/k3Aed24HffWDB/MNkk72LdZXWcnaVjRRp2QUKiF67PWP7twO0H+0A/fXn35R+ub3xW7u9nvjsCckhmJwIbl+gNEXOg9ItAN8w93agr2+VWt2VWjNrgoVVTVLktocdQoO26U5yX4KCWjTgXyzWdKhX6BJgUnKtGdqa91WLUkaW1US7tYOMDi0A0i0g/6Ki4uTJJWVlfV6vbT0wMF+fW0DK7l+bNC9N/DUqKm9Xrf6W5Vpz5StwKYl08bW9yKAseFY/WC30MBWzZ1crK+zEvV1VpJOnuJQfuNG+kcAbqe//eLh3O33xmFPSAzF4EJy/QADAMaK2uZ2rcur1NpdFcopa5AkBXh76NyMaE2JM+mV7H/Kw2JRaMDRqx7cde9DAMDQy8jIkCRlZWX1en3nzp2SpKlTBzaYGy792RuY70UA7mwge6RLUkRwk2ZN2qdNu+L1dXayJqbY6R8BuJWB9osHc7ffG83D/QNH++ACAMaCzi6Hvthdqfvf3aHv/n29/ro2X4VVTTp9UqTuuzBdL914gn6yYIL2t36t6tYqWf2t/Xqu1d+qyuZK2QpsTv4EAIDRLDU1VePGjdOOHTu0Z8+eI66//fbbkqTzzz9/uEPrl+7Vb8f6fuR7EYC76m8/eLCY8AZNG1+qljZP5e+dq7L6evpHAG5jMP3iwdzp98ZhT0iM9sEFALgze32rXv6yUN97cYMe/TBHW4trNSc5THcsStXLN56oXyxK1QkpYfK0mAeV3T84q1/aUOrkTwMAGA0qKyuVk5OjoqKiQ16/+eabZRiGbrzxRjU0NPS8/oc//EHr1q3TzJkzdfbZZw93uMc0kO9HvhcBuKPjWQWcGFWrKcllamzxVpV9gVbuWUv/CGDU60+/WFnnp9KqwD6f4U6/Nzp1y6bKykpVVlbKz89PiYmJPa/ffPPNuv3223XjjTdq2bJlCgw88C97pA8uAMAddTkMfV1QpY932rW5qEaGIVmDvHXdSUk6O92qMH+vXt/H3ocAgKHwzDPP6IEHHtDpp5+uTz/9tOf1n/70p1q+fLlWr16tCRMmaN68eSooKNDmzZsVGhqqV155xXVBH8VAvx/5XgTgbmwFNpU0lCghKEFljb1v1300fkFlskY2aV9ZorbkpeiTZJu+M5P+EcDodbR+sbE5QEWlyaptCJWXZ5umJGyXr9mn1+cYhqGShpJR/3ujUxMS7ja4AAB3Ul7fqo+y7Poky66apnaZTdKJKWE6NyNaMxNCZTb3vZqJvQ8BAM7m4eGh999/X48//rheeeUVLV++XFFRUbr22mu1dOlSjR8/3tUhHuF4qwf5XgTgDuxNdqWEpBzXM5Jj9snR5aXSygQt29yp62YYAx53AMBI0Vu/2NbupYL9yaqsiZRkKCq0QkkxRVK7RRaTpfcHmaSUkBTZm+zOD9qJhv1Qa2l0Di4AwB10OQxtLKzWhzvKeqohIgO9dc2JiTon3arwAO9+PWew1RHdWA0KAGPLiy++qBdffLHXa0uXLtXSpUt7vebj46P77rtP9913n/OCG0JUDwKAdOtJt8qQcdzPcTgM/XZFrr7Or9Lr64u15MTEY78JAEagg/vFLoeh5Zmleu3rIvk6uvStaaG67uQkpUT4S5Kqq6sVFhZ21OeZNLoTtCbDMI7/WwKS+tdg4P5oB+g2ktpCRUObPsmy6+OsMlU1HqiGmJMcpvMyojUr8ejVEIcrbSjVr22/lsVsUXRA9KBjKmssU5ejSw8ueNCtV4OOpHYA16EdQKIduLv+fD+21LfIN8i312tj5XsRB9AfQKId9EdrR5fu/m+m9lQ06ecLJ+mM1ChXhzTkaAeQaAdjRX5Fo/6wKk/5FU2yBnnrB6eP19zkQ/+7j4W24JIKCQCA8zkchjburdGKHWXatLdaDkMKD/DSt09I1MIpVkX0sxricMe7J2w3d9n7EAAAiepBAHAGH0+LfnVhun7+xjb9YVWerEE+SosJcnVYADAgDoehd7aW6OWv9sphSJfPjtdVcxPk49nH1kxujoQEALiZysb/VUPsLFPl/6ohZiWF6twp0ZqTHCbLAKohejMUe8JKcpu9DwEAOJ6zlbpxlgQA9C4iwFv3XZiuu/+bqUfez9YTV06XNaj3A18BYKSpbGzTU5/sUua+OsWG+OgXC1M10Rro6rBcioQEALgBh8PQluIafbi9TBsKD1RDhPl76eoTEnROulVRgUP3C/tQ7QnbbbTvfQgAQH+rB1ubWuVj7vs7mepBAOjdhKgA/WJRqn7zQbYeXJal318xXb5eY3NlMYDRY0dJnR5bkaPa5g6dmxGtG09LGbNVEQcjIQEATjBcx/NUN7Xrk6wyfbzTrvKGNplM0qzEUJ2bEa25Q1AN0RuTyUQSAQCAg/S3etBisshiOsoglOpBAOjTSePC9d2Tk/XiF4V6euUu3X3e5EFXpQGAMxmGofe3l+pv6wrkZTHp7vMm69QJEa4Oa8QgIQEAQ6yssUyvZr6q8+PPd8pBRAeqIWr10c4yfZ1fJYchhfh56so58Vo4JZryZQAAhll/qwf7e0ghiX8A6N2ls+K0u6JRn+VV6s1N+3TFnARXhwQAh+jscujPtj1amW1XTLCPfnVBuhLD/Vwd1ohCQgIAhtjq/NVau3etArsClZaYNmTPrWho08psu1ZmfVMNMTMhRIumROuElDB5WMxD9rMAAED/9bd60Gwyy2zi+xoABstkMumWMyeqqLpZr3y1V+OjAjQrMdTVYQGAJKmlvUu//TBbm4tqNTspVL9YlKoAb6bfD8e/EQAYQt2HWlrMFq0rWqfFDYuP61DKzi6H1hdU6+MsuzYX1cj439kQV86J1znp0YoOphoCAAAAwNjh62XRL89P023/3qrHV+Tq6atnUCUOwOVqm9v1wLIs7S5v1MJ0q368YIJTttF2ByQkAGAI2QpsqmqpUkZUhjbt2TToQyn31TTrkyy7VueUq7a5Q2aTNDc5TAvTrZrjpLMhAAAAAGA0iA3x1S8Wpeqh5Vl65P1s/e7yaRwUC8Bl7PWt+uXbO2Svb9W3T0jUt09I4IyboyAhAQBDpLs6Iso/SmaTWZH+kbIV2rQgZUG/qiRqm9u1ZleF1uRWKK+8UZJkDfLRdSfH6qzJUQoP8Hb2RwAAAACAUWFucpi+fUKiXvu6SH9Zs0e3nj3J1SEBGINK61p071vbVdXUrp8smKBzM6JdHdKIR0ICAIZId3XE1KipkqQovyjlNecdtUqitaNLX+VX6dPcCm0pqpHDkHw9LTorLUoLUqM0NS5YZqohAAAAAOAIV81JUG5Zg1Zll2t6fIgWTI5ydUgAxpCS2hb98u3tqmlq121nT6IP6icSEgAwBA6ujuguyzOZTLIGWI+okuhyGNq2r1af5pTry/wqtXY4ZDabNDspTGekRuqElDDKjQEAAADgGMxmk247e5Ju+dcWPfvpbk2IClBCmJ+rwwIwBpTUtuiet7arrrldP1+YqvmTIl0d0qhBQgIAhsDh1RHdrP5WZdoztTrfpllRF2rtrgp9trtStc0dkqTU6ECdkRqpeRMiFezn6YrQAQAAAGDUCvbz1B2LUvXLt7frsRU5euLK6fL2YIEXAOcpb2jVr94+kIy4Y9FknTYxwtUhjSokJADgOPVWHSFJhiE1NPuotf5EPbWiWRHem+Rp8VRsiI/Oy4jRGamRig3xdWHkAAAAADD6ZcQFa8mJiXrlqyI9v65AP1kwwdUhAXBTtc3tuu+dHapsbNft50wiGTEIJCQA4DgdXh3R1OKlksogFZUFqKXjQLlwh1Gt8SlVuu2MRRofGXBI4gIAAAAAcHyumJ2g7SV1WrGjTFPjgtk+BcCQa2rr1P3v7dT+2lb94PRxnBkxSCQkAOA4dFdHBHvGK780XPsrg1XbeKDqwcPUrqToasVF1KvdlK92o0v+vqfKZAp0cdQAAAAA4F7MZpN+sTBVP319i55ZvVuTrIGKDvZxdVgA3ERbZ5ceWp6l/IomXXNioi6cFuvqkEYts6sDAIDRqrWjS899tk5bdqUqM+dUZRVGq7HFS/GRtToxba/mp2/XtHFlCg9qVnSAVZXNlbIV2FwdNgAAAAC4pRA/L/18YapaOrr0xMe56nIYrg4JgBswDEN/WJmnnfvrdfGMWF01N8HVIY1qVEgAwAA4HIaySuu1MtuuT3NLtae6XWazVQkRDYqLqFNUaKMs5gO/9LbUf/M+k8kka4BVtkKbFqQsUExgjIs+AQAAAAC4rxkJIbp4Rqze3bpf/95QrCUnJro6JACj3L83FGtdXqVOGR+uG05NYRvu40RCAgD6obKxTR/vtGt1jl32+jZJkodXtSIiN+uk8cHy9jz2yhurv1WZ9kzZCmxaMm2Js0MGAAAAgDHpOycna9u+Ov17Q5FmJYVocnSQq0MCMEp9sbtSr35dpJQIf912ziSZzSQjjhdbNgFAHxwOQ5uLavTI+1m68cUNen19kdq7DF02K05LL4lXRMwHSo1v7lcyQjq0SqK0odTJ0QMAAADA2OTlYdYdC1NlMZv0+492qaW9y9UhARiF9lQ06slPdinEz1O/ujBNPp4WV4fkFqiQAIDD1Ld2aFW2XR9uL1NpXatMJmlWYqjOy4jWnOQwWcwmvZb5mqpaqjQ1auqAnk2VBAAAAAA4X2K4n64/NUV/W5uvv67N18/OnujqkACMIjVN7XpoeZa6DEP3np+mqEAfV4fkNkhIAMD/lNS26N2tJVqVXa72ToeCfD102aw4nZsRo+jgb754ShtKZSu0Kco/asD7BnKWBAAAAAAMj8XTYrSpsFors+2amxyqUyZEuDokAKNAe6dDj3yQrarGdt1+ziSlxbDt21AiIQFgTDMMQ9mlDXp7yz59XVAtw5AmRgVo8fRYnTohQl4eR+5sZyuwDao6ohtVEgAAAADgfCaTST87e5J++vpm/Wn1bk2KDlREgLerwwIwghmGoWdW5ym3rEGXzYrTgslRrg7J7ZCQADAmORyGvsqv0ltbSpRb1iBJOiElTN+aGacpsUF9Vj4cT3VEN6okAAAAAGB4hPl76adnTtQj72fr6ZW79OBFGRxKC6BP/91cIltuhU5ICdN3Tk52dThuiYQEgDHF4TD02e5K/XtDsYqqm+VpMencjGhdND1WCWF+x3y/rcCmkoYSJQQlqKyx7Kj3tja1ysfc+x6DhmGopKGEKgkAAAAAcLKTxoVr0RSrPtpp17LM/bp4RpyrQwIwAn2dX6WXvixUYriffrEwleSlk5CQADAmdDkMrc2r0BsbirWvpkU+nmZdOitO35oZpxA/r34/x95kV0pISr/utZgsspgsvV80SSkhKbI32fv9swEAAAAAg3PjaeOUua9O//yiUNPjQ5Qc4e/qkACMIAWVTfr9x7kK9PHQry9Ml69XH/M5OG4kJAC4tS6HoU9zy/XGxmLtr22Vr6dFV86J10Uz4hTs6zng59160q0yZPTr3urqaoWFhR31HpPItgMAAACAs/l6WfTzham6881t+v3HuXryyhm9nhkIYOypbW7Xw8uz1NFl6P7FabIG9b7bBYYGCQkAbqmzyyFbboX+vaFY9vpW+XlZdPUJCbpoeqwCfQaeiOhmMpn6nUQwm8wym/gFFwAAAABGgtToQF01N1Gvry/SK1/t1Q2n9a/6HYD7au906NEPclTe0KafnjlBGXHBrg7J7ZGQAOBW2jsdWp1j13827lN5Q5sCvD10zYmJunB6rAK86fIAAAAAYCy7am6CNu2t0TtbSzQnOVTT4kNcHRIAFzEMQ899ukdZpfW6eEasFk6JdnVIYwKzcwDcQnunQ59k2fXmpmJVNrYr0MdD152cpAunxcjPi64OAAAAACBZzCb9fOEk/exfW/TkJ7v0p2/PPK4qegCj17tb92tltl2zk0J1w6lUTA0XZukAjGrtnQ59nFWmNzftU1Vju4J9PXX9Kck6f2oMBxABAAAAAI4QG+Krm+aN0zOrd+sva/bojkWTXR0SgGG2sbBa//i8QAlhvrpjUarMZs74HC4kJACMSm2dXVqxo0z/3VyimqZ2hfh56obTknVeRox8PElEAAAAAAD6tjDdqg0F1Vq7q1Jzk8t1RmqUq0MCMEyKqpr1u49y5efloV9dkC5/tvgeVvzbBjCqtHZ06aOdByoiaps7FOLnqZvmpWjRlGgSEQAAAACAfjGZTPrpmROV+/pmPffpHqXHBikq0MfVYQFwsvrWDj24PEttnQ49dPEUxYb4ujqkMYeEBIAhZxiGTKahLXWrb+3Q+5mlWp65X/UtnQrz99L/zR+nRVOs8vYgEQEAAAAAGJhgP0/97KyJemBZlp76JE+PXJLBti2AG+vocujRD3Jkr2/Vj88Yz6H2LkJCAsCQKmss06uZr+qaadcoOiD6uJ9nr2/Vu1tL9PFOu9o6HbIGeevbJyRqYXq0vDzMQxAxAAAAAGCsmpMcpvOnxuiD7aV6Z2uJLp0V7+qQADiBYRj669p87Sip0wXTYnTe1BhXhzRmkZAAMKRW56/W2r1rFRMQoyXTlgz6ObvLG/TOlv1al1chhyGNi/TXZbPideqECFlYsQIAAAAAGCLfOzVZ24pr9dKXezUjIUTjIgNcHRKAIbY8s1QrdpRpekKw/m/eOFeHM6aRkAAwZEobSmUrtMlitshWaNOClAWKCex/xrm906HP91Tq/cxS5ZY1SJKmJwTrslnxmpEQMuTbQAEAAAAA4ONp0c8XTtIv3szUEx/v0lNXzaAiH3AjGwur9fy6fMWG+Oiucyez0NXFSEgAGDK2ApuqWqqUEZWh7fbtshXY+lUlUdHQphU7SvXRTrvqWjrkaTHp7DSrLpweo/GsTAEAAAAAONlEa6CuOSFRL3+1Vy99WaibWEENuIXCyib9bkWu/Lw89OvFUxTo4+nqkMY8EhIAhkR3dUSUf5TMJrOsAdajVkl0dDm0oaBaK7PLtWlvtRyGZA3y0WWz43R2mpUvCAAAAADAsLp8drw27q3Wu1v3a3ZSqGYmhro6JADHoaapXQ8uz1J7l0MPL85QXIivq0OCSEgAGCLd1RFTo6ZKkqz+VmXaM4+oksivaNSq7HJ9uqtc9S2dMpukWUmhumBqjGYlhspM2RwAAAAAwAXMZpN+vjBVP31ti55emadnlsxksRwwSrV1dunh97NV0dCmn501URlxwa4OCf9DQgLAcTu4OqL7nAeTydRTJTEt8jTllZn1aW6F8iuaJElxIb761sx4LUiNVHiAtyvDBwAAAABA0oHK/e/PH6c/rMrTn1bv1j3nTeY8Q2CUcTgMPb0yT7vsDbp8drzOTre6OiQchIQEgON2eHWEJLW0eaipLl07S7r0g5wtCvMNl6+nRQvTrTo73arJ0YH8UgcAAAAAGHHOSovS5qIarcur1Hvb9uviGXGuDgnAALy6vkif5VXqlPHhuu6kJFeHg8OQkABwXA6ujmjr8FRpVZD2VwWput5PkmSoVV6Be/TDBWk6Z/IEeXmYXRwxAAAAAAB9M5lM+umZE7WnolF//7xQqdGBmhwd5OqwAPTDB9tL9caGYk2MCtBt50xia/ARiJlBAMfl/ZxPtXt/oPYUnqhPNk7SjoJo1TX6KDa8XnNSi3XBifkKiVynOsdGkhEAAAAAgFHB18uiu89Lk8Uk/W5FrupbO1wdEoBj+Hx3pf6yZo9iQ3z068Xp8vG0uDok9IIKCQADVtfSoS/3VOqjrCKt2tUlaba8PTwUE16v2PB6RYU2yMNi9NzffZbEgpQFigmMcV3gAAAAAAD0U0qEv350xgT9cVWenvpkl+67IJ3V1sAItX1fnX7/ca6CfT314MUZCvHzcnVI6AMJCQD90tDaoa/yq7Uur0LbimvlMKS6tir5+O3TtAQvRYc1ysPi6PW9Vn+rMu2ZshXYtGTakmGOHAAAAACAwTk7LUo7Suq0Oqdcb28p0WWz410dEoDD5Fc06qH3s+RpMeuBi6bIGuTj6pBwFOyfAqBPTW2dWp1j19L3duq6F9brj6vylLmvTrOTwnT9aRGaMP5DzZpUqPjI+j6TEdKB/Te7qyRKG0qH8RMAAABXWbNmjRYuXKioqCgFBgbqlFNO0RtvvDGgZ0yfPl0mk6nPPzk5OU6KHgCAA0wmk350xnglhvnppS8Lta241tUhAThIaV2L7n9vpzq6HLrvgnSNiwxwdUg4hkFXSKxZs0aPPPKItm7dqpaWFk2dOlW33nqrrrzyyn4/Y/r06crMzOzzenZ2tiZPnjzYEAEMQkt7l9YXVmvdrgptLqpRR5chs0makRCi0yZG6qRxYQr08dRrma+ptr1cU0Om9uu5VEkAADB2vPrqq7ruuuvk4eGhM888UxaLRatWrdJVV12lnTt36oEHHjjmM9ra2pSVlaXQ0FCdf/75vd4THBw81KEDAHAEH0+L7jl/sm5/Y5t++2GOnr56BiuwgRGgvL5Vv3x7h+pbOnTXuZM1NZ7fDUeDQSUkGGAA7sXhMLSluEarssv1dUG12jsdMpukjLhgzZsYoZPHRyjY17Pn/tKGUtkKbYryj5LJ1L/9Mw+ukuAsCQAA3Jfdbtf//d//yd/fX2vWrNGsWbMkSTk5OTrjjDP00EMP6eKLL+55vS/bt29XZ2enzjrrLL3yyivDEToAAH2KD/XTHYtS9dDyLD20PEuPXz5dvl4cmAu4SmVjm+59e7sqG9t029mTdMqECFeHhH4acEKCAQbgPkpqW7Qq267VOeWqamyXJKXHBGnepAidOj5Cof69HwBkK7CpqqVKU6P6Vx3RjSoJAADc35///Ge1tLTo7rvvPmRMMHnyZD366KO64YYb9PTTT+ull1466nM2b94sSZo9e7ZT4wUAoL/mJofpupOS9NKXe/XUyl26+9zJHHINuEB1U7t++fZ22evbdPOZE7RgcpSrQ8IADPgMie4Bxs0339zrAMMwDD399NPHfA4DDMA1Wtq79PHOMt355jb98OVN+s/GfTKbTLpqboL++p3ZeuzyabpwWmyfyYjBVEd04ywJAADc3/vvvy9JuuSSS464dskll8hkMmn58uXHfM6WLVskMV4AAIwsl8+O17yJEfpyT5X+taHY1eEAY05tc7t+9c527a9t1Q9OH6dFU6JdHRIGaMAJCQYYwOjjcBjavq9OT36yS9e98LX+tHq3dpc36vRJkXrokgw9/505uvakJMUE+x7zWd3VEVZ/66BisfpbVdlcKVuBbVDvBwAAI5dhGMrKypIkZWRkHHE9NDRU0dHRqqmpUUlJyVGf1b2Aaf/+/Tr77LMVHh6uwMBAnXnmmfroo4+GPngAAPrBZDLplrMmanykv15fXyRbbrmrQwLGjIqGNt3130wVV7fopnkpunBarKtDwiAMKCHBAAMYXcrrW/X6+iJ9/+WNuvft7bLllCsp3F8/WTBeL914on6xKFUzEkL6XWJ6PNUR3aiSAADAfdXU1Ki1tVWBgYHy9/fv9Z6YmAPnSNnt9j6f09XVpe3bt0uSrr/+elVWVur0009XYmKibDabzj33XD3xxBND/wEAAOgHH0+LfnVhusIDvPSHlXnaWlzr6pAAt1da16K7/5vZUxlx8Yw4V4eEQRrQGRL9HWCUlpbKbrcrLq73hnH4AGP69Ok6/fTTlZubK5vNJpvNpt///vf6+c9/PsCPA6C1o0tf5ldpVbZdmfvqZBhSiJ+nLp0Vp7PTrEoI8xv0s20FNpU0lCghKEFljWWDfo5hGCppKOEsCQAA3ExTU5Mkyc+v7983fH0PVGQ2Njb2eU92drZaWlrk4+OjN954Q4sXL+659u9//1vXXnut7rzzTs2fP19z587t9RltbW1qa2s75DVvb295e3v3+/MAANCXiABvPXhRhu54c5t+8362fnvZVI2LDHB1WMCoZRhGn4tfi6ub9at3dqi2uV0/O2uizk4f3K4dGBkGlJBggAGMTIZhKKesQauy7VqbV6mW9i5ZzCadPC5cZ6dbNSsxVJYhOGjL3mRXSkjK8QdsklJCUmRv6ntlJAAAGH0sFosk9auS0uFw9HktIyNDZWVlampq0rhx4w65dtVVV+mrr77S008/rWeffVb/+Mc/en3Go48+qgceeOCQ1+666y7deeedx4zNWWpqalz2szGy0BYg0Q7cQYBJumVenH67skC/emublp43XhEBvZ/H2BfaASTagb3Rrjez3tTl6ZfLGnBosmFvdYt++0mBmtq79KPTEjQr2lPV1dUuitT5XN0WwsLCnP4zBpSQYIBxdK5uMBgZhrNTrGnu0Gf5NVq7u0b76w4k6JLCfHR6RpROSQlRoM+B/8XraoembX5n0ndkyBiSZ0mSSSa+ROD2aAeQaAc4wNXtYDgGFwEBB1aGtrS09HlP97Xue/titfa98m3x4sV6+umntXHjxj7vueeee3T77bcf8tpIWMA0HP8dMDrQFiDRDtzBqWFhusPDV49/lKun15Xo0W9NU7Cf54CeQTuANLbbwYp9K7SxZqPG147XksRvdtLYVlyrR1cVqdMw6b7FGTpxXLgLoxw+7t4WBpSQYIBxbO7eYHB0ZY1len336/q/0/5P0QHRTvkZ7Z0OrS+o1spsu7YU1chhSIE+HvrW7ASdnWalRHSEoU+ARDvAAbQDSO7fDgIDAxUYGKi6ujq1tLT0VE8frLT0wBlS3WdJDEZ09IHfs5qbm/u8ZySMDQAAY8P8SZGqaW7X8+sKdN+7O/TItzIU6DOwpAQwVnWfV2oxW2QrtGlBygLFBMbo09xyPb0yT14eZi29aIqmxYe4OlQMkQElJBhgAEe3On+1vij+QuPzxw/p2QiGYSjX3qBV2eVal1ehprYumU3SrKRQnZNm1ZzkMHl5DOiMegAAgCFnMpk0ZcoUffXVV8rOztasWbMOuV5dXa2ysjKFhob2ed6cJP33v//VW2+9pTPPPFM33njjEdfz8/MlSfHx8UP7AQAAGKSLZ8SprdOhl7/cq/vf3amHLsmQv/eApt2AMclWYFNVS5UyojK03b5dq/Nt8umcrxe/KFR4gJeWLp6i5IjezzLG6DSgGczuAYZ04ByIww1kgHHNNdfohRde6PU6AwyMRodndEsbSo/7meUNrXpjQ7F++Mom3fGfTK3YUaaIAG/dcFqyXvzeCbp/8RSdMiGCZAQAABgxzjvvPEnSO++8c8S1d955R4Zh6Pzzzz/qM6qrq/Xaa6/pmWeekWEcuV3kP//5T0nSokWLjj9gAACGyJVzEnT1CQnKK2/U0vd2qqW9y9UhASNa91xalH+UzCazovytevGLYv1tXZ4Sw/30+yumk4xwQwOexWSAAfSuO6ObFpGmyuZK2Qpsg3pOQ2uHVmbZ9cu3t+umf27Uy1/tVVNbly6aHqunr56hP317pr41M16h/gM7KAsAAGA43HDDDfLz89OTTz6pL774ouf13Nxc/fKXv5Qk3XHHHT2vl5aWKicnp6fSWpKuuOIKhYWFaevWrXrwwQcPGTP87W9/05tvvqmoqCj98Ic/HIZPBABA/y05IVGXzopTTlmDlr63U83tna4OCRixuufSrP5WdXSaVVQ8V/vKrQoMqNFjl01TRAC747ijASckGGAARzo8o2sNsA6oSqKprVO2nHI9sGynrnthvf6wKk8799fr5HHh+tUFaXrxe3P1f/PHaXxkQL8OlQcAAHCV+Ph4/fGPf1Rzc7Pmz5+vc845RxdeeKFmzJihsrIyPfroo5o+fXrP/ffcc4/S0tJ0zz339LwWEhKil19+Wd7e3lq6dKkmT56syy+/XNOnT9f3v/99BQQE6K233nL7MzkAAKOPyWTS9ack6+IZscoqrdev3t6hupYOV4cFjDgHz6U1tXrrs+0pKq8NVFxktYIiVqihvcLVIcJJBpyQYIABHOngjK4kWf2tx6ySaGnvki23XA8tz9K1L3ytJz/Zpc17azQtPlg/PXOCXrrxBN1zfppOHBcuDwtbMgEAgNHjxhtv1Icffqh58+bpq6++0meffaZZs2bpv//9r+6+++5+PeP888/Xxo0bdfXVV6uurk7vvfeeqqqqdMMNNygzM1Onnnqqkz8FAACDYzKZdONpKT3bN9371nZVNbb1em9vO4cAY0H3XJq5Y5w+256iplYvTUku0ymT61TdWjHonUcw8g3qdJ0bb7xR8fHx+u1vf6uvvvpKFotFs2bN0s9//nNdeuml/XpG9wDjkUcekc1m03vvvaeoqCjdcMMN+tWvfqWUlJTBhAYMu4Mzut3VCyaTqadKYkHKAsUEHjjkvaW9SxsKq/XZ7kptLKxWR5chs0maGh+s0yZE6OTxEQr29XTlxwEAABgSixYt6tcWrC+++KJefPHFXq9lZGTo9ddfH+LIAABwPpPJpGtOTJK/l4de+KxAd/03Uw9fMlXRwT4995Q1lulvG/6m/zvt/xQdEO3CaIHhVdpQqtUFNnU2zdT6/CRZzA6dMLlIUaFNknqfU4P7GFRCQmKAAXTrzuhOjZp6yOtWf6sy7Zn6OM+m8YELtW53hTYW1qi90yGTSZoSG6z5EyN08vhwhfhxHgQAAAAAAO7mkplx8vWy6M+23brjzW369YXpmmgNlCStzl+tL4q/0Pj88VoybYmLIwWGzye7bdpZkKD25lQF+LZp7uRiBfi291zvnlOzFdj4f8MNDTohAaD36ghJ6nKYVF4TqNrK0/XUnk5F+2+Xl4enpsQG6dQJETp1fASHUgMAAAAAMAYsmhKtQG8P/f7jXN391nb9YmGqkqPaZSu0yWK2sBIcY0qOvVh/sdWqqXm84sIaNWvSPnl6OA65p6+dR+AeSEgAx+Hg6oguh0kVtf4q2m9VVVOoOrsOnPtg9tynGeN9dceCixQe4O3iiAEAAAAAwHA7ZUKEfhPgpYeXZ+vRD7M1ObFUlc1VSotI0+7m3awEx5iwu7xRt/xrk2ob/DQlqU7pSeU6aH3vIaiScF+clAsMUmlDqVYVfCrPrlRt2xOnjzdM0oacRJXWhCrQr1VTkst09uxdOiF9l6qMj9VuVLs6ZAAAAAAA4CKTo4P0xJXTFR5g0ofb2tRYc4pkWHpWgpc2lLo6RMBp1u6q0G1vbJS9sV5pKfmaktx3MkI6tEqC/zfcCwkJYIAcDkPb99Xp/uVfaP32udpdOFPF5SHy9+lQWpJd89J26rSphRoXWy1f705Z/a2qbK6UrcDm6tABAAAAAIALWYN8NG/qXnn7lqm6JlEb8ycq2DOOeQO4LYfD0MtfFurxj3LV2lWr2Lg1ykg4SibiIMypuSe2bAL6qayuVaty7FqdXa6S2kaVNLTI39fQuOhyxYbX9xy+01LfLsm3533sewcAAAAAAKQDuy18WWLT7FSLqiv9tXtfqD7bPk7JCfXMG8DttLR36YmPc/V1QbXiwixq8f5Evj6eh5zDejTMqbknKiSAo2hp79InWXbd81am/u+ljfrX+mJ1OgxNiKtSYtInWjS7RJPiK3uSEX0howsAAAAAALrPoowOsGpKil1TEwvV0WnRrvw52r3fn3kDuA17fat+8eY2fV1QrdMnReqU9AI1dJXK6m8d0HOYU3M/VEgAvdhb1aQPtpfJllOulo4ueVpMmjcxQmelWRUd2qKln/5NCb6+ZHQBAAAAAEC/lDaUylZoU5R/VM98QkxojcLDpQ25CaqqnKvn1+3XyfElSgmLc3G0wODtKKnTbz7IVmNbp757SrJOmWjW/Z8e2vb7izk190OFBPA/HV0Ord1VoXveytTNr23RB9tLFR3sox+fMV4v3Xii7jx3smYnhWpN4aeqaqkiowsAAAAAAPqtuzri8PmE4IBWzZuWr9iwVpVWRuin/1qvoqpmF0UJHJ8VO0r1y3d2qLPL0C/PT9Pls+P16SDn0roxp+ZeqJDAmNfY1qnl2/br/e2lqm3ukIfFpAWpkTpvaowmRwcekrntbTVDf5HRBQAAAABgbDrWfIK3Z5dOTCuWp7eH9pZ66ObX1+uWM9N0dvrgJnCB4dblMPS3dfl6P7NU1iBv3XdhupLC/Y9rLq0bc2ruhYQExqy65g69u61EyzNL1dLepchAb333lGSdk2ZVsJ9nr+/pXs0wNWrqoH6m1d+qTHumbAU2LZm25HjCBwAAAAAAo0R/5hNMJmnWuA61m2xqbVisP6zKU2ZJnX58xnj5eFqGMVpgYBpaO/TYihxtK65TRlyw7j5vsoJ9D8yt2QpsKmkoUUJQgsoaywb9MwzDUElDCXNqboCEBMacqsY2vb2lRCt2lKmt06G4EF9deXq85k+MlIel713MyOgCAAAAAICBGsh8gslkUkqUp1pDPlaUcb1sOeXKszfojkWpGhcZMEwRA/1XXN2sh5ZnqbSuVedmROsH88cdMr9mb7IrJSTl+H+QSUoJSZG9yX78z4JLkZDAmGGvb9Wbm/ZpZbZdnV2GkiP8deWceJ06PkJm87ETDP3N6LY2tcrH7NPndTK6AAAAAACMHQPdbcHqb1VmY6bOTtutE5Pn66Wv9uoX/9mm609N0eJpMYNeJAkMtc1FNfrthzlq6+jSD08frwumHbnw9taTbpUhY8h+pkm0/9GOhATc3r6aZv1n4z59mlsuhyFNsgbqqrkJmpscOqAv8f5mdC0miyymo5RSktEFAAAAAGBMGMxuC927K6zZa9ODCxZoavw0Pf5Rrv62Nl9bimp061mT+txqGhguK3aU6blPd8vPy0MPXJyhGQkhvd5nMplIIuAQJCTgtgoqm/SfjcX6bHelDEPKiAvWVXMTND0+eFCrCfqb0a2urlZYWNgx76MzBgAAAADAvQ32LMrDz6D849Uz9dynu2XLrdDNr2/W7edM0szEUCdFDfTN4TD0ytd79Z+N+2QN8tbSi6YoPtTP1WFhFCEhAbezy96gf28o1vqCaknS7KRQXTEnXlNig4/ruf3N6JpNZplNfZ9FAQAAAAAA3N/xnEXZ2xmUty9M1cykUD1n26Nfv7tTl86K07UnJcnzKOdhAkOpvdOhp1fu0rq8Sk2yBuq+C9MU4ufl6rAwypCQgNvYUVKnf28o1tbiWknSyePDdeWceE2ICnRtYAAAAAAAYMwZbHVEt8OrJCRpQWqU0qKD9PhHuXprc4ky99XpF4tSFRfiO5ShA0eob+3QI8uzlVVar5PHh+v2cybJx/MoW5YDfSAhgVHNMAxtLqrVfzYWa+f+eplN0umTInXFnHglhfu7OjwAAAAAADAGHU91RLfeqiQkKTrYR49dNlWvry/Sfzbt063/2qIfzB+vs9IG/7OAo9lf26IHlu3U/tpWXTwjVjecmiKzmbaGwSEhgVHJ4TC0vrBab2woVl55o/4/e/cdX2V5/3/8fc7J3nsQSNghELYLZIsiCO7W2qJ+v1rrT2utWquiX/e35dvhqLXL0dpWtLXOAooDwlCkIjvMhBXI3nuf6/dHTDQmgSTk5E5yXs/HIw/lvu5znc/NubhzrutzX9dlt9s0PylaV581mKcCAAAAAACApVKOpiizPFNDgoYopyKnw/NqKmvkY/fpsNwYo8zyzFazJCTJw2HXddOGauKQED310SH9Zm2atmcU64dzR8rfm+E+9Jz92WX639X7VFHToFtmD9fiCYOsDgn9HHco9CuNTqNP0wv0zy9OKKOwSp4Omy6ZEKsrJ8cpKqjjX+AAAAAAAAC9JbcyV8NChp32PIfNIYftFMve2KRhIcOUW5nbbvGEwSF69trJ+u3aNG1KK9Ch3HL95KJEJcUGdTd0oMWn6QV68sODsttsemBRks4dHm51SBgASEigX6hrcGrdgVy9uT1TOaU18vG064rJcbp8cpzC/Nk8BwAAAAAA9B13nnenjMxpzysqKlJYWNhpz7Op4+Vxgnw89cCiJK1JzdELm47o/jd369pz4vXts4awrA66xRijd3dm6aVPjirEz1OPLBnLHq3oMSQk0KOMMT26XmFlbYPeT83RuzszVVJVrwBvD117TrwumRCrYF/PHnsfAAAAAACAnmKz2U6ZRGhmt9llt9l75P0Wjo/VuEHB+uUHB7TiPxnadbJEd1+YqMhA7zOuH+7D6TR68ZMjWrkrW0PCfPXIknGKZlUS9CASEugxORU5WrF7hb434XuKCYg5o7qKKuu0aneWVu3OVnVdo8IDvPT9mcN00dgY+XqdYiojAAAAAACAm4oP99NT356kv3x6VKt2Z+uO13boR/NGavrICKtDQz9Q29CoJz88pM8OFyo5LkgPLEpSoA8PBKNnkZBAj1l3ZJ02Ht+o2IDYVhstdZYxRgdzy7VqV7Y+SS9Qo9MoLsRXV80crDmJkfJ0nPkTAwAAAAAAAAOZl4ddt8weocnxofrN2kNa/v4BXZwco5tmDJOPJw95on2l1fX631X7dCCnXDNHRejO+aPl5cFYHHoeCQn0iOzybKUcS5HD7lDKsRTNHTZXsYGxnXptXYNTn6YXaOWuLKXlVUiSxg8O1pIJg3TusDDWOwQAAAAAAOiic4aF6dnvTNbTHx/SmtQcpWaW6qcLEjU8MsDq0NDHZJdW69F/71VWSY2unBKnG6YNZTwOLkNCAj0i5WiKCqsLlRyVrD25e5RyNOW0syQyS6r10d4crT2Qp5Kqenl52HVxcowuGR+roRH+vRQ5AAAAAADAwBQe4K3HL03W2zsy9bctx3X367t03XkJumJyHAPOkCQdyi3X4yv3qbymXrfMHq7FEwZZHRIGOBISOGPNsyOi/KNkt9kVHRDd4SyJmvpGfZJWoI/352pvVpkkKTrIW/99/lBdODaadekAAAAAAAB6kN1u01VTB2vikBD9+oODennzMX1xvFh3XThKUYFsVuzO/nOkUL/84KAk6YFFSTp3eLjFEcEdkJDAGWueHTE+arwkKdo/Wrtzd7fMknA6jVKzSrXxUL42HipQdX2jPB02zUmM1PykaI2PCyYrDwAAAAAA4EIjowL0zHcm6S+fHtN7e7L1o1d36La5IzV7dKTVoaGXGWO0ane2Xtx0RIE+nnpo8VglxgRaHRbcBAkJnJGvz46w2ZqSCjabTVH+0Xpnzw5lZCVpb2adSqrqJUkjIv114dgYzRodwWwIAAAAAACAXuTj6dCtc0bo7KGh+s3aNP36g4P64liRbpk9QgHeDBO6g4ZGp/608YjWpOZoUIiPHr10nGKDfa0OC26EOw3OyNdnRziNVFTmr6yCIGUXjVZJVZ0yMzM0MW6wLk6O0YyREUoIZ28IAAAAAAAAK501NEy/vXayfrsuXesP5mtvVpnuvnC0kuOCrQ4NLlReU6//e/+Adp8s1cQhwbrv4jE8MIxeR0IC3ZZdnq11R1Pk5RytPUcGKbsoUHX1TU0q0K9Ww+OyFBK0Qw8tvL/NXhIAAAAAAACwToifl/7nkiR9sDdHL246qgfe3qPFE2J1/bSh8vF0WB0eetiJoio9sWqfsktrtGh8rG6eOUweDrvVYcENkZBAlzU6jfZklur3n2zWf46cLS9biKSmJMTQmGINCi9ToF+tjDHanZvRspcEAAAAAAAA+g6bzaaLk2M1fnCInvnokFbuytbnR4v0o3mjNHFIiNXhoYdsO16sX645oJr6Rt0ye7gWTxhkdUhwYyQk0CnNSYhP0vL12ZFCFVZUK7O8Wv6+Ng2Lzm9JQnydzWZTdEC0Uo6laO6wucySAAAAAAAA6IPiQnz1i6smaNWebP1t8zH9zzupujg5Rv81faj82Vui33I6jV7/4oRe/TxDvp4OPXrpOE2OD7U6LLg57ijoUKPTaPfJEn2aXqDPjhSqrLpBkhQf5qfIsBNyVHyks4YMbdnMuj3R/tHanbubWRIAAAAAAAB9mN1u06UTB+mcoWH67bo0rUnN0dZjRfrBrOGaNjz8lOM/6HvKa+r15IeHtO14sYZG+GvZwjEaFMLm1bAeCQm08vWZEJsPF6q85sskRLifFk8YpBkjI+ThWaqHU/6gIWG+p/1lxCwJAAAAAACA/iMm2Ef/e3myPtibqz9/elTL3zugKfEh+sHsEYpjQLtfSMst1y/WHFBuWa3mjonSbXNGsC8I+gwSEpDTabQ3q0yb0vO1Ob1QpdX1kpqSEEsmNiUhhoT5tZz/6u53VFhdqPFR4ztVP7MkAAAAAAAA+o+mvSVidN7wMP3l02NadyBPP1yxXVdOidO3zxrC4HYf5XQavbH9pFb8J0N2m/TDuSO0YFwMs1vQp5CQcFNOp9GBnHJtSsvXp4cLVVxZJ0kaEuarReNjNXNU6yREs+zybKUcS1GUf1Snb2bMkgAAAAAAAOh/Qvy8dNeFo7VgXIz+uOGw/vXFSa07kKfvnhOvC5Ki5bAz0N1X5JXV6KmPDmlvVpniw/z0k4tGa3hkgNVhAW2QkHAjxhgdyq3QprR8fZJeoMKKpiREbLCPvn3WYM0cFamEcL9TJhpSjqZ0aXZEM2ZJAAAAAAAA9E9jBwXp6Wsm6f3UbL32eYZ+uy5d7+zM1PXThurcYWE8gW8hY4w+3p+nFzYdUXVdo5ZMjNUN04fK24NZLOibSEgMcMYYHS2o1MZDTUmI3LJaSVJ0kLeunBKnmaMiNSLSv1O/OLozO6IZsyQAAAAAAAD6L4fdpsUTBmnemCi9tT1T7+zI1M9W71diTKC+fdYQnT00tFuJCWMMCY1uyiyp1u9S0rXnZKlC/b1038VjNDUh1OqwgFMiITFAZZZUa8PBfG1Ky9fJ4mpJUniAly6bNEizRkdqVFRAl2/23Z0d0YxZEgAAAAAAAP2bn5eHlp6XoEXjY/WPrRn6aF+unli1T0Mj/PWtqYM1Y2SE7J1cyimnIkcrdq/Q9yZ8TzEBMS6OfOCobWjU29sz9foXJ1TfaHRxcoxumD5UAd4M9aLvo5UOIPnltdp4qCkJcTi/UpIU7OupSyY07QmRFBPU6V8I33QmsyOaMUsCAAAAAABgYAjz99Jtc0bq22cN0Ts7MrUmNUe/+uCg/vbZMS1MjtWF46IV5ON5yjrWHVmnjcc3KjYglodXO8EYo41pBXr506MqqKjT4FBf/XDuSCXHBVsdGtBpJCT6uZKqOn2aXqiNh/K1L7tMkuTn5dD8pGjNHB2hiYNDemSDoZSjKcosz9SQoCHKqcjpdj3GGGWWZzJLAgAAAAAAYACICPDW92cO17fOGqKVu7L0wd4cvbz5mFb857hmj47SguRoJUYHtnnAtfnhV4fdwcOrp2GM0Y4TJVqxJUOHcsvl7+3Q92cO06LxsfJ02K0OD+gSEhI9yBjTK+9TVFmn/xwp1ObDhdp9skROI3l52DVjVIRmjYrU1IRQeXn07M0otzJXw0KGnXlFNmlYyDDlVuaeeV0AAAAAAADoE4J9PbX0vAR9+6wh+vRwgd7bna2P9+fq4/25ig320ZzEKM0dE6nYYF9JXy0NnhyVrD25e9z64dWOxhSNMdqeUaJ/fJ6hAznlcthtumRCrL57bvxpZ58AfRUJiR6SU5GjF7a+oJtn3OySNe9ySmv02ZECbU4v1MHcchkjeThsmpoQplmjI3TusHD5ejl6/H2b3XnenTLquYSLTWxWBAAAAAAAMNB4edg1NzFKcxOjdDi/QikH8rThUL5e+zxDr32eoRGR/kqM9dCmnK2K9IuS3WZ36yW+2xtTrG1oVMqBfK3cnaWMwio57DZdnByjb501WFGBPhZHDJwZEhI9ZN2Rddp8YrNGHBnRI9ncRqfRgZwybT9erK3HinW0oGlPCB9Pu6aPiND0EeE6a2io/Lx65yO02WwkEQAAAAAAANBpIyIDNCIyQP99/jDtPFGsDQfztfVYsbZmZKi4eqJC/T2VH1auiKBA5dR87pazJJrHFIcfHqHzBl2qlAN5WncgT+U1DfL1dOjSiYN02eRBJCIwYJCQ6AE9teZdXnmNth8v0Y6MYu08UaKqukZJUoC3hy5IitK04eGaFB8ibw/XzYQAAAAAAAAAepLD3rTKx9SEMJ0szdJPVr+hgPIoVVUO0vGcMB3PCVO9M0a/yS5XZs4OnTt0sEZGBSg22KfN3hMDSVZZllYf+I/Kysfp2Q/L9XfPrfJ0eGpQiI+uPSde85OiXboiCmAFEhI9oHnNu6SIJKVXpXcqm2uMUVZpjVIzS7U3q0z7skqVW1YrSbLbpFHRgZoSH6qpCaEaFRUgew9sTA0AAAD0lg0bNuhnP/uZdu7cqerqao0fP1533nmnvv3tb3e6jrKyMv3yl7/UG2+8oePHjys8PFxLlizRY489pqioKBdGDwAAXGXj8fWqdxzReaP9JaWrqsZLBWV+Kij107F8o3d2ZmjjwaaVQvy9HRoZFaBRUYEaFuGvIWF+igvx7fG9U3tTRW2D9meXaUdGsd7atUcZRZPlbfdRva1ckwYX6a65F7a7CTgwUHQ7IUEHo0nz7Igo/yjZnR2veVdYUav0vAodzq9Uel6F0vLKVVJV31I+KMRHF46N1qQhIZoUH8LGNAAAAOi3VqxYoeuuu04eHh6aN2+eHA6H1q5dq2uuuUZ79+7VY489dto6ysvLNXfuXG3fvl0jRozQ4sWLtWfPHv3xj3/UypUrtWXLFg0ePLgXrgYAAPSUr4+jNQ+4+/vWyd+3TgnRJRoUm6PKmn26YuRtKir3UlpeufZnl2vXidKWOuw2KSrIR0NC/TQkzFexwb6KDvJWVJCPIgO8+1SyorahUSeKqnS0oErpeRXam1WqjKIqGSPVN9aroLZQQ6KLNCSwVvbATDnVoGD/6bLZgqwOHXCZbiUk6GB8pXl2xPio8aouq1GI5yDtyMzQbzds0tCgqTpZXK2jBZWtkg92u03Dwv00c1SExg0K1tjYIIX6e1l4FQAAAEDPyM3N1c033yx/f39t2LBBU6ZMkSQdOHBAc+bM0RNPPKHLLrus5XhHHnnkEW3fvl3XX3+9XnrpJXl4eMjpdOqee+7R008/rR/+8Id69913e+OSAABAD/n6OFp7YgKitbtyt2od23XzrKbVRxqdRieKqnSssFIniqt1sqhKJ4qrtC2jWFuPFbWpI8TPU9FBPgr391KQr6dC/bwU7OupUD9PBfl6KtjXU35eDvl5ecjH097tmQhOp1FlXYPKahpUXlOvwoo65ZbVKK+8VrllNcoqqVZOaY2c5qvXhPl7acbICI0dFKSj5Ru17sRqTYger5ryGvkERml37m633EcD7sVmjDGnP+0rubm5GjZsmBwOR7sdjLy8PH3xxRen7WDcfffdevrppzvsYFx66aV9voORXZ6th1MelsPuUEnReB05GaJGeareWS9jjOIC4+Tn5aX4MD+NjArQyKimjXwSwv37VLYWPauoqEhhYWFWh4E+gLYAiXaAJrQDSO7TDh5++GE98cQTuv/++7V8+fJWZX/5y19044036rrrrtPf/va3DusoKyvToEGDZIzRyZMnFRoa2lLW2Nio0aNH68iRI0pPT9eIESNcdi09zV3aAE6PtgCJdoAm7tQOvj6OFhMQ0+F5ORU5anQ26vG5j59yj9aGRqeyS2taJQHyy2uVW1arvPIalVXXt0oGtMduk7w9HfLzcsjbwy4Pu10Ou00Ou012m00Ou76czeBUfaP58r9O1dQ7VVnXoI5GVT0cNkUFemtouL+GRvhraLi/hkf6KyrQWzabrc3fRXVZtXyDfDt97Ri43OGe0OUZEr/73e9UXV2t+++/v1XSYcyYMVq+fLluvPFGPfPMM6ftYDz//PPy8/PTM888Iw+PpjDsdrt+9atf6d1339W///1vHT58uE93ML6e1a0odSrAp1ohwRXy96lVTs1eXT5ujn5wznfY/wEAAABuY/Xq1ZKkyy+/vE3Z5ZdfrptuukmrVq06ZR0bNmxQZWWlLr744lbJCElyOBxasmSJfvOb32j16tW64447eix2AADgOqebHdEs2j+6UzMFPBx2DQnz05Awv3bLnU6jspp6lVTVq6S6XiVVdSqtrldZdb2q6hpVVdeo6vpGVdU1qKquUbX1TjmNUf2X/21wGjU6jWw2m7wcNnnY7fL2dCjA20PennYF+XgqwNtDgT4eCvTxVESgt6K+/An18zrleGBHfxedvXagP+tyQoIORpNvrnk3Mq5QcYFN2UxJ8q+waVveOuVWziGjCQAAALdgjNG+ffskScnJyW3KQ0NDFRMTo+zsbGVmZiouLq7devbu3dthHZI0duxYSdKePXt6ImwAAOBi7e0d0RGbzdbhHq1dYbfbFOLnpRC/vrVM+qn+Lnrq2oG+rEvrBnW2g1FcXKzMzMwO6xkIHYzmTGa0f3S75dH+0SqoKlDK0ZRejgwAAACwRnFxsWpqahQYGCh/f/92z4mNbepY5+bmdlhPVlZWq3O7UwcAAOg7TjeO9k0DeVyNMUW4uy4lJOhgNOlMVvfrGc3s8uxejhAAAADofZWVlZIkP7/2l06QJF/fphnFFRUV3a6nM3XU1taqrKys1U9tbe2pLwAAAPS4rsyOaDZQx9UYUwS6uGRTX+tgfLND4e3tLW9v7w5f01N6es07AAAAYCBwOByS1KnBBqfTecb1nKqO5cuX67HHHmt17L777tO999572thcpbi42LL3Rt9CW4BEO0ATd2gHK/euVHZ+tsZGjFV1WXWnXxdkgrQ3f69W7lypq8dd7cIIe09HfxfV5a3/XgbitaNzrL4n9MaG2l1KSNDBkHLKc/T+nvcVZA9STXlNq7Jv3jwkKdgE6/0972tS8CTFBMa4NDb0DVbfONB30BYg0Q7QhHYAyfp20Budi4CAAElSdXXHgw3NZc3ndqeeztSxbNky3X333a2O9dYDTKfSG58D+gfaAiTaAZoM5HaQXZ6trcVbNTh6sPwCOn7AuSNDHEO0tXirlngu6ff7KZzu76J5X9pmA+na0TUD+Z4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O5Zbrn1tPKD7MT7NGR2h+UnSrJ39PpzcGIAH0D129N3YlSZlbVqM3tp3U2v25qm80ig7y1rXnxOuCpKhO7/PQXfHhfrouPEHfOydeW48V6Z2dmUo5kKeNh/K1eEKsCky+hoUM61RdDptDDlsHMzZs0rCQYcqtzO3B6AEAAAAA6HtsxpgOtnlsq7CwUPHx8aqqqtLEiRNbll5qz5NPPqno6GjNmTNHGzZs0COPPKJHH320pfzEiROaNm2aMjMzNWbMGCUnJ+vzzz9XRkaGpk6dqvXr159yU+u+qKioiI0KB7Ds8mw9nPKwHHaHYgJiOjyvuqy63Y1Lm+VU5KjR2ajH5z6uCL9orUnN0VvbT6qgok4+nnadPzJC88ZEnXLN8jPldBrtzSrTxrR8fZpeoPKaBtntNk0fEa5LJw5SUmzQaet4dferemP/Gx1uXvtNxhjtzt2tb439lts8Acw9AdLAbwedvTd+09fvhe0lKUur6vX6Fye0ek+2Gp1GiTGBumrKYJ07LMxl98bOSM0s1Z8/Oaq0vAoF+XjoB7OHa+aoiNO+rjPtwCbbGc3yQN830O8H/QGfAbqLtoPuou2gu2g76C7aDvq6Ls2Q2LBhg6qqmpaQ2bVrl3bt2tXhuY8++qiio6M7LB8yZIg+//xzPfLII1q9erVWrlyphIQEPfDAA7rvvvv6XTICA19nl+Y41bIcUtPA/MmyTL24ZaNOZI9QdmmNQvw8dd15CVo4PkaBPp6uCL8Vu92m8YODNX5wsG6ZNVxbjxVr9Z4sfZJWoE/SCjQ5PkTXnhPfYWLCyo29AfQtPb2vjtNptGZvjl7efEzVdY0aFRWgG6YP1YTBwX1isD45Lli//tZEbUjL14ubjujXHxzS5sOFum3OSAX7dnz/ttvsstvsvRgpAAAAAAB9T5cSEldeeaW6MKFCkrR+/foOywYNGqQXXnihS/UBVsmtzO3U0hynXJZDUm29l6qLLtHKEx4aGlqv66Yl6LJJg+Tt0fnNV3uSh8OuaSPCNW1EuDIKq/SvbSe08VC+dmSU6LzhYbppxnDFBLdOsPTmxt4A+q6e3lfneGGlfrsuXQdzyhUR4KU75o3S+SPD+0Qi4uvsdpvmJkZp8pAQ/WHDYW1OL1RaboUeWJSkkVE8UAEAAAAAQEe6vIcE4K7uPO9OGZ0+IdfR1DhjjFIO5uuFjUcVYGvQpVOjdOOMYQrx83JFuN0SH+6nn1yUqG+fNUSv/Oe4NqcXatvxYl01dbC+NXWIvDzsvbKxN4D+oaf21Vl3JEXBtjn68ydH1eg0umzSIH3v3AT5elmTqO2sED8vLVuYpI/25eoP69N17xu7dPu8kZo3puMZogAAAAAAuDMSEkAn2Ww22XT6Afj2luWoa3DqD+uP6OP9uQrx89RPLhqnc4b13fX8hoT5adnCJO0+WaI/bTyif3x+Qp8dLtTdF47WlmzXbOwNoH85k+RkM5vNpnCfOP1pfb78zEENDg3UPQtGa0zM6fex6UsuHButoeF++tl7+/X0R2kqKK/Tt84a3OdmdgAAAAAAYDUSEoCLFVTU6ufv7VdaboWmJoTq7otGK6gX9onoCRMGh+g310zSv7ad1D+2ntDtr22VzS9dg6PPbACSWRJA/9fZfXVOparGVweOTFJZtUPThhfp2Wtmys+rf341GRUdqGeumaSH392rv285rvLaBt14/lCSEgAAAAAAfE3/7PUD/URGYZUeejdVRZV1+vZZg/W9cxNkt/evwSkPh13XnhOvs4eG6c5/faz0zCGqrA5WY/wheTgau1WnMUaZ5ZnMkgD6sc7uq9OR4rIQHTiWqMZGh5ISjmjCyIB+m4xoFuLnpZ9fOV6Pr9yrd3Zkqqa+UbfNGUFSAgAAAACAL/Xvnj/Qh6XlluuRf+9VZV2j7lmQqNmjI60O6YyMjArQrAnHVL3LroKSSKUeCtDYEfvk613T9cps0rCQYcqtzO35QAH0is7uq9OeTYfy9dRHaRod7tC9CxI1KT6kU0vi9QcB3h56/LJk/e/qfVqTmqMAbw/dMH2o1WEBAAAAANAnkJAAXGBfVpke/fdeNRqjhxcnaWpC390voit+OuPH+sn5Tr27M0svbz6uuqJxun/JWI2MCuhWfQNlABJwR53dV+ebPtybo+dS0hXq56X/vTxZQ8L8XBCdtXw8HfqfS8bqf95J1RvbTirQx0Nzhg686wQAAAAAoKvspz8FQFccL6rWYyv3SpIeu3TcgElGSE0DkA67Q1dOGaJlC5NUVdeo/3l7r3adKG3ZzLsrPyxjAriX9/Zk67fr0hUV6K1fXDVhQCYjmvl4OvTIkrGKD/PTXz49ps+OllgdEgAAAAAAliMhAfSgzJJq/eLjo6prdOrBS5KUHBdsdUguM21EuJ64PFl2u/TEqn3anlFsdUgA+rANh/L1xw2HNSjER/931QTFBPtYHZLLBfp46rHLxinU30vPbz6pI/kVVocEAAAAAIClSEgAPaS0ql6PvJuq8poG3btgjCYOCbE6JJcbNyhYP7tivLw87PrfVfu0+2SJ1SEB6IO+OFakpz46pDB/Lz1xWbIiArytDqnXRAR464FFY+Q0Rj9bvV+l1fVWhwQAAAAAgGVISAA9oKHRqf9bs1+5ZbX67/PiNG1EuNUh9ZoRkQF64rJkeTjsenzlPu3PLrM6JAB9SHpeuZa/f0D+Xg49cVmyooIG/syIbxoTE6T/OjdOeeW1+uWaA3I6u7cZOAAAAAAA/R0JCaAHvPjJUaVmlmnxhFjNG+0+yYhmo6ID9dil4yQ1Ld+UVVJtcUQA+oLiyjr97+r9MsbokSXjBvSeEaczd1SYLk6O0e6TpXprR6bV4QAAAAAAYAkSEsAZ+mhfrlbvztb4wcG6acYwq8OxTFJskH66IFGVtQ16bOVeldewLAngzuoanPrZe/tVWFGnOy4YpcSYQKtDstxNM4ZpcKiv/r7luNLzyq0OBwAAAACAXkdCAjgDJ4ur9KcNhxUR4KX7Lh4jD4d7/5M6d3i4vj9zuLJKavTz9/arodFpdUgALPKH9Yd1MKdcV08drDmJUVaH0yf4eDp0z4JE2ST9+oNDqqlvtDokAAAAAAB6lXuPngJnoL7RqSc/PKT6Rqd+clGign09rQ6pT1gycZAWT4hVamaZXt58zOpwAFhg/cE8fbw/V1MTQnXdeQlWh9OnjIgM0HXnJSizpFp/5R4JAAAAAHAzJCSAbnply3Gl51Xo6qmDlRwXbHU4fcpNM4YpKTZQ7+7M0ub0AqvDAdCLskur9fuUwwrx89Sd80fJbrdZHVKfc8XkOCXFBmr1nmwdymXpJgAAAACA+yAhAXTD3qxSvb0jU6OiA3TtOfFWh9PneDjsuvfiMQry9dAza9PY5BpwE/WNTv1qzUHVNDTqJxclKsTPy+qQ+iS73abb546SzWbTc+vS1eg0VocEAAAAAECvICEBdFFdg1PPrUuXh92mn1yU6Pb7RnQkIsBb91yUqJr6Rv36g4MMuAFu4PUvTigtr0JXTo7TpCEhVofTp8WH++nqKXE6WlCpd3dmWh0OAAAAAAC9gpFUoIve2HZSJ4ur9Z2z4xUX4mt1OH3a5PhQXTk5Tml5FfrXFyesDgeACx0tqNTrX5xUQrifvse+EZ3y7bOHKDbYR6/+J0N55TVWhwMAAAAAgMuRkAC64ERRlV7/4oTiw/10xZQ4q8PpF757boLiw/302tYTOpxfYXU4AFyg0Wn07No0yRj9+IJR8mTmWKd4ezh0y+wRqm1w6pXPjlsdDgAAAAAALseIAdBJxhj9fn26nMboR/NGMuDWSV4edt194WhJ0lMfHVJdg9PiiAD0tHd2ZCo9r0KXT47TqOhAq8PpV6YmhGpqQqhSDuazwTUAAAAAYMBjRBXopE/TC5WaWaYF42I0JibI6nD6lRGRAfruOUOUUVilN7adtDocAD0or6xGK/5zXINCfPTdc+OtDqdfuvH8YbLbpBc3HZEx7LcDAAAAABi4SEgAnVDX4NTLm4/K18uhpeeyNnp3XDllsOLD/PTGthPKKqm2OhwAPeSlT46qvtHo1jkj5e3hsDqcfik+3E8LkmO0P7tcmw8XWh0OAAAAAAAuQ0IC6IR3d2Yqt6xW154zRMF+nlaH0y95Ouy6dc4I1Tca/WnDYZ4CBgaAnSdKtPlwoaaPCNekISFWh9Ovfe+cBPl6OfTXzcfU6OT+iIFhw4YNuuiiixQVFaXAwEBNnz5dr7/+epfqyM7O1v/7f/9P8fHx8vLyUkREhC677DJt2bLFRVEDAAAAcCUSEsBpFFfW6V9fnFRMsI8uGT/I6nD6teS4YM0bE6XtGSX6NJ2ngIH+rKHRqRc2HpGnw6YbZwyzOpx+L9jPU5dPilN2aY3WHcizOhzgjK1YsUJz587V+vXrNWXKFM2aNUvbt2/XNddco0ceeaRTdRw7dkxTpkzRn/70J3l4eGjx4sVKSEjQv//9b82YMUP/+te/XHwVAAAAAHoaCQngNP75xQlV1zfqv88fKi8P/smcqRvPH6YAbw+9+MkR1dQ3Wh0OgG56LzVHGUVVumrqYEUH+VgdzoBw2aRBCvD20D+3Zqih0Wl1OEC35ebm6uabb5a/v7+2bNmiNWvWaPXq1dq5c6eio6P1xBNPaPv27aet56c//alycnJ02223KS0tTW+99Za2bduml156SY2NjbrllltUU1PTC1cEAAAAoKcwugqcQl5Zjdak5mh0dKCmDQ+3OpwBIdjPU987L16FFXV6d2em1eEA6Iaqugb9c2uGwgO8dNWUwVaHM2D4e3voislxyi2r1cf7c60OB+i23/3ud6qurtbtt9+uKVOmtBwfM2aMli9fLmOMnnnmmdPW88EHH0iSHnnkETkcX+1Rc+ONN2r06NEqLi7Wnj17ejx+AAAAAK5DQgI4hX9sPaFGp9F10xJks9msDmfAuHhcjAaF+OiNbSdVXFlndTgAuuidHVkqq27QtefEy8eTjax70pKJgxTo46F/bj2hugZmSaB/Wr16tSTp8ssvb1N2+eWXy2azadWqVaetx25v6qqcPHmy1fH6+nqVlpZKksLCws4wWgAAAAC9iYQE0IHMkmqt3Z+r5LggTRwcbHU4A4qHw67/mj5MNfVOvfp5htXhAOiCkqo6vbMjU4NCfDQ/KdrqcAYcXy+HrpoyWAUVdVp3gFkS6H+MMdq3b58kKTk5uU15aGioYmJiVFxcrMzMU8+UXLhwoSTpuuuu06ZNm1RVVaW0tDRde+21ys3N1eWXX64RI0b0/EUAAAAAcBkSEkAH/vF5hpxG+t65zI5whfOGh2ncoCB9uDdHGYVVVocDoJNe/3JfneunDZXDzr3RFRaNj1WAt4fe2p4pp9NYHQ7QJcXFxaqpqVFgYKD8/f3bPSc2NlZS014Tp/Lb3/5WM2bM0L59+zRr1iz5+/tr9OjReuutt/Tggw/qn//8Z4/HDwAAAMC1SEgA7ThZXKUNh/I1OT5EyXHMjnAFm82mG2cMk9NIK/5z3OpwAHRCXlmN3tuTo1FRAZo+gn11XMXXy6FLJsQqu7RGmw8XWh0O0CWVlZWSJD8/vw7P8fX1lSRVVFScsq6wsDDdcMMNCg0N1fDhw3XZZZdpwoQJMsboL3/5i1JSUk75+traWpWVlbX6qa2t7eIVAQAAAOhJHlYHAPRFb23PlDHSNWcPsTqUAW10dKDOHRamzYcLdTi/QiMiA6wOCcApvLk9U41Oo++dx8wxV1syYZDe3pGpN7ef1Pkjw/n7Rr/RvPl0Z9qs03nqfVK+973v6R//+IeeeOIJPfjggy11vvXWW/rOd76jyy+/XNu2bdPYsWPbff3y5cv12GOPtTr24x//WA8//HBnLgVopbi42OoQ0E/RdtBdtB10F20HZ6I39mgjIQF8Q0FFrdYdyFNSbKDGDWJ2hKt999x4/edokV79T4YeWtz+gAIA6xVW1OqjfU2zI6bEh1gdzoAX7OepC8dGa/XubO06WapJQ0KsDgnolICApocLqqurOzynuaz53PZ8+OGH+sc//qG5c+fqf/7nf1qVXXnllbrnnnu0fPlyPfnkk3rppZfarWPZsmW6++67Wx2rrKxkI2x0G20H3UXbQXfRdtBdtB30ZSzZBHzDOzuangD+1lnMjugNwyObln75/GiR0vPKrQ4HQAfe3pGp+kaja84ewtP6veSKyXGy26Q3t520OhSg0wIDAxUYGKjS0tIOkxLZ2dmSvtpLoj3r1q2TJF100UXtli9atEiStGPHjg7r8Pb2VlBQUKsfb2/vTl0HAAAAANcgIQF8TVlNvT7Ym6OEcD+dlRBqdThu47vnxstmk1b8J8PqUAC0o6SqTu+n5mhYhL/OGcaTNr0lOshH54+M0M4TJTpeWGl1OECn2Gw2jRs3TpK0f//+NuVFRUXKyclRaGio4uLiOqynpKREkuTh0f6E7ubjdXV1ZxgxAAAAgN5EQgL4mtW7s1VT79TVUwfzBHAvSgj314yREfriWDGzJIA+6N2dWaprcDI7wgKXTWoasF21O9viSIDOW7hwoSTpnXfeaVP2zjvvyBjTMsOhI0lJSZKk9957r93yjz76SJI0adKk7gcKAAAAoNeRkAC+VNfg1Ht7shUV6K2ZoyKtDsftXD11sKSmTXMB9B3VdY16b0+2Bof6atrwcKvDcTuJMYEaFRWgdQfyVF5Tb3U4QKfceOON8vPz01NPPaXNmze3HD948KAefPBBSdJPf/rTluPZ2dk6cOBAy1JOknTttdcqMDBQKSkp+uUvfyljTEvZhx9+qOXLl8tms+lHP/pRL1wRAAAAgJ5CQgL40qfpBSqpqtclE2LlsPMEcG8bHhmgqQmh2pxeoMySjjfCBNC7PtyXo6q6Rl0+OU527o2WWDJxkOoanPpoX67VoQCdMnjwYD377LOqqqrSrFmzdOGFF2rx4sWaNGmScnJytHz5ck2cOLHl/GXLlikpKUnLli1rORYVFaVXX31VPj4+uu+++zRq1ChdddVVOuuss7RgwQLV1NToqaee0rnnnmvFJQIAAADoJhISgCRjjFbuypK3h10Xjo22Ohy3dfXUwXKapo3FAViv0Wn0751ZCvHz1NzEKKvDcVvnj4xQiJ+nVu/OltNpTv8CoA+46aab9P7772vmzJnasmWLPvnkE02ZMkVvvvmm7r///k7VsXjxYm3btk3XX3+9ampqtHLlSh07dkyLFy/WunXrdOedd7r2IgAAAAD0uPZ3iQPczIGccqXlVeji5BgF+nhaHY7bGjcoSIkxgfp4f66uPSdeYf5eVocEuLXNhwuUV16r754bLy8PnmGwipeHXQuTY/Xa5xnacrRQ00dEWB0S0CkLFizQggULTnveyy+/rJdffrndsrFjx+qvf/1rD0cGAAAAwCqMLgCSVu7KkiQtmTDI4kjcm81m01VTBquh0ejfO5klAVjJGKO3d2TK02HTouRYq8Nxexcnx8hut2lNao7VoQAAAAAA0G0kJOD2Cipq9enhQk0cEqz4cD+rw3F75w4LU1yIr9bszVFNfaPV4QBua392udJyK3RBUrSC/Zg5ZrUwfy+dNyxMOzJKlFtWY3U4AAAAAAB0CwkJuL33U3PkdBpmR/QRdrtNiyfGqrK2UesP5lkdDuC2Vu1umjl26UTujX3FguQYSdKHe5klAQAAAADon0hIwK3VNzr1QWqOooN8dPbQMKvDwZcuGBMtXy+HVu7KljFs4Ar0tuLKOm0+XKgJg4M1JIyZY33FpMEhig7y1of7ctXQ6LQ6HAAAAAAAuoyEBNzaf44UqbS6Xgu/XJsbfYOvl0MXjY1WRlGVdp0stTocwO18tC9XjU6jS8azd0RfYrfbdNHYGJVU1WvrsWKrwwEAAAAAoMtISMCtfbA3R3a7TRckRVkdCr5h8YRBstm+2nAcQO9odBq9n5qtMH8vnTOMmWN9zQVJUbLbmn5/AQAAAADQ35CQgNvKKa3RzhMlOm94mEL8vKwOB98QE9y0jNbWY0XKLq22OhzAbWw9VqSCijotGBcjDwdfE/qa8ABvnTMsTNszipXH5tYAAAAAgH6mR0YaNmzYILvdrhdffLFLr7vssstks9k6/FmzZk1PhAe066N9TU+XLhgXY3Ek6MiSiYNkjLR6d7bVoQBuY/XubNlt0oJx0VaHgg5cnBwjY6QP9+VaHQoAAAAAAF3icaYVHDx4UNdee223Np7dvn27vLy89K1vfavd8kGDBp1peEC7Ghqd+nBfrqKDvDVpcIjV4aADEwcHKz7MTx/uy9XS8xLk4+mwOiRgQMssqdbOEyWaPjJc4QHeVoeDDkweEqrIQG99vD9X3z0nnj2QAAAAAAD9xhklJNatW6drr71WeXl5XX5tQUGBTp48qbPOOkuvvPLKmYQBdNnWY8UqqarXdeclMJDTh9lsNi0cH6M/bTiiTWkFunAsT2wDrvT+nqbZSGxm3bfZ7TbNHROl17ee0O7MUk0aEmJ1SAAAAAAAdEq3lmzKy8vTbbfdpgsvvFBFRUWKj4/vch3bt2+XJE2dOrU7IQBn5IO9ObLbxGbW/cDcxCh5edi1JpUNXAFXqm90KuVgngaF+Gh8XLDV4eA05o1p+v21bj/LNgEAAAAA+o9uJSR+/vOf6w9/+INGjhypdevWae7cuV2uY8eOHZJISKD35ZfXantGsc4eGsaSJP2Av7eHZo2K1KHcch3Jr7A6HGDA2nq0SGXVDbpwbIxsNmaO9XVxIb4aExOozYcLVV3XaHU4AAAAAAB0SrcSEsOHD9fvf/97paamaubMmd164+YZEtXV1br00ksVExMjf39/nXfeeVqxYkW36gQ6I+VgnowRy//0IwuSmz4rNnAFXOfDfbmy27568h593wVJUaptcOqT9AKrQwEAAAAAoFO6lZC44447dOutt8rT07Pbb9yckPjxj3+sgwcP6vzzz1diYqI+//xzLV26VHfccUe36wY6YozRuv15CvL10NSEUKvDQSclRgdqaIS/1h3IU009TwIDPa2golY7Mop11tAwhfl7WR0OOmnGqEh5Omxad4BkLQAAAACgf+hWQuJMlZeX6/Dhw7LZbPrTn/6kgwcP6s0339T27dv18ccfKzg4WL/97W/15ptvdlhHbW2tysrKWv3U1tb24lWgP0rLq1BmSbVmj46Uh8OS5o9usNlsWjAuWtV1jdqUxpPAQE9btz9PTmaO9TsB3h46d3i4UjPLlFtWY3U4AAAAAACclocVbxoYGKiCggLl5+crMTGxVdm8efP02GOP6c4779Rzzz2nq666qt06li9frscee6zVsfvuu0/33nuvy+I+neLiYsveG52zanumGhoaNCXWW0VFRS55D9qBa0yK8pRdTv17+3FNjen+7KzeRFuA1PfbgdMYvbfrhPw9peFBctm90d25qh2cNchH6/c3aOW2o7pyIgmlvs7q+0FYWJil7w8AAAAAliQkpKYOUUedoiVLlujOO+/UF1980eHrly1bprvvvrvVMW9vb3l7W7tJMR29vquuwamtJw5peFSQpo6Mc+mmrbSDnhcmaV5SiT7en6tSp7eGRfhbHVKn0BYg9e12kJpZqsJqp66cEqeoiHCrwxnQXNEO5oSE6uWtufrPiUrdNCeUDcn7gb58PwAAAAAAV+uTa9bExMRIatrwuiPe3t4KCgpq9WN1MgJ92xfHilRR26B5Y6IYsOmnmpeTWbuf9dKBnvLh3hxJ0vwknq7vjxx2m2aPjlROaY0O5pZbHQ4AAAAAAKdkSUJiw4YNuv7667V8+fJ2y48cOSJJiouL682wMMCtO5Anu02akxhpdSjopqTYQA0K8dH6g/lqaHRaHQ7Q79XUN2rz4UKNiQnUkDA/q8NBNzX/XttwMN/iSAAAAAAAODVLEhJ1dXX6+9//rt/85jftbkT917/+VZK0YMGC3g4NA1RpVb22Hi/WxCEhCg9gJk1/ZbPZdMGYaJVW12vb8b69Lj/QH/znaJFqG5yakxhldSg4AyMiAzQoxEefpBeo0WmsDgcAAAAAgA65PCFRUFCgAwcOKCMjo+XYvHnzNHr0aOXm5ur2229XfX19S9nq1av17LPPytfX19INqjGwbEzLl9NpNG8Mg2793bykKNls0scs2wScsQ0H82W3STNGRlgdCs6AzWbT7NFRKqmq1+6TJVaHAwAAAABAh1yekHjuueeUlJSk66+/vuWYw+HQa6+9ppCQEL344osaOXKkrrzySk2bNk2LFy+W0+nU3//+d40cOdLV4cFNrDuQJ19Ph84bzoat/V1EgLcmDQnR58eKVVpVf/oXAGhXWU29tmUUa9KQEAX7eVodDs7QrNFNSaWNhwosjgQAAAAAgI5Ztqn1lClTtHPnTn3/+9+X0+nUqlWrdOTIEX3729/WF198oauuusqq0DDAnCiqUnpehc4fGSEfT4fV4aAHXJAULafTaP2hPKtDAfqtzekFcjqNZrOvzoAwONRPIyL99enhAtU1sMcOAAAAAKBv6pGExMsvvyxjjL7//e+3KXv00UdljNH69evblCUkJOiFF17QiRMnVFdXp9zcXP3zn//UxIkTeyIsQJK0Ka3paVEG3QaO84aHyc/LobX7SUgA3bXhUIE8HTZmjg0gs0ZHqrquUV8cK7I6FAAAAAAA2mXZDAmgNxhjtPFQvkL8PDU+LtjqcNBDvD0cmjU6UkcLKnUkv8LqcIB+p6CiVnuzSnX2sDD5eXlYHQ56yKzRkbLZpA1p+VaHAgAAAABAu0hIYEA7UlCpzJJqnT8yQg67zepw0IMuSGraoJxZEkDXfZJWIGOk2aOYOTaQRAR4a9ygIG09WqSqugarwwEAAAAAoA0SEhjQNh5qekp0FoNuA05idKAGh/pq/aE81TeyXjrQFRsO5cvXy6GzhoZZHQp62OzRkapvNNpypNDqUAAAAAAAaIOEBAYsp9NoU1qBIgK8NCYm0Opw0MNsNpvmjYlSWXWDth8vtjocoN/ILKlWel6Fpo8Il5cHXwMGmukjI2S327ThIMs2AQAAAAD6HkYiMGAdzC1XfnmtZo2OlJ3lmgakOYlNyzalMPAGdFrzQPXs0cwcG4iCfDw1JT5EO0+UqKSqzupwAAAAAABohYQEBqzm5ZpmslzTgBUZ6K3kuGB9frRQlbWslw6cjjFGGw/lK8TPUxMGh1gdDlxk9uhIOY30SXqB1aEAAAAAANAKCQkMSI1Oo0/SCzQoxEcjIv2tDgcuNDexab30zYdZLx04ncP5lcosqdaMkRFyMHNswDp3WNNyXJsOkZAAAAAAAPQtJCQwIO3JLFVJVb1mjY6Uzcag20A2fWSEPB02rT+YZ3UoQJ/XPHNsFss1DWi+Xg6dMyxM+7LLlF9ea3U4AAAAAAC0ICGBAall0I3lmga8AG8PnT00THsyS1VQwcAb0BGn02hjWr6ig7w1JibQ6nDgYjNHRUiSPklnjx0AAAAAQN9BQgIDTn2jU5sPF2hYhL+GhPlZHQ56wZzEKBnz1Wa9ANral12mwoo6Zo65ibMSwuTr6WDZJgAAAABAn0JCAgPOjowSVdY2tjwdioFvakKoArw9tP4QCQmgIxu+/Pcxm+Wa3IKXh13njQhXWl6FskqqrQ4HAAAAAABJJCQwALFGuvvx8rBrxqgIHSuo1NGCSqvDAfqchkanPk0vUHy4nxLC/a0OB71k9ugvl21KY5YEAAAAAKBvICGBAaWmvlH/OVqoxJhARQf5WB0OetGcxKYEFJtbA23tPFGi8poGzWZfHbcycXCIArw9tCGN2WMAAAAAgL6BhAQGlC+OFaum3slyTW4oKSZI0UHe2nAoX06nsTocoE9pWa4pkYSEO/FwNM0eyyis0vFCZo8BAAAAAKxHQgIDysa0fNls0oyRJCTcjd1u0+zRkSqsqFNqVqnV4QB9Rk19o7YcYeaYu2pO0G9k2SYAAAAAQB9AQgIDRmVtg744VqTkuGCFB3hbHQ4sMCcxSpKUcoDlSYBmnx8tUk29k82s3VTyoGCF+Hnqk7R8GcPsMfSuDRs26KKLLlJUVJQCAwM1ffp0vf76612uZ8WKFZo5c6aCg4Pl6+urKVOm6Pnnn6dNAwAAAP0QCQkMGFuOFKq+0WgWyzW5rSFhfhoZFaBPDxeotqHR6nCAPmHjoXzZbWIpOzdlt9s0c1SEskpqdDifZZvQe1asWKG5c+dq/fr1mjJlimbNmqXt27frmmuu0SOPPNLpem688UYtXbpUX3zxhWbMmKHp06dr7969uuWWW3Tfffe58AoAAAAAuAIJCQwYm9IKZLfbNJ3lmtzanMRIVdc1auvRYqtDASxXXlOvL44Xa8LgEIX4eVkdDiwy88vNzDexuTV6SW5urm6++Wb5+/try5YtWrNmjVavXq2dO3cqOjpaTzzxhLZv337aev72t7/pL3/5ixITE3XgwAGtXr1aa9eu1fbt2xUWFqZf/epX2rlzp+svCAAAAECPISGBAaG0ul47Moo1eUiIgnw8rQ4HFpo1KlJ2m5RyMM/qUADLfXa4UI1Oo1ks1+TWEqMDFRnorU1pBXI6WeIGrve73/1O1dXVuv322zVlypSW42PGjNHy5ctljNEzzzxz2nqeeOIJORwO/fOf/1RCQkLL8XHjxumee+7RkCFD9MUXX7jiEgAAAAC4CAkJDAib0wvkNGKNdCjU30uThoToi+PFKq2utzocwFIbDuXL02HT9BHhVocCCzUv25RfXquDueVWhwM3sHr1aknS5Zdf3qbs8ssvl81m06pVq05Zx65du5Senq558+Zp4sSJbcqXLVumjIwMff/73++RmAEAAAD0DhISGBA2pjUNup07PMzqUNAHzEmMktNp9Gl6gdWhAJYpqqzTnsxSnTU0TP7eHlaHA4s1L9u08RDLNsG1jDHat2+fJCk5OblNeWhoqGJiYlRcXKzMzMwO69m2bZsk6ZxzzpExRmvWrNFPfvIT3XzzzXr22WdVXMzSjAAAAEB/REIC/V5BRa32ZpXp7KFh8vNi0A3SecPD5e1hV8oBlm2C+9qUli/DzDF8aUSkvwaF+OiTdJZtgmsVFxerpqZGgYGB8vf3b/ec2NhYSU17TXQkPT1dkhQUFKSLL75YCxcu1FNPPaUXX3xRP/7xj5WYmKjNmzf3/AUAAAAAcCkSEuj3Pk0vkDFijXS08PVyaNqIcB3IKVd2abXV4QCW2HAoX76eDp01NNTqUNAH2Gw2zRwVqZKqeu3JLLU6HAxglZWVkiQ/P78Oz/H19ZUkVVRUdHhOaWlTO12+fLm2bdumFStWqLCwUOnp6brllluUn5+vSy+9VNnZ2R3WUVtbq7KyslY/tbW13bksAAAAAD2Ex8nR7zHohvbMSYzU+oP5Wn8wX9eeE291OECvyiqpVlpuheaOiZK3h8PqcNBHzBoVqX9uPaFNafmaOCTE6nAwQDkcTfccm8122nOdTmeHZc2Jg5KSEqWkpGjOnDmSpLCwMP3xj39UVlaWVq5cqWeffVbLly9vt47ly5frsccea3Xsxz/+sR5++OHOXArQCsuEobtoO+gu2g66i7aDMxEW5vrl8ElIoF/LKa1pGnRLjGTQDa1MGhKqED9PrT+Yp++cPaRTAyPAQLEprWmfgNmjIyyOBH1JfLifEsL99Gl6oW6ZPUKeDibKoucFBARIkqqrO56h2FzWfG57mmdYjBs3riUZ8XW33nqrVq5cqXXr1nVYx7Jly3T33Xe3OlZZWdkrnSwMTLQddBdtB91F20F30XbQl9ETRb/WvDnnTJZrwjc47DbNHh2prJIapeV1vCQEMNAYY7ThUL6CfT01cXCI1eGgj5k1KlIVtQ3adaLE6lAwQAUGBiowMFClpaUdJiWal1lq3kuiPZGRTd/thg0b1m750KFDJUkFBQUd1uHt7a2goKBWP97e3p25DAAAAAAuQkIC/dqGtHwFeHtoEktPoB1zEpsGM9axuTXcyNGCSp0oqtb5IyPkwRPw+IaZX86a2ZjW8SAucCZsNpvGjRsnSdq/f3+b8qKiIuXk5Cg0NFRxcXEd1jN+/HhJUmZmZrvlOTk5kqSoqKgzDRkAAABAL2KkAv3W8cJKZRRWacaoCJadQLtGRAZocKivNqXlq6Gx43WqgYFkw5czx2axXBPaERvsq1FRAdpyuFB1DdwX4RoLFy6UJL3zzjttyt555x0ZY7Ro0aJT1jFv3jz5+Pho586dOnDgQJvy999/X5I0a9asMw8YAAAAQK9hFBf9VstyTaMYdEP7bDab5iZGqay6QTtYngRuwOk02pRWoMhAbyXFBFkdDvqomaMjVF3fqC+OF1kdCgaoG2+8UX5+fnrqqae0efPmluMHDx7Ugw8+KEn66U9/2nI8OztbBw4caFnKSZKCgoL0gx/8QMYYLV26VHl5X812/PDDD/Xss8/K19dXP/jBD3rhigAAAAD0FBIS6Jea1kgvUKi/l5IHBVsdDvqw2V8u25TCsk1wA/tzypRfXquZoyJkt7ORO9o3Y2TTfXETyzbBRQYPHqxnn31WVVVVmjVrli688EItXrxYkyZNUk5OjpYvX66JEye2nL9s2TIlJSVp2bJlrer5+c9/rlmzZmnbtm0aMWKElixZounTp2vhwoVqaGjQc889pxEjRvT25QEAAAA4AyQk0C+l51Uot6xGM0cy6IZTiw7y0bhBQdpypFBVdQ1WhwO41MZDTQPMs0dHWhwJ+rLIQG+NjQ3S50eLVF3XaHU4GKBuuukmvf/++5o5c6a2bNmiTz75RFOmTNGbb76p+++/v1N1+Pv76+OPP9bTTz+tkSNHau3atTp48KAWLFigdevW6cYbb3TxVQAAAADoaR5WBwB0x1drpDPohtObkxipvVll2pxeqPljo60OB3CJhkanPk0v0JAwXw2L8Lc6HPRxM0dHaF92mT4/VkQCCy6zYMECLViw4LTnvfzyy3r55ZfbLfP09NSdd96pO++8s2eDAwAAAGAJZkig32leIz06yFujowOsDgf9wPkjI+ThsGn9IZZtwsC162SJSqvrNXt0pGw2Zo7h1GaMjJDd9tV+TAAAAAAA9AYSEuh39mWXqaiyTrMYdEMnBfp46pyhYdp9slQFFbVWhwO4xIYvl2uaOYqn3XF6IX5eGj84WNuOF6u8pt7qcAAAAAAAboKEBPqdluWaGHRDF8xOjJQx0oaDPA2Mgae2oVFbDhdqVHSABoX4Wh0O+olZoyLV6DTacqTI6lAAAAAAAG6ChAT6leY10uPD/JQQ7md1OOhHzkoIU4C3h9azPAkGoK1Hi1Vd38heAOiSaSPC5bDbtCmN+yIAAAAAoHeQkEC/sutkicprGjRrdATLNaFLvDzsmjEqQscKKnW0oNLqcIAetf5gnuw2Zo6hawJ9PDUlPlS7TpSopKrO6nAAAAAAAG6AhAT6lebldlgjHd0xJ7Gp3aw/yObWGDjKa+r1xfFiTRgcolB/L6vDQT8zc3SEnEbafLjQ6lAAAAAAAG6AhAT6jdqGRm05UqRRUayRju5JiglSdJC3NhzKl9NprA4H6BGfpheo0WlaEm5AV5w3LFyeDpZtAgAAAAD0DhIS6De2HWtaI30Wa6Sjm+x2m2aPjlRhRZ32ZJZaHQ7QIzYcypenw6ZpI8KtDgX9kK+XQ2cPC9PerDIVVNRaHQ4AAAAAYIAjIYF+Y92BPNls0oxREVaHgn5sTmKUJCmFZZswAOSV1yg1s0znDg+Xn5eH1eGgn5o1KlLGNM22AQAAAADAlUhIoF8oa1kjPVgRAd5Wh4N+bEiYn0ZFBWhzeqGq6xqtDgc4IxsPNQ0gz2HmGM7A1IRQ+Xo6WtoTAAAAAACuQkIC/cInaU1rpM8bE2V1KBgA5o+NVnV9I08Do99LOZinAG8PTUkItToU9GM+ng6dOzxMh3LLlVtWY3U4AAAAAIABjIQE+oWUA3ny9rBr2nCWa8KZmzU6Up4Om9YeyLU6FKDbjhZUKqOwSjNGRcjTwa9znJmZo5pm2Ww8xObWAAAAAADXYQQDfV5WSbUO5JRr2ohw+Xo5rA4HA0CAt4emjQhXamaZMkuqrQ4H6Jb1X+6DMpvlmtADJseHyN/boU1pzBwDAAAAALjOGSckNmzYILvdrhdffLFLr6utrdWTTz6p8ePHy9/fX9HR0Vq6dKkOHz58piFhgFl/sOlpzebNiIGecOHYGEnSuv3MkkD/43QabTyUr8hAb42NDbI6HAwAng67po+I0NGCSp0oqrI6HAAAAADAAHVGCYmDBw/q2muvlTGmS69raGjQlVdeqXvuuUdFRUVauHCh4uLitGLFCk2ePFm7d+8+k7AwgBhjlHIwTyF+npo0JMTqcDCATIgLVmSgt9YeyJPT2bV7GGC1fdllKqio0+zRkbLbbVaHgwFi1pezbZglAQAAAABwlW4nJNatW6dZs2YpOzu7y6/9/e9/r/fee0/z589XWlqa3njjDW3fvl1PP/20ysvLdcMNN3Q5yYGB6UBOuXJKazR7dKQcDLqhB9ntNl2QFKXCijrtOFFsdThAlzQv1zQnkeWa0HPGxwUrxM9TGw7l8T0MAAAAAOASXU5I5OXl6bbbbtOFF16ooqIixcfHd+n1xhg9+eSTkqTnnntOfn5+LWV33nmnZs2apZ07dyolJaWroWEASvly0G3eGJZrQs+bnxQtSfpoX57FkQCdV9vQqE1pBRoa4a+EcH+rw8EA4rDbNGNkhLJKanQot8LqcAAAAAAAA1CXExI///nP9Yc//EEjR47UunXrNHfu3C69PjU1VRkZGRozZowSExPblF9xxRWSpFWrVnU1NAww9Y1ObTpUoPhwPw2LYNANPS86yEcTBgdry5FClVbXWx0O0ClbjhSpqq5R85NI1KLnXfBlovZj9tcBAAAAALhAlxMSw4cP1+9//3ulpqZq5syZXX7DvXv3SpKSk5PbLR87dqwkac+ePV2uGwPL1mNFqqht0LzEKNlsLNcE15ifFK1Gp9GGQ/lWhwJ0ytr9ubLbbZozmoQEet6ISH8NjfDXxkP5qm1otDocAAAAAMAA0+WExB133KFbb71Vnp6e3XrDrKwsSVJsbGy75c3Hc3N5Ms/dfbwvT3Yba6TDtaaNCJefl0Mf7M1hzXT0efnltdp5okTnDA1VsF/3fg8Dp2Kz2TQ/KUpVdY367HCh1eEAAAAAAAaYbm9q3V2VlZWS1GrviK/z9fWVJFVUnHrt4traWpWVlbX6qa2t7dlgYZnCilptO16kqQlhCg/wtjocDGA+ng7NHROljMIq7c8utzoc4JRSDuTJmK+W1QFcYc7oKNntNpZtAgAAAAD0OI/efkOHwyFJp12Cx+l0nrJ8+fLleuyxx1odu++++3TvvfeeWYBnoLi42LL3HmhW7slTXX2Dzh3sq6KiIqvD6RLaQf8zfYiv3t3eoLe3HlHMzPgeq5e2AKnn2oExRu/vPiF/T2l4kPrdvdHd9bf7wYQYX207VqhDGTmKCPCyOpwBw+p2EBYWZun7AwAAAECvJyQCAgIkSdXV1e2WNx9vPq8jy5Yt0913393qmLe3t7y9rX2ano7emTPG6LOMI4oI8tUFExLk4ej1iTxnjHbQv4SFSRPjC/RFZrkcvoEK9u25pXBoC5B6ph3szSpVQZVTl00apKiI8B6ICr2tP90PFk822pm1Xzty63RNfIzV4Qwo/akdAAAAAEBP6/WR3ri4OElSTk5Ou+XZ2dmSOt5jopm3t7eCgoJa/VidjEDP2JtVpqySGs1JjOqXyQj0TwvHx6qh0WgtS5Sgj1q7P09S00bsgKudNTRMIX6e+nh/HvvrAAAAAAB6TK+P9iYnJ0uS9u3b12753r17JUnjx4/vtZjQt3y0r2lA+EIG3dCLpg0PV7Cvp95PzZHTyeAb+paa+kZ9klagkVEBGhrhb3U4cAMOu01zE6OUU1qjvVllVocDAAAAABggej0hkZiYqOHDhys1NVWHDx9uU/72229LkhYtWtTboaEPqKpr0KfpBUqMCVR8ePsbnwOu4OVh1/ykpsG3XSdLrA4HaOWzw4Wqrm/UBUlRVocCN9I8G4fNrQEAAAAAPcWlCYmCggIdOHBAGRkZrY7ffvvtMsbopptuUnl5ecvx3/zmN9q0aZMmT56s+fPnuzI09FEbDxWotsGpC8cyOwK97+LkWNls0vup7S8pB1jlw3058nDYNGt0pNWhwI3Eh/tpdHSgPkkrUGVtg9XhAAAAAAAGAJcmJJ577jklJSXp+uuvb3X8Rz/6kebNm6cNGzZo5MiRuvrqqzV16lTdeeedCg0N1SuvvOLKsNBHGWO0JjVbPp52zRwVYXU4cEMxwT6aEh+q/xwpVEFFrdXhAJKkE0VVSs0s0/QR4Qry6bkN14HOuDg5RrUNTqUczLM6FAAAAADAAGDJjsEeHh5avXq1Hn/8cYWEhGjVqlXKz8/X0qVLtXXrVo0dO9aKsGCxtLwKHc6v1JzEKPl5eVgdDtzUxckxchrpw70sUYK+4YO9TTN2FibHWhwJ3NHMURHy83Lo/dQcNrcGAAAAAJyxM05IvPzyyzLG6Pvf/36bskcffVTGGK1fv75NmY+Pjx566CEdPHhQNTU1ysjI0N///neNGDHiTENCP/XenmxJ0sLkGIsjgTs7e2iYIgO99X5qtuoanFaHAzdX29CotfvzNCTMV+MGBVkdDtyQj6dDFyRFKaOwSvuzy0//AgAAAAAATsGSGRLAN5XX1GvjoXyNiQnU8MgAq8OBG3PYbVo8IVYlVfXalJZvdThwc5vTC1VR26AF42Jks9msDgdu6uJxTbNz1qRmWxwJAAAAAKC/IyGBPmHt/jzVNxotGs+SJLDeReNi5ONp17s7s1iiBJZ6PzVbng6b5o2JsjoUuLH4cD8lxwXpk/QClVbXWx0OAAAAAKAfIyEByzmdRu+nZivQx0Pnj2Qza1gvwNtDFyRF62hBpVIzy6wOB27qeGGl9meXa8aoSAWymTUsdnFyrOobjdYdYH8dAAAAAED3kZCA5XZnliqrpEbzk6Ll5UGTRN+wZOIg2WzSuzszrQ4Fbur91ObNrNlXB9abNjxcQb4een9PjpxOZo4BAAAAALqH0V9Y7v0vN7O+mEE39CFxIb46e2iYPj9WpKySaqvDgZuprmvUugN5Sgj305iYQKvDAeTlYdf8pGhll9Zox4liq8MBAAAAAPRTJCRgqdyyGm05UqipCaEaFOJrdThAK5dNGiRjpJW7sqwOBW5m7YFcVdc1avGEWDazRp9xyYRY2W3Sv3dyTwQAAAAAdA8JCVhq5a4sOU3TwC/Q14yPC9awCH99vD9XZTVs5Ire4XQardyVpQBvD81JZDNr9B1RgT6aNiJC2zNKlFFYZXU4AAAAAIB+iIQELFNV16AP9+YqPtxPk4aEWB0O0IbNZtNVUwerpt6pVbuyrQ4HbmJ7RrGySmq0YFy0fDwdVocDtNL8AMHK3cySAAAAAAB0HQkJWOajfbmqrm/UZRMHsSQJ+qwZIyMUHeSjlbuyVF3XaHU4cAP/3pUlu026ZAIzx9D3jIkJ1KioAK07kMfMMQAAAABAl5GQgCUav1ySJNjXkyVJ0Kc57DZdPTVOFbUN+mBvjtXhYIA7UVSlHRklmjYiQpGB3laHA7Rhs9l02eQ41TU4tSaVeyJObcOGDbrooosUFRWlwMBATZ8+Xa+//voZ1fnqq6/KZrNp6dKlPRQlAAAAgN5EQgKW2HKkULlltVo0PlZeHjRD9G3zxkQr1N9Lb+/IVF2D0+pwMID9+8sN1C+dyOwI9F3njwhXmL+XVu/OVkMj90S0b8WKFZo7d67Wr1+vKVOmaNasWdq+fbuuueYaPfLII92q88SJE/rhD3/Yw5ECAAAA6E2MBMMS7+zIlIfDpkXjY6wOBTgtLw+7Lp80SEWVdVp3IM/qcDBAldfUK+VAnkZGBSgpNtDqcIAOeTjsumRCrIoq67QprcDqcNAH5ebm6uabb5a/v7+2bNmiNWvWaPXq1dq5c6eio6P1xBNPaPv27V2q0xijG264QSUlJa4JGgAAAECvICGBXrcvq0wHcso1Z3SUQvy8rA4H6JSFybEK8PbQm9tPqtFprA4HA9B7e7JV2+DUpeyrg35gYXKMfD0demP7STm5J+Ibfve736m6ulq33367pkyZ0nJ8zJgxWr58uYwxeuaZZ7pU51NPPaWUlBTNmjWrh6MFAAAA0JtISKDXvf7FCdls0lVT46wOBeg0Xy+HlkwcpJz/3969x0Vd5f8Df31mgGEYhpvIJcALKoLiJdJKTdJMUbzhZXNbL5mutVnbxdbKS1bbtrRbqVlt/cpW65tZlmWieUvBvJFXFEQQFEG5KQLDfbjM+f3hMkWAwjjDB2Zez8eDx6M+5/Dh/XEOH87nvD/nHF0V4lI5S4LMq6qmDltP58BLq8LwXp5yh0N0S1pHe4wN9UHW9Qocu1QodzjUzmzfvh0AEBUV1agsKioKkiRh27ZtLT5fYmIili1bhokTJ+LRRx81V5hERERERCQDJiSoTV24VoYTmUUY2sMT/u5OcodD1CqTBt4BjUqJjUcvc910Mqs9yfkoqazFlDA/2Cn5p5k6hqg7/WCnlLDp+BUIwVkSdIMQAsnJyQCA0NDQRuXu7u7w8fFBUVERsrOzb3k+vV6PmTNnQqvV4pNPPjF7vERERERE1LY46kFtatPxywCAhwb5yxwJUes5q+ww5U4/5JdU4adznCVB5lFbZ8D3p7LhqrbHgyHecodD1GIeGgc8GOKN8/mlSMzWyR0OtRNFRUWoqqqCVquFRqNpso6vry+AG3tN3MrSpUuRmJiIjz76CN7evEcSEREREXV0TEhQm7lcWIEjF65jUDd3BHZ2ljscIpNMGuAHraMdvj6WhepazpKg2/dz2jVcK9Vj0oA74GivlDscolaZFuYPhfTrCwdE5eXlAAAnp+ZnwqrVagBAWVnZTc+1b98+rFq1CrNmzcK0adNaHYter0dJSUmDL71e3+rzEBERERGR+djJHQDZjm9PXIEQwEODAuQOhchkagclpoX5Y/3hS9idnIcJ/e+QOyTqwAwGgc0nsqG2VyKyv6/c4RC1mo+rI8KDOiMu9RrO55ciyFsrd0gkM6XyRmJVkqRb1jUYmk/sFxcXY+7cufDz88N7771nUizR0dF47bXXGhx75plnsGLFCpPOR7atqKhI7hCog2LbIVOx7ZCp2Hbodnh4eFj8ZzAhQW0iv+TGRsChfq4I8XWROxyi2zK+vy+2JGRj0/ErGN3HGyo7vtVOpjl6qRBZhRWYGuYHZxX/JFPHNP0uf8SlXsNXRy9jxcQ+codDMnN2vjELtrKystk69WX1dZuycOFCXLlyBbt374abm5tJsSxZsgSLFi1qcKy8vLxNHrLIOrHtkKnYdshUbDtkKrYdas84+kFt4qujl2EQwIzBnB1BHZ+jvRLT7/LH2gMZ2HY6F9Pu4p4o1HoGg8CXv2TBXilh8kA/ucMhMlnXThoM7dkJh9OvIzWvFL19OEvClmm1Wmi1Wuh0OlRWVhqXZ/qt3NxcAL/uJfF7x48fx8aNG+Hh4YH169dj/fr1xrKLFy8CAA4fPoxZs2YhJCQEy5Yta/I8KpUKKpWqwbHa2lpTLouIiIiIiMyEe0iQxV0pqsC+lHyE+rligL+r3OEQmcW4UF94u6iw6fhllFTVyB0OdUDxF68jo6Ackf184aFxkDscotsy8+6ukCTgi/hMuUMhmUmShL59+wIAzp0716i8sLAQeXl5cHd3h59f08nY+r0lCgsLsWHDhgZfR44cAQBkZGRgw4YN2LNnj4WuhIiIiIiILIEJCbK4L3/JgkEAc4Z0bdF6wkQdgYOdAnOGdENFdR2+PsrNXKl1DAaBDb9kwdFegemcYUNWoEsnJ4wI6oyEy8VIytbJHQ7JbNy4cQCALVu2NCrbsmULhBCIjIxs9vtHjBgBIUSTX+vWrQMAzJw5E0IIxMXFWeISiIiIiIjIQpiQIIu6eK0MB9IKcFdXd+4dQVZneC9P9PJ2xrbEXGQXN79WNtHvHUwvQFZhBSb0vwNuTpwdQdbh4Xu6QPG/WRJCCLnDIRnNmzcPTk5OWLlyJQ4fPmw8npqaalxeafHixcbjubm5SElJMS7lRERERERE1osJCbKoDb9kAQBmD+kqcyRE5idJEuYN6w6DQeDzw5fkDoc6iLr/7R2htldiShj3jiDr4euqxug+3jibU4JTl4vlDodk5O/vjzVr1qCiogLh4eEYPXo0JkyYgIEDByIvLw/R0dEYMGCAsf6SJUsQEhKCJUuWyBg1ERERERG1BSYkyGJS80pxNKMQQ3t2Qo/OznKHQ2QRoX6uuDfQA4cvXMe53BK5w6EOYP/5q8gursSkgXfAxdFe7nCIzOqhwQGwU0r44kgmDAbOkrBl8+fPx44dOzB8+HDEx8fj4MGDCAsLw+bNm/HSSy/JHR4REREREcmECQmyCCEE1h/OgEK6sdElkTWbO6w7FAoJn/x8kQNwdFPVtQZsiM+CRqVE1J2cHUHWx0vriPH9fJF2tQw/p12TOxySWUREBGJjY1FaWori4mIcOnQIU6dObVRv/fr1N/qO69ff8pxz586FEAJfVx11OQAATO9JREFUfPGFBSImIiIiIiJLY0KCLOLIxetIyi7BmL4+6NLJSe5wiCzKz02Nif1vDMDtOZcvdzjUjm07k4OrpXo8NCgAzio7ucMhsogZg2+078+PZEJfWyd3OERERERERNSOMCFBZldTZ8C6Q5egtldi5j1d5A6HqE386Z4ucHOyx2eHL6G0qkbucKgdKqmqwdfHLsPbRYUJ/e+QOxwii9E62uOPdwfgWqkeWxNy5A6HiIiIiIiI2hEmJMjsfkzMRZ6uCtMH+cPNyUHucIjahJODHeYN647SqlrjZu5Ev/X10cuoqK7DnCHd4GDHP79k3SL7+cLH1RHfHL+C4opqucMhIiIiIiKidoIjImRWJVU12Hg0C521KkweyDeAybaM6N0ZIb5a7EjMxcVrZXKHQ+1IdnEltiXmope3M4b38pQ7HCKLs1cq8OjQbqisqcOXR5mkJSIiIiIiohuYkCCz+upoFsr1dXhkaDeo7JRyh0PUpiRJwl/u7wEA+DDuAgyCG1zTDZ8fvgSDQWDesO6QJEnucIjaxJAendDH1wW7kvJwqaBc7nCIiIiIiIioHWBCgszmUkE5tp/JRZC3FuF8A5hsVGBnZ9wX5Iz96WnYciZD7nCoHUjKLcXhC9dxb6AHQv1c5Q6HqM1IkoQF4d0hAPy/ny9AMElLRERERERk85iQILMQQuDDuAsQAJ4YEcg3gMmmdfZMRqXhGj47loXrZXq5wyEZ1dQZ8PnRHNgrJfx5eKDc4RC1uZ5eWowN9UFSdgniUq/JHQ4RERERERHJjAkJMot9KVeRnFuCcaG+6OmllTscItnklubi0JVY+PgkoqiyHCt/OiN3SCSjmNM5yC7W4w+DAuDt4ih3OESymH1vV7io7fDfQxko19fKHQ4RERERERHJiAkJum1l+lqsO3QJrmp7zLwnQO5wiGQVmxGL65XXMSTQEyrVJcSdz8GRC9flDotkUFCmx8ajWfDSOmBamL/c4RDJRutoj7lDu6O4ohpf/sINromIiIiIiGwZExJ0276Iz4SusgaTw1zxyan3kFeWJ3dIRLLILc1F7KVYeGm8oJAU6O13GRW1xXh3bzLfCrZB/z2YgaoaA2YPvgMOdvxzS7atb4ABtYosfJ+QiYvXyuQOh4iIiIiIiGTCERK6LefzS7EjMRchvlrolSfxc+bP2Hdxn9xhEcmifnaEt8YbAODv4gY3j1O4XFyI9YcvyRsctalTWUU4kFaAu7u5IyzARe5wiGQXlxELg9MulFSV4P196TAYuME1ERERERGRLWJCgkxWU2fAu3vTIEkS/nC3K/ZnxkKpUCL2UixyS3PlDo+oTf12dkT9pu6SJCHIrwqwv4yY05eRcLlY3iCpTVTV1OGD2HQoFHUwaH5Cflm+3CERyar+/qhxKoPGNRnJuUXYejpH7rCIiIiIiIhIBkxIkMm+PXEFWdcr8NCgAKQVH8b1yusI9QpFQUUBYjNi5Q6PqE39fnZEPR9nb7h1OoLKuhKs2ZuGimou3WTtvojPRH6JHsEBV3Eyfx8OZB6QOyQiWdXfH0O9QuGoPQHJTof/i89Erq5S7tCIiIiIiIiojTEhQSbJul6Br49dRhcPJ9zXW9lg3XxvZ2/OkiCb0tTsiHqSJCHAQwtXj5PIKS7D2gMZMkVJbSE1rxRbT+cgoJMS18VuKBVKHMg6wPsh2azf763j6+IJrcchlOv1eH9fOoTg0k1ERERERES2hAkJajWDQeC9fWkwCIG/juqJg1n7G7wZ7q3x5iwJsinNzY6o563xhsLxDFy1RdiTnI/jlwrbOEJqCzV1BqzZlwaFJKFPt0sorLrxRnhhZSHvh2Szfn9/9NZ4o0aRDn+vQpy5osNP567KHCERERERERG1JSYkqNW2JeYiJa8UkwbcAVdNeZPr5nOWBNmKm82OqCdJEny03lC57oNSWYc1+9JRWlXTxpGSpdUvYxcR6oLTBfuMb4R31nTm/ZBsUnN763g7e6NO/ROcVAKfHryIwvJqmSMlIiIiIiKitsKEBLVKrq4S/3fkErxdVJh1b9dm3wznLAmyFbeaHVHPW+ON0tps9OuWj6Lyanz888U2ipDawqWCcmw6fmMZO2fXxAZtwsvJi/dDskk36yMU6/PQr3sOyvV1eG9fGpduIiIiIiIishFMSFCLGQwCK3efR1WNAX99oBeKqq7edN18zpIga9eS2RH16n8ncmv2oK+fCnGp13AovaCNIiVLqq414J0952EwCMwc4oafszhrjOhWe+t4O3sjs/InDOquxvFLN5azIyIiIiIiIuvHhAS12OaTV5CSV4rJA+/AgAC3Fq2bz7eCyZq1dHZEPW+NN65XFqBnQDpc1HZ4f186Csr0Fo6SLO3LXzJxqaAcMwZ3waWyeM4aI0LL9tYpqChAF99UdHJ2wNoDGcgvqWrjKImIiIiIiKitMSFBLXLxWhk2/JKFAA81Zg/p2uJ18/lWMFmr1syOqFf/O/FL7j7MHOKBMn0tVv9048166piSsnX47lQ2enk5Y3iwHWeNEaHle+t4O3vjcHYsZg3xQGVNHe+HRERERERENsDkhMT+/fsxZswYeHl5QavVYujQodi0aVOrzjFgwABIktTsV0pKiqnhkRlV1xqwcs95CACLRgdBZads1br5fCuYrFFrZ0fUq/+dKK47jrGhPjh9WYcfTmdbKEqypMrqGwOo9koFFo0JwoHMOM4aI0Lr9tYpqCjA1eqjGN/fF0nZJdh6OqeNoiQiIiIiIiI52JnyTRs2bMDs2bNhZ2eHBx54AEqlEnv37sWMGTNw9uxZvPbaa7c8h16vR3JyMtzd3REZGdlkHVdXV1PCIzPb8EsmMq9X4E/3dEFPL61J6+bHXorFyO4j4av1baOoiSzHlNkR9X77O7H0vvuReEWNzw5nYoC/GwI7O1soYrKETw5cRH6JHo/fHwilna5Vs8Z4PyRrZWofYel99+NUliM+P3IJYV3c0aWTUxtFTERERERERG2p1TMk8vPzsWDBAmg0GsTHx2Pnzp3Yvn07EhIS4O3tjddffx0nT5685XkSExNRW1uLUaNG4Ysvvmjyy9eXgzVyS8rW4ftT2ejl7YyHBgUAMG3dfL4VTNbE1NkR9ep/J45c2Y+/RQRBAHh7dyqqaurMGyhZTPzF69iTnI87u7hhfD9fzhoj+h9T+whHruzHc6ODUGcQeGdPKqprDRaOlIiIiIiIiOTQ6oTEBx98gMrKSjz11FMICwszHg8ODkZ0dDSEEFi9evUtz1OftLjrrrtaGwK1kQbLkYwOglIh3da6+Vw7nazB7cyOqPfb3wmNugyz7+2Ky4WVWH/4knmDJYsoKNNjzd40OKvs8PSoXsgryzPpjXDeD8na3G4fwVVTjumDAnDxWjm+iM+0cLREREREREQkh1YnJLZv3w4AiIqKalQWFRUFSZKwbdu2W57n1KlTAJiQaM8+jEtHfokejw7rBn/3G0sn3O66+XwrmDq62IxYZJdmQwiBvLK8m37ll+c3WyaEQHZpNmIzYjH1Tj+E+rli+5lcHM0olPsS6SYMBoF3dqeitKoWT4/qBU9nFWeNEf2POfoIDw8OQG8fLb4/lY1TWUUWipSIiIiIiIjk0qqEhBACycnJAIDQ0NBG5e7u7vDx8UFRURGys2++SWv9DImcnBw8+OCD6NSpE7RaLR544AHs2rWrNWGRBcSmXEVs6jUM7uaB8f1uLJ1lrnXz+VYwdWT55fno7tYddgo7KCWlyV92Cjt0d+uO/PJ8KBQSFo0OgkalxJq9aSiuqJb7MqkZXx+/jKTsEozv74shPTpx1hjR/5irj3CtIh+LI3pDba/Eyj3noauosVDEREREREREJIdWbWpdVFSEqqoqaLVaaDSaJuv4+voiNzcX+fn58PPza7JOXV0dEhMTAQBz587FgAEDcP/99yM1NRWxsbGIjY3F22+/jeeff76Vl0PmkFNciQ/jLsBd44BnHuxlHFiof/Oxn1c/k87rrfHGmfwziM2IxZ/6/8mcIRO1mWfvfRYCokV1CwsL4eHhcdM6Em78fnXWqvDkyJ74985UrP4pDSsm9IFCYdqSUGQZSdk6fHU0C909NZg3rDsA0++LvB+StTF3H2HhyB54Z/d5rN57Hism9DF5iTwiIiIiIiJqX1o1Q6K8vBwA4OTk1GwdtVoNACgrK2u2zrlz51BZWQlHR0ds3boVCQkJ+O6773D27Fl89dVXsLOzwwsvvIBjx441ew69Xo+SkpIGX3q9vjWXQ02oqTPg7V2pqKqtw/Ojg+Cqtgdg/nXz+VYwdVSSJEEhKcz29dvfp+G9OuOBYC+cyCxCzJkcGa+Sfq+kqgZv706FvVKBxRG94WCn4Kwxov+xRB9hRG8vjAz2wvFLRdh2hr8jRERERERE1qJVMySUSiUAtOhh02AwNFsWGhqKvLw8lJeXIzAwsEHZjBkzEB8fj9WrV+M///kP1q1b1+Q5oqOj8dprrzU49uKLL+KFF164ZWyWUlTU8dc63ngiF+dyijGpnxcCNAYUFt5Yzz7mbAwycjPgp/XDpdJLJp+/ft38mIQYTO873UxRty/W0A7IPExpCw/1d8eZrOv4ZH86/DQCgZ2aTwBT2xBCYGVsJvKLK/DYMH9oUIXCwirEnI1B7rVc9PHsg8qSyma/v7K06TIX4YKz185a9f2QfmXNfxss1UeY0d8dZzIL8Mn+NARoBLp4qM0XtEzkbge3mrVHRERERERkaa1KSDg7OwMAKitvMvDyv7L6us3x9m5+w8OJEydi9erVOH78eLN1lixZgkWLFjU4plKpoFKpbvpzLa0jP+idzCrCzpQi9PFzx4KRwbBT/jqBptyuHL39e5vl5/R27Y1yu/IO/W91K9Z8bdQ6prSFZRPV+Ns3p/HxkTy8+8c7oXZQWiAyaqmtp3NwJrcCD/b1RdTgHpAkCbmluThWdAz+3v5wcr510kjt0vRAaoAyAMeKjmGi/UT4an3NHTq1M9b6t8GSfYSlEx2x+Nsz+OSXfLzz0AA42nf8+6G1tgMiIiIiIqKWaFVCQqvVQqvVQqfTobKy0rg802/l5t6YVu/ra/rAio+PDwCgoqKi2TrtIflgTYorqrFqz3moHZR4YWzvBskIoHXr5rdE/br5RNRYTy9nPDqsG9YeyMCH+y9g0egguUOyWelXy7DuUAZ8XR2xcETPBnvqZJdmI8AlAHlleTc9R1V5FRwVjk2W1b8Rzr0kqCOzZB8hyFuLWfd0wedHMvHfQxlYOKKn2X4OERERERERtb1W7SEhSRL69u0L4MY+EL9XWFiIvLw8uLu7N7uhNQBs3rwZM2fOxKefftpk+cWLFwEA/v7+rQmPTGQwCKz+KQ3FFTV4cmRPeLs0Hjiz5Lr5RNTYpAF3YFA3d8SmXEVsylW5w7FJZfpavLkjBUIAL4zt3WCmSn55Prq7dYedwg5KSWnyl53CDt3duiO/PF/GKyW6PZbuI0wL80d/f1fsSMzD4QsFMl0lERERERERmUOrZkgAwLhx4xAfH48tW7YgLCysQdmWLVsghEBkZORNz1FYWIgvv/wSycnJmDdvXqMHz88++wwAEBER0drwyAQxZ3JwIrMID4Z44/6gznKHQ0S4McD37Kgg/PWrU/gw7gKCfLTwc+v466d3FEIIvPvTeeSXVOHx+wPR00vboLw1b4QXFhbecokWzhojap5CIeG50UF45qtTWLM3DT06Ozf58gQRERERERG1f62aIQEA8+bNg5OTE1auXInDhw8bj6empmLZsmUAgMWLFxuP5+bmIiUlxbiUEwD84Q9/gIeHBxISEvD3v/8dQvw6qPPJJ5/g22+/hZeXF/7yl7+YdFHUcjeWI7mEO9wc8Vh44K2/gYjajKuTPZ4fHYSq2jq8vSsVNXUGuUOyGVsSshF/sRDDe3lifL/GSxBy1hhR2/J0VmHR6CCU6+vw752pqOX9kIiIiIiIqENqdULC398fa9asQUVFBcLDwzF69GhMmDABAwcORF5eHqKjozFgwABj/SVLliAkJARLliwxHnNzc8P//d//QaVS4dVXX0VwcDCmT5+OAQMG4LHHHoOzszO+++47bvpnYZXVdXhrVwoAYHFEMDfOJWqHBgS44Q93+SP9ahk+O3xJ7nBswtkcHdYfugQ/NzX++kAvJguI2om7unpgapgfzueX4vMjmXKHQ0RERERERCZodUICAObPn48dO3Zg+PDhiI+Px8GDBxEWFobNmzfjpZdeatE5IiMjcfz4cfzxj3+ETqfD1q1bcf36dcybNw9nzpzBsGHDTAmNWkgIgfdj05BTXIVHh3VDTy9nuUMiomY8fHcXBPto8UNCDk5kFsodjlUrrqjGv3emwl6pwNLIECZqidqZ2fd2RW8fLb4/lY1jl3g/JCIiIiIi6mgk8dv1kui2tGSd8PZiZ1IePohNxz3dPbBsfAjfADajjtQOyLLM2RbyS6rw9MZTsFcqsObhO+GhcTDLeelXBoPAiq1JOH1Zh0WjgzAy2Mss5+U9gQC2A3O6WlKFZ75KgCQBax6+E57OKrlDajG2A/nxMyBTse2Qqdh2yFRsO2Qqth1q70yaIUEd28VrZfj45wvw0qrwzINcjoSoI/B2ccRfH+gFXWUNVu5JhcHAXLK5bTyWhdOXdRgb6mO2ZAQRmZ+XiyOeebAXSqtq8dbOVNTxfkhERERERNRhMCFhYyqr6/CvnSmoE8ALY4OhdbSXOyQiaqH7enkioq83Tl/WYfPJK3KHY1VOZBbh62OXEdhZgwXDA+UOh4hu4d7ATpg04A4k55bgy1+4nwQREREREVFHwYSEDRFC4IPYdOQUV2HesG7o7aOVOyQiaqU/Dw9EFw8nfBGfiZS8ErnDsQrXSvV4Z3cq1PZKvDQuGA52/NNI1BHMHdYNvbyc8c2JKziVVSR3ONSE/fv3Y8yYMfDy8oJWq8XQoUOxadOmVp3j/PnzePTRR9GlSxc4ODjAw8MDERER2LVrl4WiJiIiIiIiS+Koiw3ZnZyP/eev4e7uHpg04A65wyEiEzjaK7E4ojeUCglv70pFmb5W7pA6tJo6A/69MwWlVbV4bnQQfF3VcodERC1kr1Rg8djecLRXYuWe8ygsr5Y7JPqNDRs2YOTIkYiLi0NYWBjCw8Nx8uRJzJgxA6+88kqLznHo0CGEhYVh/fr1cHR0xPjx49GzZ0/s3r0bY8eOxdtvv23hqyAiIiIiInNjQsJGZBSU4//tv7FvxLPcN4KoQ+vmqcGfhwciv0SPD2LTIQTXTzfVfw9mICWvFFPu9MO9gZ3kDoeIWsnXVY2nH+iF4ooavL2b++u0F/n5+ViwYAE0Gg3i4+Oxc+dObN++HQkJCfD29sbrr7+OkydP3vQctbW1mD17NsrLy/Hmm28iNTUV33//PY4ePYrdu3fDwcEBL774IpKSktroqoiIiIiIyByYkLABldV1eHPHOe4bQWRFxoX6YEiPTjiYVoBdZ/PlDqdDik25im1nchHq54I5Q7rKHQ4Rmei+Xp4Y188HiVd0+Pr4ZbnDIQAffPABKisr8dRTTyEsLMx4PDg4GNHR0RBCYPXq1Tc9R1xcHDIyMjB48GC8+OKLDV6mGT16NB577DEYDAZ8/fXXlroMIiIiIiKyACYkrBz3jSCyTpIk4a8P9ERnrQof/3wBF6+VyR1Sh3LhWhne25eGTs4OeHFsMOyU/HNI1JH9+b5AdPfU4KujWUi8opM7HJu3fft2AEBUVFSjsqioKEiShG3btt30HKWlpRg8eDDGjRvXZHlQUBAAICcn5/aCJSIiIiKiNsURGCvHfSOIrJfW0R4vjg2GQQD/2pmCimruJ9ESJVU1iP7xHASAJeNC4ObkIHdIRHSbHOwUePF/m9K/tTsVxRXcT0IuQggkJycDAEJDQxuVu7u7w8fHB0VFRcjOzm72PFOmTMHRo0fx2muvNVl+9OhRAIC/v78ZoiYiIiIiorbChIQVq983ojP3jSCyWr19tHh0WDfkFFdhzV7uJ3ErBoPAWztTkV+ix+PhPThrjMiK+Lmp8eTInigqr8a/d6WijvtJyKKoqAhVVVXQarXQaDRN1vH19QVwY68JUyQmJmLjxo2QJAlTp041OVYiIiIiImp7TEhYqTJ9LaJ/rN83ojf3jSCyYpMG3IGhPTrhUHoBtifmyh1Ou/Z/8ZlIuFyMiL7eGBvqI3c4RGRmI3p7IbKfLxKv6LDhl0y5w7FJ5eXlAAAnJ6dm66jVagBAWVnrlxu8evUqpk2bhrq6OsydOxcDBgxotq5er0dJSUmDL71e3+qfSURERERE5mMndwBkfgaDwOo955Grq8Lj9wci2MdF7pCIyIIkScLTo3rhwrVyrD2Qgd7eWvTy5pv/v3c4vQDfnriCXt7OeCy8h9zhEJGFzL+vO9KuluKb41fQ21uLewI7yR2STVEqlQDQopm5BoOhVefOycnB6NGjkZaWhkGDBuH999+/af3o6OhGSz4988wzWLFiRat+LhFwY/YPkSnYdshUbDtkKrYduh0eHh4W/xlMSFihzSev4JeMQtwf1Bnj+/nKHQ4RtQGNyg4vjQvGC9+exr92pmDVjIGcGfUbWdcrsPqnNLiq7bE0MgQOdpwgSGStHOwUeGlcMJ79KgEr95zH6j8OhK+rWu6wbIazszMAoLKystk69WX1dVsiKSkJEyZMQGZmJgYPHoxdu3bddBYGACxZsgSLFi1qcKy8vLxNHrLIOrHtkKnYdshUbDtkKrYdas84ImNlEi4X44v4THTp5ISnHujJfSOIbEhPL2c8Fh6I/BI9Vv+Uxv0k/qe0qgZv/JgMfW0dXhoXDE9nldwhEZGFeWkd8fyY3qisqUP0jynQ19bJHZLN0Gq10Gq10Ol0zSYlcnNvLC9Yv5fErezZswfDhg1DZmYmIiIisG/fPri7u9/y+1QqFVxcXBp8qVT8G0BEREREJCcmJKzItVI93tqVApW9EksjQ+Bor5Q7JCJqYxF9fXB/UGcczSjE5pPZcocju9o6A/61MwU5xVWYPzwQoX6ucodERG3krq7uePjuLsgoKMdHcRflDsdmSJKEvn37AgDOnTvXqLywsBB5eXlwd3eHn5/fLc/35ZdfIjIyEiUlJZg/fz62bdvWqpkVRERERETUvjAhYSWqaw2I3nEOJZW1eO7BIPi5cWkCIlskSRKeHNkTXTyc8PmRSziRWSh3SLJaezADpy/rMDbUBxP7cwk7IlszY1AA7urqjp/O5WP32Ty5w7EZ48aNAwBs2bKlUdmWLVsghEBkZOQtzxMTE4M5c+agtrYWr776KtauXQs7O644S0RERETUkTEhYSXWHryItPwyTAvzw5Ae3LyRyJapHZRYPiEETg5K/HtnKrKLm1/H25r9mJiL7WdyEernisfDA7mEHZENUigkLBoThM5aFT7afwHpV8vkDskmzJs3D05OTli5ciUOHz5sPJ6amoply5YBABYvXmw8npubi5SUFONSTgCQn5+PuXPnoq6uDsuXL8crr7zSdhdAREREREQWw4SEFdiTnI8diXno7++K2UO6yR0OEbUDvq5qvDA2GFU1dXhjezIqq21r/fSEy8X4f/svwMfVEUsig2Gn5J87Ilvl4miPJeOCIQBE/3gOuooauUOyev7+/lizZg0qKioQHh6O0aNHY8KECRg4cCDy8vIQHR2NAQMGGOsvWbIEISEhWLJkifHYypUrUVhYCDs7O1y4cAGzZs1q8uvDDz+U4xKJiIiIiMhEnPPcwSXnlOCD2HR01qqwOKI3lAq+AUxEN4R1cccjQ7th3aFLeGd3KpZGhkBhA/eI7OJK/GvHjf10VkzoAxdHe7lDIiKZ9fLWYuGInlizNw1v7jyHv08OhT0TlRY1f/58+Pv7480330R8fDyUSiXCwsLw/PPPY+rUqbf8/h07dgAAamtrsXHjxpvWfeKJJ8wSMxERERERWR4TEh3Y1dIqRO84B3ulhOXjQ+Dm5CB3SETUzky50w8Xr5Vj//lr+OrYZfzpni5yh2RRReXVeOWHJFRU12LFxD4I8HCSOyQiaidG9/HGpYJybD2dg49/vognR/aUOySrFxERgYiIiFvWW79+PdavX9/g2JkzZywUFRERERERyYmvhnVQVTV1+Me2cyiuqMFzo4MQ2NlZ7pCIqB2SJAlPPdATPTprsPFoFuJSr8odksVUVtfhtZizyC/RY+HInrirq4fcIRFROzPvvu4YEOCKnUl5+DEx99bfQERERERERGbFhEQHZDAIrNpzHhkF5Zh5TxcM7eEpd0hE1I452iuxfEIfdHJ2wLt705CUrZM7JLOrqTMgesc5XLhWjofv7oKIvj5yh0RE7ZBSIeHFscHwcXXE//v5olXeD4mIiIiIiNozJiQ6oK+OXcbhC9cxrKcnZgwOkDscIuoAPJ1VeGViX9grFPjH9mRcLqyQOySzEULgvb1pOJVVjIi+3nj4bt4Xiah5Wkd7vDy+D1RKBf754znk6irlDomIiIiIiMhmMCHRwfyUnI+NR7MQ2FmDZx/sBUmy/g1qicg8untq8FJkMCprDHh161lcL9PLHdJtE0LgkwMXEZt6DXd398ATI3ryvkhEt9SlkxOeHxOEMn0tXt16FiVVNXKHREREREREZBOYkOhATmYV4b3YdHTWqrBiQh842ivlDomIOpiwLu54amRPXC3V4+UfkqCr6LiDcEIIrD98CTGncxHq54LFEb2hVDAZQUQtc09gJzwWHoic4iq8HpMMfW2d3CERERERERFZPSYkOogL18rw5o8pUNsr8NqkvujkrJI7JCLqoEb38cafh3fH5cJKvPxDEko76JvBX/yShe9OZiPYR4sVE/oySUtErTah/x2IutMPKXmlWLnnPAwGIXdIREREREREVo0JiQ7gakkVXt16FrUGA5aP74MADye5QyKiDm7yQD/MurcLMgrK8erWZFRWd5w3g4UQ2PBLJjYdu4xeXs54dVJfqB2YjCAi0zw6tBuG9fTE4fTr+O+hDLnDISIiIiIismpMSLRzxRXVN5ZVqazB82N6I9TPVe6QiMhKPDQoANPv8sf5/NIOM1PCYBD4+OeL+OroZfTorMFrk/tCo7KTOywi6sAUCgmLRgchxFeLHxJysOn4ZblDIiIiIiIislpMSLRjJVU1WL4lCTnFVXgsPBDDenrKHRIRWRFJkjBnSFdMC/NDal4pln2fhOKKarnDalZtnQGrfzqPbWdu7Bnxz6n9oHW0lzssIrICDnYKvDyhD7p7avB/RzIRczpH7pCIiIiIiIisEhMS7VRFdS1e/eEsMq9X4JGh3TCh/x1yh0REVkiSJMwd1h2zh3RFRkE5Xtx8BjnFlXKH1UhFdS3+sf0cYlOv4e7uHnh1Ul84OXBmBBGZj9bRHn+f3Bd+bmp8/PNF7EnOlzskIiIiIiIiq8OERDtUWV2H17YmI+1qGWYMvrGkChGRJT00KAB/ub8H8nRVeH7TaSRl6+QOyShXV4nF35zBicwijArxwtLIEKjsuGcEEZmfm5MD/jElFN4uKry3Lw0/n78md0hERERERERWhQmJdqb0f8s0JeeWYPLAOzDzni5yh0RENmJ8f18sn9AHdQaB5VuSsDMpF0IIWWM6kVmERV+fxuWiCjw6rBueGdULSoUka0xEZN08nVX4R1Q/uDs54J3dqYhNvSp3SERERERERFaDCYl2RFdRg2XfJ+F8fin+MMgf8+/rDkniwBsRtZ3B3Tzw7+n90UnjgA9iL+Dfu1JRrq9t8ziqaw1Ye+AiXt16FnVC4OUJfTA1zJ/3RCJqEz6ujvjn1H5w1zhg1Z7z2JmUK3dIREREREREVoEJiXaioEyPpd8nIqOgHLPu7YI5Q7px4I2IZNHNU4PVfxyIoT064WBaAZ756hQSLhe32c9Pyy/F3745jR8SchDso8V7D9+Jwd082uznExEBgJ+bGv+a1h/eLo74IPYCfkjIljskIiIiIiKiDo8JiXYgo6Acf/vmNLIKKzDvvm6YMZjLNBGRvLSO9nhpXDAWjuiBoooavLwlCe/sTkVxRbXFfmaZvhYf7b+A5785jczCCswYHIA3/zcYSEQkB28XR7w5tR+6eDhh7YEMrDuUAYNB3qXsiIiIiIiIOjI7uQOwdScyi/CvHSmorjNg0ZggjOztJXdIREQAAEmSMK6fL8K6uuPDuAuIS72GXy4WYsIAX0we6AdXtb1Zfk65vhZbT+fgh4RslOvr0MfXBQtH9kDXThqznJ+I6HZ0clbhn1P74R/bkvHdyWzk6aqwaEwQVHZKuUMjIiIiIiLqcJiQkIkQAjuS8vD/9l+A2kGJf0wMRaifq9xhERE14u3iiFcm9sGRi9fxRXwmvjl+BdtO52JEcGeMDvFGTy9nk5aYu3itDLuT8xGXehXl+jr4uDri8fAeuD+oMxTcuJqI2hFXtT3+MSUUq39Kw8G0AhR8l4SXJ4TAzclB7tCIiIiIiIg6FCYkZFBVU4f/xKYjNvWacaAvwMNJ7rCIiJolSRKG9vDEvd074WB6Ab45cQU7EvOwIzEPAR5qhHVxx4AAN3T31KCTxqFRgkIIgcLyalwsKEfiFR1OZBYhq7ACANDFwwl/vs8PI4O9oGQigojaKZWdEovH9IavqyO+OX4Fz3yVgBfG9kbfO/hCCRERERERUUsxIdHGcoorEb0jBZcKyjG4mweeG90LWkfzLHtCRGRpCoWE8KDOGN7LExeuleGnc1dxKL0APyTk4IeEHACA2l4JVyd7ODkoIcSNJGxRRTWqagzG87g52WNsqA8eDPFGkLdpMyyIiNqaQiFhzpBu6OLhhPf3pWPpd4l4ZGg3TLnTj/cxIiIiIiKiFmBCoo0IIbDrbD7+ezADVbV1mH1vV0y/y5/LkhBRhyRJEnp6adHTS4vHwwNx6XoFknNKcOl6OXJ1ldBV1qJcXwtAgspegRBfF3i7OKK7pwa9fbTo3knD+x8RdVgjensh0NMZ0TvOYd2hS0jKLsFfH+gJdw2XcCIiIiIiIroZJiTaQGF5NdbsTcOJzCJ0cnbA0vEhGBjgJndYRERmIUkSuntq0N2Tm1ATke3o0skJKx8aiP/EpSMu9RoWbjiJJ0b0wPBenpwtQURERERE1AwmJCzIYBDYdTYPnx/JRJm+FiODvfBYeCCcVfxnJyIiIuro1A5KPD+mN4YEdsIHcel4a1cqDqUX4M/DA9FZq5I7PCIiIiIionaHI+MWkppXio/2X0D61TJ4aBywZFQwhvbwlDssIiIiIjKzoT090ecOF3y4/wIOp1/H8cwiPDTIH1Pu9IeDnULu8IiIiIiIiNoNJiTMSAiBK0UV+PKXLBxIK4BCIWFqmB/+OLgL1A5KucMjIiIiIgtxc3LAknEhOJFZiI9/vogv4rOwJzkfM+/pivuDOkOhkCCEkDtMIiIiIiIiWTEhYSaJuVlY8cMu6Kt7QinZ466u7pg3rDu6dHKSOzQiIlkIIbiOOhHZnLu6euD9P7kh5nQOvjp2GSv3nMem45cxtp8Wp3O+xmPDF8DH2UfuMImIiIiIiGTBOeRm8vaeYziXawdnpyL8e3p/vDqpL5MRRGSz8srysPLISuSV5ckdChFRm7NXKjA1zB+fPjIIDw0OwPWyavx71zlsPVOLfRf3yR0eERERERGRbJiQMIPc0lzUqffjDq8DcPXaATfncrlDIiKS1b6L+/Bz5s8ceCMim6Z1tMfse7vijWkB0LqehatzJmIvxSK3NFfu0IiIiIiIiGRhckJi//79GDNmDLy8vKDVajF06FBs2rSpVecoKSnB8uXLERwcDLVaDX9/fzzxxBO4evWqqWHJIjYjFhWGK7jLvxMKKgoQmxErd0hERLLJLc1F7KVYKBVKDrwREQE4lvsznNyOI8y/M/uKRERERERk00xKSGzYsAEjR45EXFwcwsLCEB4ejpMnT2LGjBl45ZVXWnSO0tJSjBw5Em+88QZqa2sxYcIEODs746OPPkJYWBiuXLliSmhtrn7gzUvjBYWkgLezNwfgiMimxWbE4nrldYR6hXLgjYhsHvuKREREREREv2p1QiI/Px8LFiyARqNBfHw8du7cie3btyMhIQHe3t54/fXXcfLkyVue55VXXsHJkycxZ84cpKSk4JtvvkFycjKee+45ZGdn48knnzTpgtpa/cCbt8YbAOCt8eYAHBHZLA68ERE1xL4iERERERHRr1qdkPjggw9QWVmJp556CmFhYcbjwcHBiI6OhhACq1evvuk5SkpK8PHHH8PJyQmrV6+GnZ3djWAUCrz11lsIDAzE1q1bceHChdaG16Z+O/AmSRIAQJIkDsARkc3iwBsR0a/YVyQiIiIiImqo1QmJ7du3AwCioqIalUVFRUGSJGzbtu2m59i/fz/Ky8sRHh4Od3f3BmVKpRITJ05s8LPaq98PvNXjABwR2SIOvBERNcS+IhERERERUUOtSkgIIZCcnAwACA0NbVTu7u4OHx8fFBUVITs7u9nznD17ttlzAECfPn0AAImJia0Jr001NfBWjwNwRGSLOPBGRPQr9hWJiIiIiIgaa1VCoqioCFVVVdBqtdBoNE3W8fX1BXBjr4nm5OTkNKhryjnk1tzAWz0OwBGRLeHAGxFRQ+wrEhERERERNdaqhER5eTkAwMnJqdk6arUaAFBWVmbyeVpyDr1ej5KSkgZfer3+5hdgJjcbeKvHATgisiUceCMi+hX7ikRERERERE2za01lpVIJAM0+WP2WwWC47fPc7BzR0dF47bXXGhx78cUX8cILL9wyttsVczYGuddy0cezDypLKo3HK0srG9RzES44e+0sYhJiML3vdIvHRe1DUVGR3CFQO2ErbSGvNA87EnfAReGCqtKqZuu5ClfsSNyBga4D4aP1acMI5WUr7YBuju3AtrTXvqKHh4fFfwYREREREdHNtCoh4ezsDACorKxstk59WX1dU87TknMsWbIEixYtanBMpVJBpVI1+z3mkFuai2NFx+Dv7Q8n58YzPNQu6gb/H6AMwLGiY5hoPxG+2qaXqCLrwwd+qmcLbWHnlZ0otytHP69+N000d9V2xZn8M0jQJeBPXf/UhhHKzxbaAd0a24FtYF+RiIiIiIioea1askmr1UKr1UKn0zWbTMjNvTHlvLn9IQDAz88PAJCXl2fyOVQqFVxcXBp8WToZAdx6WZLf4zIlRGTNWrIsST0uT0JEtoB9RSIiIiIioua1KiEhSRL69u0LADh37lyj8sLCQuTl5cHd3d2YdGhKaGgoACA5ObnJ8rNnzwIA+vXr15rwLK41A2/1OABHRNaMA29ERL9iX5GIiIiIiOjmWpWQAIBx48YBALZs2dKobMuWLRBCIDIy8qbnGD58ODQaDeLi4qDT6RqU1dXVISYmBpIkYezYsa0Nz6JaO/BWjwNwRGSNOPBGRNQQ+4oN7d+/H2PGjIGXlxe0Wi2GDh2KTZs2teocJSUlWL58OYKDg6FWq+Hv748nnngCV69etVDURERERERkSa1OSMybNw9OTk5YuXIlDh8+bDyempqKZcuWAQAWL15sPJ6bm4uUlBTjMkwA4OTkhPnz56O0tBSPP/44qqurAQBCCLzwwgvIyMhAVFQUgoKCTL4wczNl4K0eB+CIyBpx4I2I6FfsKza0YcMGjBw5EnFxcQgLC0N4eDhOnjyJGTNm4JVXXmnROUpLSzFy5Ei88cYbqK2txYQJE+Ds7IyPPvoIYWFhuHLlioWvgoiIiIiIzK3VCQl/f3+sWbMGFRUVCA8Px+jRozFhwgQMHDgQeXl5iI6OxoABA4z1lyxZgpCQECxZsqTBeV5//XWEhobi66+/RlBQEP7whz+gT58+WLlyJbp164YPPvjg9q/OjEwdeKvHATgisiYceCMiaoh9xV/l5+djwYIF0Gg0iI+Px86dO7F9+3YkJCTA29sbr7/+Ok6ePHnL87zyyis4efIk5syZg5SUFHzzzTdITk7Gc889h+zsbDz55JNtcDVERERERGROrU5IAMD8+fOxY8cODB8+HPHx8Th48CDCwsKwefNmvPTSSy06h4uLCw4cOIDnn38eABATE4Pq6mosXLgQR44cuemG1m3tdgbe6nEAjoisCQfeiIh+xb5iQx988AEqKyvx1FNPISwszHg8ODgY0dHREEJg9erVNz1HSUkJPv74Yzg5OWH16tWws7MDACgUCrz11lsIDAzE1q1bceHCBUteChERERERmZmdqd8YERGBiIiIW9Zbv3491q9f32SZm5sb3n77bbz99tumhtEmYjNikV2ajQCXAOSV5TVbr6q8Co4Kx2bLhRDILs1GbEYs/tT/T5YIlYjI4sw98Day+0j4attPEpqIqLXYV2xo+/btAICoqKhGZVFRUZg/fz62bdt203Ps378f5eXlGDt2LNzd3RuUKZVKTJw4Ee+++y62b9+Op59+2myxExERERGRZZmckLAl+eX56O7W/Zb1lJISSknZfAUJ6O7WHfnl+WaMjoiobbV04O1WrGXgjYiIfcVfCSGQnJwMAAgNDW1U7u7uDh8fH+Tm5iI7Oxt+fn5Nnufs2bPNngMA+vTpAwBITEw0R9hERERERNRGmJBogWfvfRYC4pb1CgsL4eHhcct6Ekx7o5iIqD1o6cDbLVnBwBsREcC+4m8VFRWhqqoKWq0WGo2myTq+vr7Izc1Ffn5+swmJnJwcY93mzgHc2K+CiIiIiIg6DiYkWkCSpBY9GCokBRSSSdtyEBF1GC0deGupjjzwRkQEsK/4W+Xl5QAAJyenZuuo1WoAQFlZmcnnack59Ho99Hp9o2NERERERCQf634iakN6vR5r1qzhQ46NYzugetbcFiRJMg6qmePL1H0oOgJrbgfUcmwHBNhOO1AqbyxJ1ZJ7u8FguO3z3Owc0dHRcHV1bfD18MMPW/1nQOZnK7+/ZH5sO2Qqth0yFdsO3Q69Xo9XX33V4u1HEkKY7zVXG1ZSUgJXV1fodDq4uLjIHQ7JhO2A6rEtEMB2QDewHRBgO+2g/jpdXV1RXFzcZJ277roLJ0+exLFjxzBo0KAm6yxatAirVq3CqlWr8OyzzzYqj4mJwaRJkzB+/PhmN8j+/QyJkpISBAQEWP1nQOZnK7+/ZH5sO2Qqth0yFdsO3Y62aj9csomIiIiIiMxCq9VCq9VCp9OhsrLSuLTSb+Xm5gJofn8IAMa9JfLy8posb8k5VCoVVCpVi2MnIiIiIiLL45JNRERERERkFpIkoW/fvgCAc+fONSovLCxEXl4e3N3dm93QGgBCQ0MBAMnJyU2Wnz17FgDQr1+/2w2ZiIiIiIjaEBMSRERERERkNuPGjQMAbNmypVHZli1bIIRAZGTkTc8xfPhwaDQaxMXFQafTNSirq6tDTEwMJEnC2LFjzRY3ERERERFZHhMSZqJSqfDKK69wWriNYzugemwLBLAd0A1sBwTYVjuYN28enJycsHLlShw+fNh4PDU1FcuWLQMALF682Hg8NzcXKSkpxmWYAMDJyQnz589HaWkpHn/8cVRXVwMAhBB44YUXkJGRgaioKAQFBbU4Llv6DMi82HbIVGw7ZCq2HTIV2w7djrZqP9zUmoiIiIiIzOrTTz/FggULoFAoMHLkSKhUKuzduxdVVVWIjo7GSy+9ZKw7d+5cfPbZZ3jkkUewfv164/GSkhIMGzYMSUlJ6Nq1KwYPHoykpCSkpKSgW7duOHz48E33kCAiIiIiovaHMySIiIiIiMis5s+fjx07dmD48OGIj4/HwYMHERYWhs2bNzdIRtyMi4sLDhw4gOeffx4AEBMTg+rqaixcuBBHjhxhMoKIiIiIqAPiDAkiIiIiIiIiIiIiIrI4zpAgIiIiIiIiIiIiIiKLY0LCDPbv348xY8bAy8sLWq0WQ4cOxaZNm+QOi26DwWDAxx9/jCFDhsDFxQWOjo7o3bs3XnzxRRQXFzeqf/HiRcyZMwddu3aFWq1GSEgIoqOjUVNT0+T59Xo93nnnHfTr1w8ajQbe3t6YNWsWLly4YOErI1Pp9Xr0798fkiQhPT29UXl+fj6eeuop9OzZE2q1GoGBgXjxxRdRWlra5PkMBgP++9//YtCgQXBxcYGnpyeioqJw4sQJS18KmeDKlSv4y1/+gq5du0KlUsHHx6fZ31m2Bev19ddfY9iwYXB2doajoyP69u2Lv//976iqqmpUl+3Auuzfvx8KhQJr165tsrykpATLly9HcHAw1Go1/P398cQTT+Dq1avNnvP777/HfffdB3d3d7i5uWH06NHYu3dvs/Vb29ewFeboh5vy+ZF1MEf7OX/+PB599FF06dIFDg4O8PDwQEREBHbt2mWhqKk9sMQYwJdffglJkjBr1iwzRUntkbnazoYNGzB8+HC4urpCrVYjLCwMH3/8MbgIivUyR9vJzc3FX/7yF+PfLE9PT0yePBnx8fEWiprak1s90zTHImOYgm7LF198ISRJEvb29iIiIkJERkYKlUolAIgVK1bIHR6ZoK6uTkyZMkUAEE5OTmLEiBFi/PjxonPnzgKA6Nmzp8jLyzPWT0pKEu7u7gKAuOeee8TUqVONdUeNGiVqamoanL+mpkZERkYKAOKOO+4Q06ZNE3feeacAILRarTh9+nRbXzK1wKJFiwQAAUCkpaU1KMvJyRFdu3YVAES/fv3EtGnTRJcuXYz/r9PpGp3vscceEwCEu7u7mDJlihg6dKgAIOzt7cWuXbva6rKoBU6ePCk8PDwEABESEiKmTJkiAgMDBQDh6ekpLl26ZKzLtmC9XnrpJePnMmrUKDFhwgTh5uZmvPdXVFQY67IdWJeUlBTh6+srAIhPPvmkUXlJSYkICwsTAESPHj3E9OnTRe/evQUA4efnJy5fvtzoe9544w0BQGg0GjFx4kQxatQooVQqhSRJ4tNPP21Uv7V9DVthjn64KZ8fWQdztJ+DBw8KjUYjAIhevXqJqKgoMXjwYGOf8a233rLwVZAcLDEGkJWVZexXzJw508wRU3thrrbz6KOPCgDC0dFRREZGigceeEA4ODgIAGLx4sUWvAKSiznaTkZGhvDx8REARPfu3cWUKVOMfSClUik2bdpk4asgOd3qmaY5lhrDZELiNuTl5Qm1Wi2cnZ3FiRMnjMfPnTsnvL29hSRJDY5Tx7B27VoBQPTu3VtkZGQYj5eUlIiJEycKAOIPf/iD8Xj9Dfzjjz82HtPpdCI8PFwAECtXrmxw/nfffVcAEA8++KAoLy83Hl+1apUAIAYOHCgMBoPlLpBabe/evUKSpGYTEvUJrKVLlxqP6fV68dBDDwkA4umnn25Q/4cffjAOTBYUFBiPf/vtt0KpVApfX98GbYPko9frRXBwsAAg3nzzTePx2tpasXDhQgFATJ482XicbcE6nTlzRkiSJDw8PERSUpLx+PXr18XAgQMFAPGvf/3LeJztwHrs3btXeHl5Ge//TXXen3vuOQFAzJkzx5gYqKurMx6fNGlSg/oJCQnGDv1v+xkHDhwQGo1GqNVqkZ2d3eB7WtvXsAXm6oe39vMj62CO9lNTUyO6d+9u7CP8tv++e/du4eDgIBQKhUhMTLTYdVDbs8QYgMFgECNHjjT+rWFCwjqZq+189tlnxvGK374YlZSUZHyJ6tSpU5a4BJKJudrO9OnTBQCxcOFCUVtbazz+6aefGl+MqqystMg1kLxa8kzTHEuNYTIhcRtefvllAUC89NJLjcr++9//CgBi9uzZMkRGt2PYsGECgNi2bVujsmvXrhmz0hUVFWLv3r0CgBgyZEijuunp6UKSJNGlSxfjL6fBYDC+JZuSktLoe+oHFvbu3Wv+CyOTFBUViYCAABEUFGR8m+C3CYm0tDQhSZIICAho9IZqcXGxcHFxEWq1WpSWlhqPDx8+XAAQO3fubPTz5syZIwA0+YYstb0vvvhCABDTpk1rVFZeXi66dOkiBgwYIGpra9kWrNg777wjAIgnnniiUdmXX34pAIjIyEghBO8J1iI/P1888cQTQqFQCDs7O+Pf7t933nU6ndBoNMLJyUkUFhY2KKutrTXOpkpPTzcenz17tgAgPvroo0Y/d8WKFQKAePnll43HWtvXsBXm6Ieb8vmRdTBH+9mzZ48AIAYPHtxk+VNPPSUAiOXLl5slZmofLDEG8PbbbwsAxmdBJiSsk7naTs+ePYVSqRQJCQmNyv75z3+KgICAVg02Uvtnrraj1WoFAJGfn9+oLCgoSAAQR48eNUvM1D609JmmOZYcw+QeErdh+/btAICoqKhGZVFRUZAkCdu2bWvjqOh2ubu7Izg4GPfee2+jMk9PT7i7u6OmpgYFBQXGNjB58uRGdXv06IH+/fsjKysLiYmJAICkpCRkZWUhODgYvXv3bvQ9U6ZMAQC2m3Zk4cKFyMnJweeffw6VStWofMeOHRBCYPz48bCzs2tQ5urqipEjR6KyshL79u0DAOh0Ohw6dAjOzs4YNWpUo/OxDbQv3377LQBg0aJFjcqcnJyQmZmJhIQEKJVKtgUrplDc6C5duXKlUdm1a9cAAB4eHgB4T7AW//znP/Hhhx+iZ8+e2LdvH0aOHNlkvf3796O8vBzh4eFwd3dvUKZUKjFx4kQAv/YZAeDHH38E0HT/sanPu7V9DVthjn64KZ8fWQdztJ/S0lIMHjwY48aNa7I8KCgIAJCTk3N7wVK7Yu4xgMTERCxbtgwTJ07Eo48+aq4wqR0yR9s5ffo00tPT8cADD2DAgAGNypcsWYKsrCz8+c9/NkvM1D6Y677T3DNNTU0NdDodgF+facg6tPSZpjmWHMNkQsJEQggkJycDAEJDQxuVu7u7w8fHB0VFRcjOzm7r8Og2xMTE4Ny5c+jUqVOjsgsXLqCwsBAODg7o3Lkzzp49C6DpNgAAffr0AQDjIEFr65O8Nm7ciI0bN2LJkiW45557mqzT2s/03LlzMBgMCA4ObjRY2VR9kteJEyegUCgwaNAg5Obm4p133sFjjz2Gv/3tb9i/f3+DumwL1isiIgKSJCEmJgYrVqxAXl4eSktLsWnTJqxYsQIqlQp//etfAbAdWIvAwED85z//QVJSEoYPH95svdZ+3nl5ebh+/To8PT3h7e3dqH5ISAgkSUJycjLq6upM+hm2wFz9cP7b2iZztZ8pU6bg6NGjeO2115osP3r0KADA39/fDFFTe2DuMQC9Xo+ZM2dCq9Xik08+MXu81H6Yq+2cOHECAHD33XdDCIGdO3fi+eefx4IFC7BmzRoUFRVZ5gJINua879Qn0GfPno0DBw6goqICaWlpePjhh5Gfn4+oqCj06NHD/BdBsmnpM01zLNlXbvzkSy1SVFSEqqoqaLVaaDSaJuv4+voiNzcX+fn58PPza+MIyRKWLl0KAJgwYQIcHR2Nbzz5+vo2Wb/+eH5+PgC0uj7J5/Lly1i4cCHCwsKwYsWKZuuxDVgvvV6Py5cvw9PTE9u3b8cjjzyC0tJSY/k777yDRx55BGvXroWdnR3bghULCQnB2rVr8fTTT+P111/H66+/bizr27cv1q1bh8GDBwPgPcFaPP300y2qZ+7PW6VSwd3dHYWFhdDpdPDw8GAbaYK5+uH8t7VNbfEcl5iYiI0bN0KSJEydOvV2Q6Z2wtxtZ+nSpUhMTMS3337bZJKarIe52k56ejoAwMXFBWPHjsXu3bsblP/jH//Ali1bMHToUPNeAMnGnPed9957D1euXMHBgwcRHh5uPC5JEpYtW3bTcQ/qmFr6TNMcS/aVOUPCROXl5QBuLNnRHLVaDQAoKytrk5jIslatWoVNmzbByckJb7zxBoBbt4Pft4HW1id5CCHwyCOPoLKyEp9//jns7e2brWupNlBfj+RTUlIC4MZn9/DDDyMiIgJnz56FTqdDTEwM/Pz88Nlnnxk7bmwL1u2+++5DREQE1Go17r//fkRERMDNzQ3JyclYtWoV9Ho9ALYDW2Puz9uU77HFvoO5+uH8t7VNln6Ou3r1KqZNm4a6ujrMnTu3yWVVqGMyZ9vZt28fVq1ahVmzZmHatGnmC5LaJXO1nfpldaKjo3HixAls2LAB169fR3p6Oh5//HFcu3YNkyZNQm5urhmjJzmZ877j4eGBRx55BO7u7ggMDMTkyZPRv39/CCGwbt06xMbGmi9wsgqW7CszIWEipVIJ4EYm8VYMBoOlwyELW716NRYtWgRJkvDpp58iODgYQMvbQX0baG19ksfKlSsRGxuLN954A3379r1pXUu2ASFES0MmC6gfYK6qqsKQIUPwzTffoE+fPnBxccGECROwZcsWSJKEVatWobi4mG3Bih09ehSDBw9GUlISTp8+jbi4OOzcuRPp6ekYPXo0Nm7ciAULFgDgPcHWWOrzNuV7bKnvYK5+OP9tbZMln+NycnIwcuRIpKWlYdCgQXj//fdNipHaJ3O1neLiYsydOxd+fn547733zBYftV/majv1zyfFxcX49ttv8ac//QkeHh7o0aMHPvroI0ycOBHXr1/HmjVrzBM4yc6cf7NmzpyJBQsWYNGiRUhPT8eWLVtw+vRpbN68GdeuXUNUVJRxeSgiwLJ9ZSYkTOTs7AwAqKysbLZOfVl9Xep4hBB44YUX8Nxzz0GpVGLdunX44x//aCy/VTv4fRtobX1qe/Uby4WHh+O55567ZX1LtQGNRtOiTgdZzm/fAnjyyScblQ8aNAiDBw9GVVUVjhw5wrZgxZ599lmUlJTg448/Rq9evYzHO3XqhC+++AIuLi7YsGEDMjMz2Q5sjLk/b1O+xxb7Dubqh/Pf1jZZ6jkuKSkJQ4cORXJyMgYPHozdu3ff9I1W6njM1XYWLlyIK1euYN26dXBzczNrjNQ+mavt1N9T+vbtixEjRjQqf+KJJwDcmIFD1sFcbWf37t346quvMHLkSCxfvrzBc8XUqVPxt7/9DVVVVXjnnXfMFDlZA0v2lbmHhIm0Wi20Wi10Oh0qKyuN01R+q36aXHNrbVH7VllZiVmzZuG7776DWq3Gxo0bMXny5AZ1/Pz8cOrUKeTl5TV5jt+3gfr1/Fpan9rekiVLoNfroVAoMGfOnAZlBQUFAIC//e1vcHZ2xrJly1r9mbINdByurq5wcHBAdXU1unfv3mSdbt264ejRoygoKGBbsFKVlZWIj4+HWq1uciOwzp07Y/Dgwdi7dy9Onz7NdmBjzP156/V6FBUVwcHBAR4eHsbvaU1fwxaYqx/O3z/bZInnuD179mD69OkoKSlBREQEvv32WyayrJA52s7x48exceNGeHh4YP369Vi/fr2x7OLFiwCAw4cPY9asWQgJCcGyZcvMfyHU5sx13+ncuTMA3PTZBPj1uZU6PnO1nfok1ZgxY5osj4yMRHR0NE6dOmWGqMlaWLKvzBkSJpIkybiUy7lz5xqVFxYWIi8vD+7u7tzQugMqKSnBqFGj8N1336Fz586IjY1tlIwAft1pvrlpbfU70vfr18+k+tT26te+i4uLw4YNGxp81a+f98MPP2DDhg3Iz89v9WcaEhIChUKBlJSUJqe1sQ20H0qlEiEhIQCA7OzsJuvU/2H28vJiW7BSOp0OQggolUooFE13m+zsbrzfUV1dzXZgY1r7eXfq1Ak+Pj7Iz8/H9evXG9VPTk6GEAJ9+/Y1tjf2HRozVz+c/7a2ydzPcV9++SUiIyNRUlKC+fPnY9u2bUxGWClztJ36Z43CwsJGzxpHjhwBAGRkZGDDhg3Ys2ePha6E2pq57jv1f49a8mxC1sFcbae4uBjAr88tv/fb5xmiepbsKzMhcRvGjRsHANiyZUujsi1btkAIgcjIyDaOim5XTU0Nxo8fjyNHjqBHjx44cuQI7rnnnibr3qwNXLhwAYmJiQgICDD+cvbu3RuBgYFISkrChQsXGn3P999/DwBsNzKKi4uDEKLJr65duwIA0tLSIITAiBEjMHbsWEiShG3btqGurq7BuXQ6HWJjY+Hk5IT7778fAIz/rdPpEBcX1+jnsw20L/Wfw1dffdWo7OrVqzhx4gRUKhXuuecetgUr5eXlBQ8PD5SVleHnn39uVK7T6XDs2DEAwMCBA9kObMzw4cOh0WgQFxdn3GiyXl1dHWJiYiBJEsaOHWs8Xt93+OGHHxqdr6nPu7V9DVthjn64KZ8fWQdzPcfFxMRgzpw5qK2txauvvoq1a9c2O9hD1uF2286IESOafdZYt24dgBvrvAshmuwXUMdljvvOAw88AEdHRyQkJCAlJaVR+Y4dOwAA4eHhtx8wtRvmaDv1L9r9+OOPTZbXJ0AHDhxoeqBkdSw6hinIZJcvXxZOTk5Co9GIQ4cOGY+npKQIHx8fAUAkJCTIGCGZYunSpQKA8PHxEVeuXLlpXYPBIAYOHCgAiHfffdd4XKfTifDw8EbHhRBi5cqVAoC4//77RUlJifH46tWrBQBx5513mveCyGy6du0qAIi0tLQGxydNmiQAiEWLFgmDwSCEEEKv14sZM2YIAOK5555rUP+7774TAERISIjIzc01Ht+8ebNQKpXC19dXVFVVWf6C6JYyMzOFs7OzACDWrl1rPF5WViYmTpwoAIjHHnvMeJxtwTq99NJLAoAIDg4Wly5dMh4vKSkRU6ZMEQBEZGSk8TjbgfV55JFHBADxySefNCp7+umnBQAxY8YModfrhRA3+geLFi0SAMSUKVMa1D927JhQKBTCx8dHpKSkGI8fOnRIaDQa4ejoKPLy8ozHTelr2ILW9sNzcnLEuXPnRE5OToPztPbzI+tgjvaTl5cnPDw8BACxfPnyNo2f5GOue09T1q1bJwCImTNnWiR2kpe5/27dddddIj8/33h8165dQqVSCbVaLdLT0y1/QdRmzNF28vPzhVarFQDEv/71L+MzihA32o5arRaSJIn4+Pi2uSiSxc2eaa5duybOnTsnMjMzGxy31BgmExK3ae3atUKSJKFUKsWDDz4oxo8fLxwdHQUAER0dLXd41EoFBQXCyclJABADBgwQM2fObParfrDg1KlTwsXFRQAQYWFhYtq0acLLy0sAEOPHjxc1NTUNfkZNTY144IEHBADh5eUlpk2bJsLCwgQA4e7uLs6ePSvHpVMLNJeQyMrKEn5+fsYBy+nTp4suXboYO4qlpaWNzjV79mwBQLi4uIioqChx3333CUmShEqlErGxsW10RdQSmzdvFvb29gKA6Nevn5g0aZKx4zdgwABRXFxsrMu2YJ2qqqrEqFGjBADh4OAgRo8eLSIjI4Wnp6fxs/7tADLbgfW5Weddp9OJ0NBQAUB07dpVTJ8+XQQHBwsAolu3bk0OQr388ssCgHB0dBTjx48XDz74oFAqlUKSJLFhw4ZG9Vvb17AVremH13+GjzzySIPjpnx+ZB1ut/288MILAoCws7MTDz/8cLPPDP/5z3/a+MrI0sxx72kKExLWzxxtp6yszPhCgrOzs5gwYYIYMmSIUCgUQqlUik8//bQNr4jaijnaTkxMjPF7evToIaZOnSruuusuAUBIkiRWrVrVdhdEsrjZM80rr7xiTDz8lqXGMJmQMIOdO3eKESNGCGdnZ+Hq6iqGDh0qNm/eLHdYZILNmzcLAC36+u2gdGpqqpgxY4bw9PQUarVahIaGirfeeqvZN1orKyvF3//+dxEUFCRUKpUICAgQs2bN4psM7VxzCQkhhMjOzhZ//vOfha+vr1CpVCIoKEgsXbpU6HS6Js9VV1cn3nvvPdG/f3/h6OgofHx8xOTJk8WpU6csfBVkijNnzogZM2YILy8voVKpRK9evcSKFStEWVlZo7psC9appqZGvP/+++Luu+8WGo1GqFQqERISIl5++eUmP1u2A+tys867EEIUFRWJ559/XnTt2lWoVCoRGBgoFi5c2GDGy+9t2LBB3H333cLJyUl4enqKBx988KbJp9b2NWxFS/vhNxsUNOXzI+twO+2nX79+LXpm4OCydTLHvef3mJCwDeZoO9XV1WLVqlVi4MCBQq1WCw8PDzFu3Dixf//+NrgCkos52s7Zs2fFnDlzhJ+fn7C3txedOnUSEyZM4AtQNsKUhIQQlhnDlIQQAkRERERERERERERERBbETa2JiIiIiIiIiIiIiMjimJAgIiIiIiIiIiIiIiKLY0KCiIiIiIiIiIiIiIgsjgkJIiIiIiIiIiIiIiKyOCYkiIiIiIiIiIiIiIjI4piQICIiIiIiIiIiIiIii2NCgoiIiIiIiIiIiIiILI4JCSIiIiIiIiIiIiIisjgmJIiIiIiIiIiIiIiIyOKYkCAiIiIiIiIiIiIiIotjQoKIiIiIiIiIiIiIiCyOCQkiIiIiIiIiIiIiIrI4JiSIiIiIiIiIiIiIiMji/j9hPjPzWv+nGwAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
        "<Figure size 1600x800 with 4 Axes>"
       ]
@@ -8080,9 +1628,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "env_orbit",
+   "display_name": "orbit39",
    "language": "python",
-   "name": "env_orbit"
+   "name": "orbit39"
   },
   "language_info": {
    "codemirror_mode": {
@@ -8094,7 +1642,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.9.18"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/examples/ktrlite.ipynb b/examples/ktrlite.ipynb
deleted file mode 100644
index d80dc285..00000000
--- a/examples/ktrlite.ipynb
+++ /dev/null
@@ -1,1118 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# KTRLite Examples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:07:43.615335Z",
-     "start_time": "2022-01-26T02:07:43.591401Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:07:46.042483Z",
-     "start_time": "2022-01-26T02:07:44.105558Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "pd.set_option('display.float_format', lambda x: '%.5f' % x)\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import orbit\n",
-    "from orbit.models import KTRLite\n",
-    "\n",
-    "from orbit.utils.features import make_fourier_series_df, make_fourier_series\n",
-    "from orbit.diagnostics.plot import plot_predicted_data, plot_predicted_components\n",
-    "from orbit.diagnostics.metrics import smape\n",
-    "from orbit.utils.dataset import load_iclaims, load_electricity_demand\n",
-    "from orbit.utils.plot import get_orbit_style\n",
-    "plt.style.use(get_orbit_style())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:07:46.076120Z",
-     "start_time": "2022-01-26T02:07:46.044442Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.1.4dev\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(orbit.__version__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:07:47.444705Z",
-     "start_time": "2022-01-26T02:07:46.906768Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(3288, 2)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/towinazure/edwinnglabs/orbit/orbit/utils/dataset.py:112: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.\n",
-      "  df[\"date\"] = pd.date_range(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>electricity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2000-01-01</td>\n",
-       "      <td>9.43760</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2000-01-02</td>\n",
-       "      <td>9.50130</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2000-01-03</td>\n",
-       "      <td>9.63565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2000-01-04</td>\n",
-       "      <td>9.65392</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2000-01-05</td>\n",
-       "      <td>9.66089</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        date  electricity\n",
-       "0 2000-01-01      9.43760\n",
-       "1 2000-01-02      9.50130\n",
-       "2 2000-01-03      9.63565\n",
-       "3 2000-01-04      9.65392\n",
-       "4 2000-01-05      9.66089"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# from 2000-01-01 to 2008-12-31\n",
-    "df = load_electricity_demand()\n",
-    "\n",
-    "DATE_COL = 'date'\n",
-    "RESPONSE_COL = 'electricity'\n",
-    "\n",
-    "df[RESPONSE_COL] = np.log(df[RESPONSE_COL])\n",
-    "\n",
-    "print(df.shape)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:07:49.256998Z",
-     "start_time": "2022-01-26T02:07:49.223030Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "starts with 2000-01-01 00:00:00\n",
-      "ends with 2008-12-31 00:00:00\n",
-      "shape: (3288, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'starts with {df[DATE_COL].min()}\\nends with {df[DATE_COL].max()}\\nshape: {df.shape}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Train / Test Split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:07:51.498636Z",
-     "start_time": "2022-01-26T02:07:51.465738Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "test_size=365\n",
-    "\n",
-    "train_df=df[:-test_size]\n",
-    "test_df=df[-test_size:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## KTRLite"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:07:54.536344Z",
-     "start_time": "2022-01-26T02:07:54.501278Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ktrlite = KTRLite(                   \n",
-    "    date_col=DATE_COL,\n",
-    "    response_col=RESPONSE_COL,\n",
-    "    seasonality=[7, 365.25],\n",
-    "    seasonality_fs_order=[2, 5],\n",
-    "    level_knot_scale=.1,\n",
-    "    level_segments=19,\n",
-    "    estimator='stan-map',\n",
-    "    n_bootstrap_draws=1e4,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:01.901847Z",
-     "start_time": "2022-01-26T02:07:54.908202Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "20:46:46 - cmdstanpy - INFO - Chain [1] start processing\n",
-      "20:46:46 - cmdstanpy - INFO - Chain [1] done processing\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<orbit.forecaster.map.MAPForecaster at 0x289028280>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ktrlite.fit(train_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:01.934988Z",
-     "start_time": "2022-01-26T02:08:01.904931Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2923, 3)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ktrlite._model.kernel_coefficients.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:09.576908Z",
-     "start_time": "2022-01-26T02:08:04.086518Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>prediction_5</th>\n",
-       "      <th>prediction</th>\n",
-       "      <th>prediction_95</th>\n",
-       "      <th>trend_5</th>\n",
-       "      <th>trend</th>\n",
-       "      <th>trend_95</th>\n",
-       "      <th>seasonality_7_5</th>\n",
-       "      <th>seasonality_7</th>\n",
-       "      <th>seasonality_7_95</th>\n",
-       "      <th>seasonality_365.25_5</th>\n",
-       "      <th>seasonality_365.25</th>\n",
-       "      <th>seasonality_365.25_95</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2008-01-02</td>\n",
-       "      <td>9.70162</td>\n",
-       "      <td>9.97555</td>\n",
-       "      <td>10.25148</td>\n",
-       "      <td>9.97975</td>\n",
-       "      <td>9.98128</td>\n",
-       "      <td>9.98277</td>\n",
-       "      <td>0.02645</td>\n",
-       "      <td>0.02645</td>\n",
-       "      <td>0.02645</td>\n",
-       "      <td>-0.03218</td>\n",
-       "      <td>-0.03218</td>\n",
-       "      <td>-0.03218</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2008-01-03</td>\n",
-       "      <td>9.70819</td>\n",
-       "      <td>9.97678</td>\n",
-       "      <td>10.24796</td>\n",
-       "      <td>9.97822</td>\n",
-       "      <td>9.98128</td>\n",
-       "      <td>9.98425</td>\n",
-       "      <td>0.02824</td>\n",
-       "      <td>0.02824</td>\n",
-       "      <td>0.02824</td>\n",
-       "      <td>-0.03274</td>\n",
-       "      <td>-0.03274</td>\n",
-       "      <td>-0.03274</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2008-01-04</td>\n",
-       "      <td>9.71065</td>\n",
-       "      <td>9.98740</td>\n",
-       "      <td>10.25998</td>\n",
-       "      <td>9.97669</td>\n",
-       "      <td>9.98128</td>\n",
-       "      <td>9.98574</td>\n",
-       "      <td>0.03911</td>\n",
-       "      <td>0.03911</td>\n",
-       "      <td>0.03911</td>\n",
-       "      <td>-0.03299</td>\n",
-       "      <td>-0.03299</td>\n",
-       "      <td>-0.03299</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2008-01-05</td>\n",
-       "      <td>9.64117</td>\n",
-       "      <td>9.91650</td>\n",
-       "      <td>10.18986</td>\n",
-       "      <td>9.97516</td>\n",
-       "      <td>9.98128</td>\n",
-       "      <td>9.98722</td>\n",
-       "      <td>-0.03186</td>\n",
-       "      <td>-0.03186</td>\n",
-       "      <td>-0.03186</td>\n",
-       "      <td>-0.03292</td>\n",
-       "      <td>-0.03292</td>\n",
-       "      <td>-0.03292</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2008-01-06</td>\n",
-       "      <td>9.58980</td>\n",
-       "      <td>9.86288</td>\n",
-       "      <td>10.13765</td>\n",
-       "      <td>9.97363</td>\n",
-       "      <td>9.98128</td>\n",
-       "      <td>9.98871</td>\n",
-       "      <td>-0.08587</td>\n",
-       "      <td>-0.08587</td>\n",
-       "      <td>-0.08587</td>\n",
-       "      <td>-0.03253</td>\n",
-       "      <td>-0.03253</td>\n",
-       "      <td>-0.03253</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        date  prediction_5  prediction  prediction_95  trend_5   trend  \\\n",
-       "0 2008-01-02       9.70162     9.97555       10.25148  9.97975 9.98128   \n",
-       "1 2008-01-03       9.70819     9.97678       10.24796  9.97822 9.98128   \n",
-       "2 2008-01-04       9.71065     9.98740       10.25998  9.97669 9.98128   \n",
-       "3 2008-01-05       9.64117     9.91650       10.18986  9.97516 9.98128   \n",
-       "4 2008-01-06       9.58980     9.86288       10.13765  9.97363 9.98128   \n",
-       "\n",
-       "   trend_95  seasonality_7_5  seasonality_7  seasonality_7_95  \\\n",
-       "0   9.98277          0.02645        0.02645           0.02645   \n",
-       "1   9.98425          0.02824        0.02824           0.02824   \n",
-       "2   9.98574          0.03911        0.03911           0.03911   \n",
-       "3   9.98722         -0.03186       -0.03186          -0.03186   \n",
-       "4   9.98871         -0.08587       -0.08587          -0.08587   \n",
-       "\n",
-       "   seasonality_365.25_5  seasonality_365.25  seasonality_365.25_95  \n",
-       "0              -0.03218            -0.03218               -0.03218  \n",
-       "1              -0.03274            -0.03274               -0.03274  \n",
-       "2              -0.03299            -0.03299               -0.03299  \n",
-       "3              -0.03292            -0.03292               -0.03292  \n",
-       "4              -0.03253            -0.03253               -0.03253  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predicted_df = ktrlite.predict(df=test_df, decompose=True)\n",
-    "predicted_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:09.612786Z",
-     "start_time": "2022-01-26T02:08:09.579224Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'SMAPE: 0.44%'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f\"SMAPE: {smape(predicted_df['prediction'].values, test_df['electricity'].values):.2%}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:11.983809Z",
-     "start_time": "2022-01-26T02:08:11.610954Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_predicted_data(training_actual_df=train_df,\n",
-    "                        predicted_df=predicted_df, \n",
-    "                        date_col=DATE_COL,\n",
-    "                        actual_col=RESPONSE_COL, \n",
-    "                        test_actual_df=test_df,\n",
-    "                        markersize=20, lw=.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:14.219458Z",
-     "start_time": "2022-01-26T02:08:13.701400Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_predicted_components(predicted_df=predicted_df,\n",
-    "                              date_col=DATE_COL, \n",
-    "                              plot_components=['trend', 'seasonality_7', 'seasonality_365.25'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:18.152201Z",
-     "start_time": "2022-01-26T02:08:17.850350Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = ktrlite.plot_lev_knots()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:20.974703Z",
-     "start_time": "2022-01-26T02:08:20.947780Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatetimeIndex(['2000-01-01', '2000-06-03', '2000-11-04', '2001-04-06',\n",
-       "               '2001-09-07', '2002-02-08', '2002-07-12', '2002-12-13',\n",
-       "               '2003-05-15', '2003-10-16', '2004-03-18', '2004-08-19',\n",
-       "               '2005-01-19', '2005-06-22', '2005-11-23', '2006-04-26',\n",
-       "               '2006-09-27', '2007-02-27', '2007-07-31', '2008-01-01'],\n",
-       "              dtype='datetime64[ns]', freq=None)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ktrlite._model._level_knot_dates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:27.595562Z",
-     "start_time": "2022-01-26T02:08:27.560929Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>lev_knot</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2000-01-01</td>\n",
-       "      <td>9.53615</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2000-06-03</td>\n",
-       "      <td>9.56628</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2000-11-04</td>\n",
-       "      <td>9.61450</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2001-04-06</td>\n",
-       "      <td>9.51648</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2001-09-07</td>\n",
-       "      <td>9.60272</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        date  lev_knot\n",
-       "0 2000-01-01   9.53615\n",
-       "1 2000-06-03   9.56628\n",
-       "2 2000-11-04   9.61450\n",
-       "3 2001-04-06   9.51648\n",
-       "4 2001-09-07   9.60272"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lev_knots_df = ktrlite.get_level_knots()\n",
-    "lev_knots_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Stability Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Test on different seeds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:38.938673Z",
-     "start_time": "2022-01-26T02:08:32.092182Z"
-    },
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:Optimizing(PyStan) with algorithm:LBFGS .\n",
-      "INFO:orbit:Optimizing(PyStan) with algorithm:LBFGS .\n"
-     ]
-    }
-   ],
-   "source": [
-    "ktrlite1 = KTRLite(                   \n",
-    "    date_col=DATE_COL,\n",
-    "    response_col=RESPONSE_COL,\n",
-    "    seasonality=[7, 365.25],\n",
-    "    seasonality_fs_order=[2, 5],\n",
-    "    level_knot_scale=.1,\n",
-    "    level_segments=19,\n",
-    "    seasonality_segments=0,\n",
-    "    estimator='stan-map',\n",
-    "    seed=2020\n",
-    ")\n",
-    "\n",
-    "ktrlite2 = KTRLite(\n",
-    "    date_col=DATE_COL,\n",
-    "    response_col=RESPONSE_COL,\n",
-    "    seasonality=[7, 365.25],\n",
-    "    seasonality_fs_order=[2, 5],\n",
-    "    level_knot_scale=.1,\n",
-    "    level_segments=19,\n",
-    "    seasonality_segments=0,\n",
-    "    estimator='stan-map',\n",
-    "    seed=2021\n",
-    ")\n",
-    "\n",
-    "ktrlite1.fit(df);\n",
-    "ktrlite2.fit(df);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:38.977514Z",
-     "start_time": "2022-01-26T02:08:38.940997Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.allclose(\n",
-    "    ktrlite1.get_point_posteriors()['map']['lev_knot'],\n",
-    "    ktrlite2.get_point_posteriors()['map']['lev_knot'],\n",
-    "    rtol=1e-3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Test on different segments"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:46.383010Z",
-     "start_time": "2022-01-26T02:08:38.980048Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:Optimizing(PyStan) with algorithm:LBFGS .\n",
-      "INFO:orbit:Optimizing(PyStan) with algorithm:LBFGS .\n"
-     ]
-    }
-   ],
-   "source": [
-    "ktrlite1 = KTRLite(                   \n",
-    "    date_col=DATE_COL,\n",
-    "    response_col=RESPONSE_COL,\n",
-    "    seasonality=[7, 365.25],\n",
-    "    seasonality_fs_order=[2, 5],\n",
-    "    level_knot_scale=.1,\n",
-    "    level_segments=18,\n",
-    "    seasonality_segments=0,\n",
-    "    estimator='stan-map',\n",
-    ")\n",
-    "\n",
-    "ktrlite2 = KTRLite(\n",
-    "    date_col=DATE_COL,\n",
-    "    response_col=RESPONSE_COL,\n",
-    "    seasonality=[7, 365.25],\n",
-    "    seasonality_fs_order=[2, 5],\n",
-    "    level_knot_scale=.1,\n",
-    "    level_segments=19,\n",
-    "    seasonality_segments=0,\n",
-    "    estimator='stan-map',\n",
-    ")\n",
-    "\n",
-    "ktrlite1.fit(df);\n",
-    "ktrlite2.fit(df);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:46.418436Z",
-     "start_time": "2022-01-26T02:08:46.385020Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.allclose(\n",
-    "    ktrlite1.get_point_posteriors()['map']['obs_scale'],\n",
-    "    ktrlite2.get_point_posteriors()['map']['obs_scale'],\n",
-    "    rtol=1e-2,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## KTR and knots utilities demo and tests"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:54.695170Z",
-     "start_time": "2022-01-26T02:08:54.661628Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from orbit.utils.knots import get_knot_idx, get_knot_dates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:55.056329Z",
-     "start_time": "2022-01-26T02:08:55.023153Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2923\n",
-      "[   0  292  584  877 1169 1461 1753 2045 2338 2630 2922]\n",
-      "DatetimeIndex(['2000-01-01', '2000-10-19', '2001-08-07', '2002-05-27',\n",
-      "               '2003-03-15', '2004-01-01', '2004-10-19', '2005-08-07',\n",
-      "               '2006-05-27', '2007-03-15', '2008-01-01'],\n",
-      "              dtype='datetime64[ns]', freq=None)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# case1 use segements\n",
-    "date_array = train_df[DATE_COL]\n",
-    "print(train_df.shape[0])\n",
-    "knot_idx = get_knot_idx(\n",
-    "    num_of_obs=train_df.shape[0],\n",
-    "    num_of_segments=10,\n",
-    ")\n",
-    "print(knot_idx)\n",
-    "knot_dates = get_knot_dates(date_array[0], knot_idx, date_array[1] - date_array[0])\n",
-    "print(knot_dates)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:08:59.494178Z",
-     "start_time": "2022-01-26T02:08:59.459246Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[   0 2922]\n",
-      "DatetimeIndex(['2000-01-01', '2008-01-01'], dtype='datetime64[ns]', freq=None)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# case2 use segements = 1\n",
-    "knot_idx = get_knot_idx(\n",
-    "    num_of_obs=train_df.shape[0],\n",
-    "    num_of_segments=1,\n",
-    ")\n",
-    "print(knot_idx)\n",
-    "knot_dates = get_knot_dates(date_array[0], knot_idx, date_array[1] - date_array[0])\n",
-    "print(knot_dates)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:09:01.260520Z",
-     "start_time": "2022-01-26T02:09:01.225458Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0]\n",
-      "DatetimeIndex(['2000-01-01'], dtype='datetime64[ns]', freq=None)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# case3 use segements = 0\n",
-    "knot_idx = get_knot_idx(\n",
-    "    num_of_obs=train_df.shape[0],\n",
-    "    num_of_segments=0,\n",
-    ")\n",
-    "print(knot_idx)\n",
-    "knot_dates = get_knot_dates(date_array[0], knot_idx, date_array[1] - date_array[0])\n",
-    "print(knot_dates)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:09:03.710676Z",
-     "start_time": "2022-01-26T02:09:03.673397Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[   0   22  122  222  322  422  522  622  722  822  922 1022 1122 1222\n",
-      " 1322 1422 1522 1622 1722 1822 1922 2022 2122 2222 2322 2422 2522 2622\n",
-      " 2722 2822 2922]\n",
-      "DatetimeIndex(['2000-01-01', '2000-01-23', '2000-05-02', '2000-08-10',\n",
-      "               '2000-11-18', '2001-02-26', '2001-06-06', '2001-09-14',\n",
-      "               '2001-12-23', '2002-04-02', '2002-07-11', '2002-10-19',\n",
-      "               '2003-01-27', '2003-05-07', '2003-08-15', '2003-11-23',\n",
-      "               '2004-03-02', '2004-06-10', '2004-09-18', '2004-12-27',\n",
-      "               '2005-04-06', '2005-07-15', '2005-10-23', '2006-01-31',\n",
-      "               '2006-05-11', '2006-08-19', '2006-11-27', '2007-03-07',\n",
-      "               '2007-06-15', '2007-09-23', '2008-01-01'],\n",
-      "              dtype='datetime64[ns]', freq=None)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# case4 use knot_distance\n",
-    "knot_idx = get_knot_idx(\n",
-    "    num_of_obs=train_df.shape[0],\n",
-    "    knot_distance=100,\n",
-    ")\n",
-    "print(knot_idx)\n",
-    "knot_dates = get_knot_dates(date_array[0], knot_idx, date_array[1] - date_array[0])\n",
-    "print(knot_dates)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-01-26T02:09:05.901570Z",
-     "start_time": "2022-01-26T02:09:05.863277Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Int64Index([1096, 1916], dtype='int64')\n",
-      "DatetimeIndex(['2003-01-01', '2005-03-31'], dtype='datetime64[ns]', freq=None)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# case5 use knot_dates\n",
-    "knot_idx = get_knot_idx(\n",
-    "    date_array=train_df[DATE_COL],\n",
-    "    knot_dates=pd.to_datetime(['2003-01-01', '2005-03-31'])\n",
-    ")\n",
-    "print(knot_idx)\n",
-    "knot_dates = get_knot_dates(date_array[0], knot_idx, date_array[1] - date_array[0])\n",
-    "print(knot_dates)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "orbit39-cmdstan",
-   "language": "python",
-   "name": "orbit39-cmdstan"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/lgt.ipynb b/examples/lgt.ipynb
deleted file mode 100644
index e14eaa5a..00000000
--- a/examples/lgt.ipynb
+++ /dev/null
@@ -1,1888 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# LGT Quick Start"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "LGT stands for Local and Global Trend, which is an important model type in orbit package. In the model equation, there is a local trend term and a global trend term.\n",
-    "\n",
-    "In this notebook we will show how to use Orbit LGT models with the US unemployment claims data.\n",
-    "\n",
-    "**Note: Negative response values are not allowed in LGT model, due to the existence of the global trend term.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:22.048121Z",
-     "start_time": "2021-08-14T22:30:22.028361Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:24.435003Z",
-     "start_time": "2021-08-14T22:30:22.049926Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from orbit.models import LGT\n",
-    "from orbit.diagnostics.plot import plot_predicted_data, plot_predicted_components\n",
-    "from orbit.utils.dataset import load_iclaims\n",
-    "\n",
-    "from orbit.utils.plot import get_orbit_style\n",
-    "plt.style.use(get_orbit_style())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*iclaims_example* is a dataset containing the weekly initial claims for US unemployment benefits against a few related google trend queries (unemploy, filling and job) from Jan 2010 - June 2018. \n",
-    "This aims to mimick the dataset from the paper [Predicting the Present with Bayesian Structural Time Series](https://people.ischool.berkeley.edu/~hal/Papers/2013/pred-present-with-bsts.pdf) by SCOTT and VARIAN (2014).\n",
-    "\n",
-    "Number of claims are obtained from [Federal Reserve Bank of St. Louis](https://fred.stlouisfed.org/series/ICNSA) while google queries are obtained through [Google Trends API](https://trends.google.com/trends/?geo=US).\n",
-    "Note that dataset is transformed by natural log before fitting in order to be fitted as a multiplicative model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:24.892583Z",
-     "start_time": "2021-08-14T22:30:24.437875Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "week              datetime64[ns]\n",
-       "claims                   float64\n",
-       "trend.unemploy           float64\n",
-       "trend.filling            float64\n",
-       "trend.job                float64\n",
-       "sp500                    float64\n",
-       "vix                      float64\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# load data\n",
-    "df = load_iclaims()\n",
-    "\n",
-    "# define date and response column\n",
-    "DATE_COL = 'week'\n",
-    "RESPONSE_COL = 'claims'\n",
-    "\n",
-    "df.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:24.956939Z",
-     "start_time": "2021-08-14T22:30:24.896502Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "      <th>sp500</th>\n",
-       "      <th>vix</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.318452</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.417633</td>\n",
-       "      <td>0.122654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>13.624218</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.168794</td>\n",
-       "      <td>-0.425480</td>\n",
-       "      <td>0.110445</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>13.398741</td>\n",
-       "      <td>0.236143</td>\n",
-       "      <td>-0.292477</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.465229</td>\n",
-       "      <td>0.532339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>13.137549</td>\n",
-       "      <td>0.203353</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.106918</td>\n",
-       "      <td>-0.481751</td>\n",
-       "      <td>0.428645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>13.196760</td>\n",
-       "      <td>0.134360</td>\n",
-       "      <td>-0.242466</td>\n",
-       "      <td>0.074483</td>\n",
-       "      <td>-0.488929</td>\n",
-       "      <td>0.487404</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week     claims  trend.unemploy  trend.filling  trend.job     sp500  \\\n",
-       "0 2010-01-03  13.386595        0.219882      -0.318452   0.117500 -0.417633   \n",
-       "1 2010-01-10  13.624218        0.219882      -0.194838   0.168794 -0.425480   \n",
-       "2 2010-01-17  13.398741        0.236143      -0.292477   0.117500 -0.465229   \n",
-       "3 2010-01-24  13.137549        0.203353      -0.194838   0.106918 -0.481751   \n",
-       "4 2010-01-31  13.196760        0.134360      -0.242466   0.074483 -0.488929   \n",
-       "\n",
-       "        vix  \n",
-       "0  0.122654  \n",
-       "1  0.110445  \n",
-       "2  0.532339  \n",
-       "3  0.428645  \n",
-       "4  0.487404  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Train / Test Split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:25.004750Z",
-     "start_time": "2021-08-14T22:30:24.959917Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "test_size = 52\n",
-    "\n",
-    "train_df = df[:-test_size]\n",
-    "test_df = df[-test_size:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:25.051746Z",
-     "start_time": "2021-08-14T22:30:25.007107Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "      <th>sp500</th>\n",
-       "      <th>vix</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.318452</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.417633</td>\n",
-       "      <td>0.122654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>13.624218</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.168794</td>\n",
-       "      <td>-0.425480</td>\n",
-       "      <td>0.110445</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>13.398741</td>\n",
-       "      <td>0.236143</td>\n",
-       "      <td>-0.292477</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.465229</td>\n",
-       "      <td>0.532339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>13.137549</td>\n",
-       "      <td>0.203353</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.106918</td>\n",
-       "      <td>-0.481751</td>\n",
-       "      <td>0.428645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>13.196760</td>\n",
-       "      <td>0.134360</td>\n",
-       "      <td>-0.242466</td>\n",
-       "      <td>0.074483</td>\n",
-       "      <td>-0.488929</td>\n",
-       "      <td>0.487404</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week     claims  trend.unemploy  trend.filling  trend.job     sp500  \\\n",
-       "0 2010-01-03  13.386595        0.219882      -0.318452   0.117500 -0.417633   \n",
-       "1 2010-01-10  13.624218        0.219882      -0.194838   0.168794 -0.425480   \n",
-       "2 2010-01-17  13.398741        0.236143      -0.292477   0.117500 -0.465229   \n",
-       "3 2010-01-24  13.137549        0.203353      -0.194838   0.106918 -0.481751   \n",
-       "4 2010-01-31  13.196760        0.134360      -0.242466   0.074483 -0.488929   \n",
-       "\n",
-       "        vix  \n",
-       "0  0.122654  \n",
-       "1  0.110445  \n",
-       "2  0.532339  \n",
-       "3  0.428645  \n",
-       "4  0.487404  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## LGT Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In orbit, we have three types of LGT models, `LGTMAP`, `LGTAggregated` and  `LGTFull`.\n",
-    "Orbit follows the sklearn model API. We can create an instance of the Orbit class and then call its fit and predict methods."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### LGT-MAP"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "LGT model for MAP (Maximum a Posteriori) prediction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:25.087141Z",
-     "start_time": "2021-08-14T22:30:25.053810Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "lgt = LGT(response_col=RESPONSE_COL,\n",
-    "          date_col=DATE_COL,\n",
-    "          regressor_col=['sp500'],\n",
-    "          seasonality=52,\n",
-    "          estimator='stan-map',\n",
-    "          seed=8888)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:25.436164Z",
-     "start_time": "2021-08-14T22:30:25.090790Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 122 ms, sys: 8.66 ms, total: 131 ms\n",
-      "Wall time: 311 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "lgt.fit(df=train_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:25.478598Z",
-     "start_time": "2021-08-14T22:30:25.438743Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>regressor</th>\n",
-       "      <th>regressor_sign</th>\n",
-       "      <th>coefficient</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>sp500</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>-0.12971</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  regressor regressor_sign  coefficient\n",
-       "0     sp500        Regular     -0.12971"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lgt.get_regression_coefs()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:25.556377Z",
-     "start_time": "2021-08-14T22:30:25.480620Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>prediction</th>\n",
-       "      <th>trend</th>\n",
-       "      <th>seasonality</th>\n",
-       "      <th>regression</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2017-07-02</td>\n",
-       "      <td>12.418269</td>\n",
-       "      <td>12.469615</td>\n",
-       "      <td>-0.008168</td>\n",
-       "      <td>-0.043179</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2017-07-09</td>\n",
-       "      <td>12.536054</td>\n",
-       "      <td>12.466527</td>\n",
-       "      <td>0.114516</td>\n",
-       "      <td>-0.044989</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2017-07-16</td>\n",
-       "      <td>12.376226</td>\n",
-       "      <td>12.464701</td>\n",
-       "      <td>-0.042788</td>\n",
-       "      <td>-0.045687</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2017-07-23</td>\n",
-       "      <td>12.230214</td>\n",
-       "      <td>12.463012</td>\n",
-       "      <td>-0.187133</td>\n",
-       "      <td>-0.045664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2017-07-30</td>\n",
-       "      <td>12.181999</td>\n",
-       "      <td>12.461337</td>\n",
-       "      <td>-0.233425</td>\n",
-       "      <td>-0.045912</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week  prediction      trend  seasonality  regression\n",
-       "0 2017-07-02   12.418269  12.469615    -0.008168   -0.043179\n",
-       "1 2017-07-09   12.536054  12.466527     0.114516   -0.044989\n",
-       "2 2017-07-16   12.376226  12.464701    -0.042788   -0.045687\n",
-       "3 2017-07-23   12.230214  12.463012    -0.187133   -0.045664\n",
-       "4 2017-07-30   12.181999  12.461337    -0.233425   -0.045912"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predicted_df = lgt.predict(df=test_df, decompose=True)\n",
-    "predicted_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:25.982294Z",
-     "start_time": "2021-08-14T22:30:25.559000Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_predicted_data(training_actual_df=train_df,\n",
-    "                        predicted_df=predicted_df, \n",
-    "                        date_col=DATE_COL,\n",
-    "                        actual_col=RESPONSE_COL,  \n",
-    "                        test_actual_df=test_df,\n",
-    "                        title='Prediction with LGTMAP Model')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### LGT-MCMC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "LGT model for full prediction. In full prediction, the prediction occurs as a function of each parameter posterior sample, and the prediction results are aggregated after prediction. Prediction will always return the median (aka 50th percentile) along with any additional percentiles that are specified."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:26.022451Z",
-     "start_time": "2021-08-14T22:30:25.984217Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "lgt = LGT(response_col=RESPONSE_COL,\n",
-    "          date_col=DATE_COL,\n",
-    "          estimator='stan-mcmc',\n",
-    "          regressor_col=['sp500'],\n",
-    "          seasonality=52,\n",
-    "          seed=8888)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:33.334912Z",
-     "start_time": "2021-08-14T22:30:26.024607Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:1 of 100 iterations ended with a divergence (1 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.8 to remove the divergences.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 74.3 ms, sys: 60.2 ms, total: 134 ms\n",
-      "Wall time: 7.27 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "lgt.fit(df=train_df, point_method=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:33.395867Z",
-     "start_time": "2021-08-14T22:30:33.338277Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>regressor</th>\n",
-       "      <th>regressor_sign</th>\n",
-       "      <th>coefficient</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>sp500</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>-0.105232</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  regressor regressor_sign  coefficient\n",
-       "0     sp500        Regular    -0.105232"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lgt.get_regression_coefs()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:33.500530Z",
-     "start_time": "2021-08-14T22:30:33.398316Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>prediction_5</th>\n",
-       "      <th>prediction</th>\n",
-       "      <th>prediction_95</th>\n",
-       "      <th>trend_5</th>\n",
-       "      <th>trend</th>\n",
-       "      <th>trend_95</th>\n",
-       "      <th>seasonality_5</th>\n",
-       "      <th>seasonality</th>\n",
-       "      <th>seasonality_95</th>\n",
-       "      <th>regression_5</th>\n",
-       "      <th>regression</th>\n",
-       "      <th>regression_95</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>2018-05-27</td>\n",
-       "      <td>12.121450</td>\n",
-       "      <td>12.222408</td>\n",
-       "      <td>12.336262</td>\n",
-       "      <td>12.265188</td>\n",
-       "      <td>12.421705</td>\n",
-       "      <td>12.585555</td>\n",
-       "      <td>-0.267771</td>\n",
-       "      <td>-0.118234</td>\n",
-       "      <td>-0.091846</td>\n",
-       "      <td>-0.102139</td>\n",
-       "      <td>-0.047667</td>\n",
-       "      <td>-0.006838</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>2018-06-03</td>\n",
-       "      <td>12.035511</td>\n",
-       "      <td>12.164595</td>\n",
-       "      <td>12.277020</td>\n",
-       "      <td>12.287317</td>\n",
-       "      <td>12.419028</td>\n",
-       "      <td>12.548928</td>\n",
-       "      <td>-0.322052</td>\n",
-       "      <td>-0.184279</td>\n",
-       "      <td>-0.148801</td>\n",
-       "      <td>-0.105771</td>\n",
-       "      <td>-0.049362</td>\n",
-       "      <td>-0.007081</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>2018-06-10</td>\n",
-       "      <td>12.157590</td>\n",
-       "      <td>12.269862</td>\n",
-       "      <td>12.398718</td>\n",
-       "      <td>12.276035</td>\n",
-       "      <td>12.420867</td>\n",
-       "      <td>12.559491</td>\n",
-       "      <td>-0.229390</td>\n",
-       "      <td>-0.087272</td>\n",
-       "      <td>-0.052992</td>\n",
-       "      <td>-0.105822</td>\n",
-       "      <td>-0.049386</td>\n",
-       "      <td>-0.007084</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>2018-06-17</td>\n",
-       "      <td>12.107162</td>\n",
-       "      <td>12.253584</td>\n",
-       "      <td>12.372341</td>\n",
-       "      <td>12.276023</td>\n",
-       "      <td>12.433750</td>\n",
-       "      <td>12.578969</td>\n",
-       "      <td>-0.259427</td>\n",
-       "      <td>-0.118373</td>\n",
-       "      <td>-0.082977</td>\n",
-       "      <td>-0.103803</td>\n",
-       "      <td>-0.048444</td>\n",
-       "      <td>-0.006949</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>2018-06-24</td>\n",
-       "      <td>12.139809</td>\n",
-       "      <td>12.277902</td>\n",
-       "      <td>12.380484</td>\n",
-       "      <td>12.268771</td>\n",
-       "      <td>12.408450</td>\n",
-       "      <td>12.564300</td>\n",
-       "      <td>-0.210829</td>\n",
-       "      <td>-0.070608</td>\n",
-       "      <td>-0.034148</td>\n",
-       "      <td>-0.100795</td>\n",
-       "      <td>-0.047040</td>\n",
-       "      <td>-0.006748</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         week  prediction_5  prediction  prediction_95    trend_5      trend  \\\n",
-       "47 2018-05-27     12.121450   12.222408      12.336262  12.265188  12.421705   \n",
-       "48 2018-06-03     12.035511   12.164595      12.277020  12.287317  12.419028   \n",
-       "49 2018-06-10     12.157590   12.269862      12.398718  12.276035  12.420867   \n",
-       "50 2018-06-17     12.107162   12.253584      12.372341  12.276023  12.433750   \n",
-       "51 2018-06-24     12.139809   12.277902      12.380484  12.268771  12.408450   \n",
-       "\n",
-       "     trend_95  seasonality_5  seasonality  seasonality_95  regression_5  \\\n",
-       "47  12.585555      -0.267771    -0.118234       -0.091846     -0.102139   \n",
-       "48  12.548928      -0.322052    -0.184279       -0.148801     -0.105771   \n",
-       "49  12.559491      -0.229390    -0.087272       -0.052992     -0.105822   \n",
-       "50  12.578969      -0.259427    -0.118373       -0.082977     -0.103803   \n",
-       "51  12.564300      -0.210829    -0.070608       -0.034148     -0.100795   \n",
-       "\n",
-       "    regression  regression_95  \n",
-       "47   -0.047667      -0.006838  \n",
-       "48   -0.049362      -0.007081  \n",
-       "49   -0.049386      -0.007084  \n",
-       "50   -0.048444      -0.006949  \n",
-       "51   -0.047040      -0.006748  "
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predicted_df = lgt.predict(df=test_df, decompose=True)\n",
-    "predicted_df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:33.846496Z",
-     "start_time": "2021-08-14T22:30:33.502243Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_predicted_data(training_actual_df=train_df,\n",
-    "                        predicted_df=predicted_df,\n",
-    "                        date_col=DATE_COL,\n",
-    "                        actual_col=RESPONSE_COL,\n",
-    "                        test_actual_df=test_df,\n",
-    "                        title='Prediction with LGTFull Model')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:41.850509Z",
-     "start_time": "2021-08-14T22:30:33.848052Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:1 of 100 iterations ended with a divergence (1 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.8 to remove the divergences.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>prediction</th>\n",
-       "      <th>trend</th>\n",
-       "      <th>seasonality</th>\n",
-       "      <th>regression</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>2018-05-27</td>\n",
-       "      <td>12.213349</td>\n",
-       "      <td>12.400611</td>\n",
-       "      <td>-0.142465</td>\n",
-       "      <td>-0.044797</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>2018-06-03</td>\n",
-       "      <td>12.147506</td>\n",
-       "      <td>12.398705</td>\n",
-       "      <td>-0.204809</td>\n",
-       "      <td>-0.046390</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>2018-06-10</td>\n",
-       "      <td>12.241098</td>\n",
-       "      <td>12.396799</td>\n",
-       "      <td>-0.109288</td>\n",
-       "      <td>-0.046413</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>2018-06-17</td>\n",
-       "      <td>12.209391</td>\n",
-       "      <td>12.394893</td>\n",
-       "      <td>-0.139975</td>\n",
-       "      <td>-0.045527</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>2018-06-24</td>\n",
-       "      <td>12.257019</td>\n",
-       "      <td>12.392987</td>\n",
-       "      <td>-0.091761</td>\n",
-       "      <td>-0.044208</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         week  prediction      trend  seasonality  regression\n",
-       "47 2018-05-27   12.213349  12.400611    -0.142465   -0.044797\n",
-       "48 2018-06-03   12.147506  12.398705    -0.204809   -0.046390\n",
-       "49 2018-06-10   12.241098  12.396799    -0.109288   -0.046413\n",
-       "50 2018-06-17   12.209391  12.394893    -0.139975   -0.045527\n",
-       "51 2018-06-24   12.257019  12.392987    -0.091761   -0.044208"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lgt.fit(df=train_df, point_method='mean')\n",
-    "predicted_df = lgt.predict(df=test_df, decompose=True)\n",
-    "predicted_df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:41.900678Z",
-     "start_time": "2021-08-14T22:30:41.852899Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>regressor</th>\n",
-       "      <th>regressor_sign</th>\n",
-       "      <th>coefficient</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>sp500</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>-0.098896</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  regressor regressor_sign  coefficient\n",
-       "0     sp500        Regular    -0.098896"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lgt.get_regression_coefs()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:49.197998Z",
-     "start_time": "2021-08-14T22:30:41.903285Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>prediction</th>\n",
-       "      <th>trend</th>\n",
-       "      <th>seasonality</th>\n",
-       "      <th>regression</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>2018-05-27</td>\n",
-       "      <td>12.232460</td>\n",
-       "      <td>12.398649</td>\n",
-       "      <td>-0.119574</td>\n",
-       "      <td>-0.046614</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>2018-06-03</td>\n",
-       "      <td>12.170546</td>\n",
-       "      <td>12.396948</td>\n",
-       "      <td>-0.178130</td>\n",
-       "      <td>-0.048272</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>2018-06-10</td>\n",
-       "      <td>12.261167</td>\n",
-       "      <td>12.395247</td>\n",
-       "      <td>-0.085784</td>\n",
-       "      <td>-0.048296</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>2018-06-17</td>\n",
-       "      <td>12.229913</td>\n",
-       "      <td>12.393546</td>\n",
-       "      <td>-0.116259</td>\n",
-       "      <td>-0.047374</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>2018-06-24</td>\n",
-       "      <td>12.277632</td>\n",
-       "      <td>12.391845</td>\n",
-       "      <td>-0.068211</td>\n",
-       "      <td>-0.046001</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         week  prediction      trend  seasonality  regression\n",
-       "47 2018-05-27   12.232460  12.398649    -0.119574   -0.046614\n",
-       "48 2018-06-03   12.170546  12.396948    -0.178130   -0.048272\n",
-       "49 2018-06-10   12.261167  12.395247    -0.085784   -0.048296\n",
-       "50 2018-06-17   12.229913  12.393546    -0.116259   -0.047374\n",
-       "51 2018-06-24   12.277632  12.391845    -0.068211   -0.046001"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lgt.fit(df=train_df, point_method='median')\n",
-    "predicted_df = lgt.predict(df=test_df, decompose=True)\n",
-    "predicted_df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:49.246426Z",
-     "start_time": "2021-08-14T22:30:49.200243Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
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-       "\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>regressor</th>\n",
-       "      <th>regressor_sign</th>\n",
-       "      <th>coefficient</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>sp500</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>-0.102908</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  regressor regressor_sign  coefficient\n",
-       "0     sp500        Regular    -0.102908"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lgt.get_regression_coefs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### LGT-SVI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:30:49.281899Z",
-     "start_time": "2021-08-14T22:30:49.248454Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "lgt = LGT(response_col=RESPONSE_COL,\n",
-    "          date_col=DATE_COL,\n",
-    "          estimator='pyro-svi',\n",
-    "          regressor_col=['sp500'],\n",
-    "          seasonality=52,\n",
-    "          seed=8888,\n",
-    "          num_steps=101)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:31:08.077798Z",
-     "start_time": "2021-08-14T22:30:49.284111Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:root:Guessed max_plate_nesting = 2\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 18.8 s, sys: 1.23 s, total: 20 s\n",
-      "Wall time: 18.8 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "lgt.fit(df=train_df, point_method=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:31:08.119495Z",
-     "start_time": "2021-08-14T22:31:08.083035Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>regressor</th>\n",
-       "      <th>regressor_sign</th>\n",
-       "      <th>coefficient</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>sp500</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>-0.079233</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  regressor regressor_sign  coefficient\n",
-       "0     sp500        Regular    -0.079233"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lgt.get_regression_coefs()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:31:08.208427Z",
-     "start_time": "2021-08-14T22:31:08.122781Z"
-    }
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-   "outputs": [
-    {
-     "data": {
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>prediction_5</th>\n",
-       "      <th>prediction</th>\n",
-       "      <th>prediction_95</th>\n",
-       "      <th>trend_5</th>\n",
-       "      <th>trend</th>\n",
-       "      <th>trend_95</th>\n",
-       "      <th>seasonality_5</th>\n",
-       "      <th>seasonality</th>\n",
-       "      <th>seasonality_95</th>\n",
-       "      <th>regression_5</th>\n",
-       "      <th>regression</th>\n",
-       "      <th>regression_95</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>2018-05-27</td>\n",
-       "      <td>12.134188</td>\n",
-       "      <td>12.239194</td>\n",
-       "      <td>12.345663</td>\n",
-       "      <td>12.293881</td>\n",
-       "      <td>12.433754</td>\n",
-       "      <td>12.555211</td>\n",
-       "      <td>-0.226589</td>\n",
-       "      <td>-0.153846</td>\n",
-       "      <td>-0.110348</td>\n",
-       "      <td>-0.080082</td>\n",
-       "      <td>-0.035890</td>\n",
-       "      <td>0.017217</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>2018-06-03</td>\n",
-       "      <td>12.067673</td>\n",
-       "      <td>12.174843</td>\n",
-       "      <td>12.315627</td>\n",
-       "      <td>12.303840</td>\n",
-       "      <td>12.436522</td>\n",
-       "      <td>12.590489</td>\n",
-       "      <td>-0.289718</td>\n",
-       "      <td>-0.216006</td>\n",
-       "      <td>-0.170248</td>\n",
-       "      <td>-0.082930</td>\n",
-       "      <td>-0.037167</td>\n",
-       "      <td>0.017829</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>2018-06-10</td>\n",
-       "      <td>12.151177</td>\n",
-       "      <td>12.274897</td>\n",
-       "      <td>12.367586</td>\n",
-       "      <td>12.302466</td>\n",
-       "      <td>12.445126</td>\n",
-       "      <td>12.549077</td>\n",
-       "      <td>-0.193503</td>\n",
-       "      <td>-0.119538</td>\n",
-       "      <td>-0.078269</td>\n",
-       "      <td>-0.082970</td>\n",
-       "      <td>-0.037185</td>\n",
-       "      <td>0.017838</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>2018-06-17</td>\n",
-       "      <td>12.126432</td>\n",
-       "      <td>12.247311</td>\n",
-       "      <td>12.345989</td>\n",
-       "      <td>12.310941</td>\n",
-       "      <td>12.447040</td>\n",
-       "      <td>12.554084</td>\n",
-       "      <td>-0.223979</td>\n",
-       "      <td>-0.150387</td>\n",
-       "      <td>-0.109883</td>\n",
-       "      <td>-0.081387</td>\n",
-       "      <td>-0.036475</td>\n",
-       "      <td>0.017497</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>2018-06-24</td>\n",
-       "      <td>12.156756</td>\n",
-       "      <td>12.296717</td>\n",
-       "      <td>12.396728</td>\n",
-       "      <td>12.298664</td>\n",
-       "      <td>12.426746</td>\n",
-       "      <td>12.567291</td>\n",
-       "      <td>-0.175204</td>\n",
-       "      <td>-0.102026</td>\n",
-       "      <td>-0.061579</td>\n",
-       "      <td>-0.079028</td>\n",
-       "      <td>-0.035418</td>\n",
-       "      <td>0.016990</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         week  prediction_5  prediction  prediction_95    trend_5      trend  \\\n",
-       "47 2018-05-27     12.134188   12.239194      12.345663  12.293881  12.433754   \n",
-       "48 2018-06-03     12.067673   12.174843      12.315627  12.303840  12.436522   \n",
-       "49 2018-06-10     12.151177   12.274897      12.367586  12.302466  12.445126   \n",
-       "50 2018-06-17     12.126432   12.247311      12.345989  12.310941  12.447040   \n",
-       "51 2018-06-24     12.156756   12.296717      12.396728  12.298664  12.426746   \n",
-       "\n",
-       "     trend_95  seasonality_5  seasonality  seasonality_95  regression_5  \\\n",
-       "47  12.555211      -0.226589    -0.153846       -0.110348     -0.080082   \n",
-       "48  12.590489      -0.289718    -0.216006       -0.170248     -0.082930   \n",
-       "49  12.549077      -0.193503    -0.119538       -0.078269     -0.082970   \n",
-       "50  12.554084      -0.223979    -0.150387       -0.109883     -0.081387   \n",
-       "51  12.567291      -0.175204    -0.102026       -0.061579     -0.079028   \n",
-       "\n",
-       "    regression  regression_95  \n",
-       "47   -0.035890       0.017217  \n",
-       "48   -0.037167       0.017829  \n",
-       "49   -0.037185       0.017838  \n",
-       "50   -0.036475       0.017497  \n",
-       "51   -0.035418       0.016990  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predicted_df = lgt.predict(df=test_df, point_method=None, decompose=True)\n",
-    "predicted_df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:31:08.554651Z",
-     "start_time": "2021-08-14T22:31:08.210752Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_predicted_data(training_actual_df=train_df,\n",
-    "                        predicted_df=predicted_df,\n",
-    "                        date_col=DATE_COL,\n",
-    "                        actual_col=RESPONSE_COL,\n",
-    "                        test_actual_df=test_df,\n",
-    "                        title='Prediction with LGTFull Model')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:31:27.351013Z",
-     "start_time": "2021-08-14T22:31:08.556464Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:root:Guessed max_plate_nesting = 2\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>prediction</th>\n",
-       "      <th>trend</th>\n",
-       "      <th>seasonality</th>\n",
-       "      <th>regression</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>2018-05-27</td>\n",
-       "      <td>12.238239</td>\n",
-       "      <td>12.431259</td>\n",
-       "      <td>-0.160223</td>\n",
-       "      <td>-0.032796</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>2018-06-03</td>\n",
-       "      <td>12.175089</td>\n",
-       "      <td>12.429819</td>\n",
-       "      <td>-0.220767</td>\n",
-       "      <td>-0.033963</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>2018-06-10</td>\n",
-       "      <td>12.268645</td>\n",
-       "      <td>12.428380</td>\n",
-       "      <td>-0.125755</td>\n",
-       "      <td>-0.033979</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>2018-06-17</td>\n",
-       "      <td>12.237186</td>\n",
-       "      <td>12.426940</td>\n",
-       "      <td>-0.156423</td>\n",
-       "      <td>-0.033331</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>2018-06-24</td>\n",
-       "      <td>12.285014</td>\n",
-       "      <td>12.425500</td>\n",
-       "      <td>-0.108122</td>\n",
-       "      <td>-0.032365</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         week  prediction      trend  seasonality  regression\n",
-       "47 2018-05-27   12.238239  12.431259    -0.160223   -0.032796\n",
-       "48 2018-06-03   12.175089  12.429819    -0.220767   -0.033963\n",
-       "49 2018-06-10   12.268645  12.428380    -0.125755   -0.033979\n",
-       "50 2018-06-17   12.237186  12.426940    -0.156423   -0.033331\n",
-       "51 2018-06-24   12.285014  12.425500    -0.108122   -0.032365"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lgt.fit(df=train_df, point_method='mean')\n",
-    "predicted_df = lgt.predict(df=test_df, decompose=True)\n",
-    "predicted_df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-08-14T22:31:46.496066Z",
-     "start_time": "2021-08-14T22:31:27.352760Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:root:Guessed max_plate_nesting = 2\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>prediction</th>\n",
-       "      <th>trend</th>\n",
-       "      <th>seasonality</th>\n",
-       "      <th>regression</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>2018-05-27</td>\n",
-       "      <td>12.242491</td>\n",
-       "      <td>12.432228</td>\n",
-       "      <td>-0.153846</td>\n",
-       "      <td>-0.035890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>2018-06-03</td>\n",
-       "      <td>12.177768</td>\n",
-       "      <td>12.430941</td>\n",
-       "      <td>-0.216006</td>\n",
-       "      <td>-0.037167</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>2018-06-10</td>\n",
-       "      <td>12.272932</td>\n",
-       "      <td>12.429655</td>\n",
-       "      <td>-0.119538</td>\n",
-       "      <td>-0.037185</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>2018-06-17</td>\n",
-       "      <td>12.241506</td>\n",
-       "      <td>12.428368</td>\n",
-       "      <td>-0.150387</td>\n",
-       "      <td>-0.036475</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>2018-06-24</td>\n",
-       "      <td>12.289638</td>\n",
-       "      <td>12.427082</td>\n",
-       "      <td>-0.102026</td>\n",
-       "      <td>-0.035418</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         week  prediction      trend  seasonality  regression\n",
-       "47 2018-05-27   12.242491  12.432228    -0.153846   -0.035890\n",
-       "48 2018-06-03   12.177768  12.430941    -0.216006   -0.037167\n",
-       "49 2018-06-10   12.272932  12.429655    -0.119538   -0.037185\n",
-       "50 2018-06-17   12.241506  12.428368    -0.150387   -0.036475\n",
-       "51 2018-06-24   12.289638  12.427082    -0.102026   -0.035418"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lgt.fit(df=train_df, point_method='median')\n",
-    "predicted_df = lgt.predict(df=test_df, decompose=True)\n",
-    "predicted_df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.8"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/orbit_m3_backtest.ipynb b/examples/orbit_m3_backtest.ipynb
deleted file mode 100644
index a5318c37..00000000
--- a/examples/orbit_m3_backtest.ipynb
+++ /dev/null
@@ -1,693 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "994f63e1",
-   "metadata": {},
-   "source": [
-    "# A Demo on Backtesting M3 with Various Models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be2cd336",
-   "metadata": {},
-   "source": [
-    "This notebook aims to\n",
-    "1. provide a simple demo how to backtest models with orbit provided functions.  \n",
-    "2. add transperancy how our accuracy metrics are derived in https://arxiv.org/abs/2004.08492.\n",
-    "\n",
-    "Due to versioning and random seed, there could be subtle difference for the final numbers. This notebook should also be available in colab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "2166de6a",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:37:42.983007Z",
-     "start_time": "2021-07-13T22:37:08.360143Z"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "JkM4yXCFaee8",
-    "outputId": "31a50da2-eb80-4769-a421-fe670956ae85"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: https://yoober11:****@pypi.uberinternal.com/index\n",
-      "Requirement already satisfied: orbit-ml==1.0.13 in /Users/zhishiw/Desktop/uTS-py/orbit (1.0.13)\n",
-      "Requirement already satisfied: numpy>=1.18.2 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from orbit-ml==1.0.13) (1.20.1)\n",
-      "Requirement already satisfied: pandas>=1.0.3 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from orbit-ml==1.0.13) (1.2.2)\n",
-      "Requirement already satisfied: pystan==2.19.1.1 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from orbit-ml==1.0.13) (2.19.1.1)\n",
-      "Collecting matplotlib==3.3.4\n",
-      "  Downloading https://pypi.uberinternal.com/packages/packages/7e/32/46285e083ce5b4a46468236e3073c794324700e62d7fbf26894ec390d99a/matplotlib-3.3.4-cp37-cp37m-macosx_10_9_x86_64.whl (8.5 MB)\n",
-      "\u001b[K     |████████████████████████████████| 8.5 MB 1.6 MB/s eta 0:00:011\n",
-      "\u001b[?25hRequirement already satisfied: scipy>=1.4.1 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from orbit-ml==1.0.13) (1.6.1)\n",
-      "Requirement already satisfied: torch in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from orbit-ml==1.0.13) (1.7.1)\n",
-      "Requirement already satisfied: tqdm in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from orbit-ml==1.0.13) (4.56.2)\n",
-      "Requirement already satisfied: seaborn>=0.10.0 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from orbit-ml==1.0.13) (0.11.1)\n",
-      "Requirement already satisfied: pyro-ppl>=1.4.0 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from orbit-ml==1.0.13) (1.5.2)\n",
-      "Requirement already satisfied: statsmodels>=0.11.1 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from orbit-ml==1.0.13) (0.12.2)\n",
-      "Requirement already satisfied: custom_inherit==2.3.1 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from orbit-ml==1.0.13) (2.3.1)\n",
-      "Requirement already satisfied: cycler>=0.10 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from matplotlib==3.3.4->orbit-ml==1.0.13) (0.10.0)\n",
-      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from matplotlib==3.3.4->orbit-ml==1.0.13) (2.4.7)\n",
-      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from matplotlib==3.3.4->orbit-ml==1.0.13) (1.3.1)\n",
-      "Requirement already satisfied: pillow>=6.2.0 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from matplotlib==3.3.4->orbit-ml==1.0.13) (8.2.0)\n",
-      "Requirement already satisfied: python-dateutil>=2.1 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from matplotlib==3.3.4->orbit-ml==1.0.13) (2.8.0)\n",
-      "Requirement already satisfied: Cython!=0.25.1,>=0.22 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from pystan==2.19.1.1->orbit-ml==1.0.13) (0.29.21)\n",
-      "Requirement already satisfied: six in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from cycler>=0.10->matplotlib==3.3.4->orbit-ml==1.0.13) (1.12.0)\n",
-      "Requirement already satisfied: pytz>=2017.3 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from pandas>=1.0.3->orbit-ml==1.0.13) (2019.2)\n",
-      "Requirement already satisfied: opt-einsum>=2.3.2 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from pyro-ppl>=1.4.0->orbit-ml==1.0.13) (3.3.0)\n",
-      "Requirement already satisfied: pyro-api>=0.1.1 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from pyro-ppl>=1.4.0->orbit-ml==1.0.13) (0.1.2)\n",
-      "Requirement already satisfied: patsy>=0.5 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from statsmodels>=0.11.1->orbit-ml==1.0.13) (0.5.1)\n",
-      "Requirement already satisfied: typing-extensions in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from torch->orbit-ml==1.0.13) (3.7.4.3)\n",
-      "Installing collected packages: matplotlib\n",
-      "  Attempting uninstall: matplotlib\n",
-      "    Found existing installation: matplotlib 3.4.2\n",
-      "    Uninstalling matplotlib-3.4.2:\n",
-      "      Successfully uninstalled matplotlib-3.4.2\n",
-      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
-      "ts-benchmark 0.0.1 requires matplotlib==3.2.1, but you have matplotlib 3.3.4 which is incompatible.\u001b[0m\n",
-      "Successfully installed matplotlib-3.3.4\n",
-      "Looking in indexes: https://yoober11:****@pypi.uberinternal.com/index\n",
-      "Requirement already satisfied: fbprophet==0.7.1 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (0.7.1)\n",
-      "Requirement already satisfied: Cython>=0.22 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from fbprophet==0.7.1) (0.29.21)\n",
-      "Requirement already satisfied: cmdstanpy==0.9.5 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from fbprophet==0.7.1) (0.9.5)\n",
-      "Requirement already satisfied: pystan>=2.14 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from fbprophet==0.7.1) (2.19.1.1)\n",
-      "Requirement already satisfied: numpy>=1.15.4 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from fbprophet==0.7.1) (1.20.1)\n",
-      "Requirement already satisfied: pandas>=1.0.4 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from fbprophet==0.7.1) (1.2.2)\n",
-      "Requirement already satisfied: matplotlib>=2.0.0 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from fbprophet==0.7.1) (3.3.4)\n",
-      "Requirement already satisfied: LunarCalendar>=0.0.9 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from fbprophet==0.7.1) (0.0.9)\n",
-      "Requirement already satisfied: convertdate>=2.1.2 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from fbprophet==0.7.1) (2.3.0)\n",
-      "Requirement already satisfied: holidays>=0.10.2 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from fbprophet==0.7.1) (0.10.5.2)\n",
-      "Requirement already satisfied: setuptools-git>=1.2 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from fbprophet==0.7.1) (1.2)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.0 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from fbprophet==0.7.1) (2.8.0)\n",
-      "Requirement already satisfied: tqdm>=4.36.1 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from fbprophet==0.7.1) (4.56.2)\n",
-      "Requirement already satisfied: pytz>=2014.10 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from convertdate>=2.1.2->fbprophet==0.7.1) (2019.2)\n",
-      "Requirement already satisfied: pymeeus<=1,>=0.3.6 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from convertdate>=2.1.2->fbprophet==0.7.1) (0.3.7)\n",
-      "Requirement already satisfied: six in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from holidays>=0.10.2->fbprophet==0.7.1) (1.12.0)\n",
-      "Requirement already satisfied: korean_lunar_calendar in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from holidays>=0.10.2->fbprophet==0.7.1) (0.2.1)\n",
-      "Requirement already satisfied: hijri_converter in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from holidays>=0.10.2->fbprophet==0.7.1) (2.1.1)\n",
-      "Requirement already satisfied: ephem>=3.7.5.3 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from LunarCalendar>=0.0.9->fbprophet==0.7.1) (3.7.7.1)\n",
-      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from matplotlib>=2.0.0->fbprophet==0.7.1) (1.3.1)\n",
-      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from matplotlib>=2.0.0->fbprophet==0.7.1) (2.4.7)\n",
-      "Requirement already satisfied: cycler>=0.10 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from matplotlib>=2.0.0->fbprophet==0.7.1) (0.10.0)\n",
-      "Requirement already satisfied: pillow>=6.2.0 in /Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages (from matplotlib>=2.0.0->fbprophet==0.7.1) (8.2.0)\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install orbit-ml==1.0.13\n",
-    "!pip install fbprophet==0.7.1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "d8a85a5b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:44:11.247033Z",
-     "start_time": "2021-09-03T00:44:11.239738Z"
-    },
-    "id": "environmental-dealing"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import tqdm\n",
-    "import pandas as pd\n",
-    "import statsmodels.api as sm\n",
-    "import inspect\n",
-    "import random\n",
-    "from fbprophet import Prophet\n",
-    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
-    "\n",
-    "import orbit\n",
-    "from orbit.models import DLT\n",
-    "from orbit.utils.dataset import load_m3monthly\n",
-    "from orbit.diagnostics.backtest import BackTester\n",
-    "from orbit.diagnostics.metrics import smape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "be3b8390",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:44:12.661903Z",
-     "start_time": "2021-09-03T00:44:12.659176Z"
-    },
-    "id": "0_43vxJ3cG2J"
-   },
-   "outputs": [],
-   "source": [
-    "seed=2021\n",
-    "n_sample=10\n",
-    "random.seed(seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e394eb60",
-   "metadata": {},
-   "source": [
-    "We can load the m3 dataset from orbit repository.  For demo purpose, i set `n_sample` to be `10`. Feel free to adjust it or simply run the entire dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3f9a81b7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:44:16.340126Z",
-     "start_time": "2021-09-03T00:44:14.294332Z"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "adopted-panel",
-    "outputId": "7a15482b-33ff-4b0d-9d81-ffa5f3ef2a6a"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['N2229', 'N2691', 'N2516', 'N1968', 'N1908', 'N2702', 'N1472', 'N2310', 'N2372', 'N2578']\n"
-     ]
-    }
-   ],
-   "source": [
-    "data = load_m3monthly()\n",
-    "unique_keys = data['key'].unique().tolist()\n",
-    "if n_sample > 0:\n",
-    "    sample_keys = random.sample(unique_keys, 10)\n",
-    "    # just get the first 5 series for demo\n",
-    "    data = data[data['key'].isin(sample_keys)].reset_index(drop=True)\n",
-    "else:\n",
-    "    sample_keys = unique_keys\n",
-    "print(sample_keys)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "21b41737",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:44:16.348761Z",
-     "start_time": "2021-09-03T00:44:16.342154Z"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "processed-barbados",
-    "outputId": "f76fbc7a-85b3-4f4a-fbcc-f8897929e4fc"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['key', 'value', 'date'], dtype='object')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45dd86cb",
-   "metadata": {},
-   "source": [
-    "We need to provide some meta data such as date column, response column etc."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "8831518f",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:44:16.925387Z",
-     "start_time": "2021-09-03T00:44:16.920908Z"
-    },
-    "id": "fabulous-humor"
-   },
-   "outputs": [],
-   "source": [
-    "key_col='key'\n",
-    "response_col='value'\n",
-    "date_col='date'\n",
-    "seasonality=12"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "351f226e",
-   "metadata": {},
-   "source": [
-    "We also provide some setting mimic M3 (see https://forecasters.org/resources/time-series-data/m3-competition/) criteria."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "7dab9ec7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:44:17.473534Z",
-     "start_time": "2021-09-03T00:44:17.470679Z"
-    },
-    "id": "right-naples"
-   },
-   "outputs": [],
-   "source": [
-    "backtest_args = {\n",
-    "    'min_train_len': 1, # not useful; a placeholder\n",
-    "    'incremental_len': 18,  # not useful; a placeholder\n",
-    "    'forecast_len': 18,\n",
-    "    'n_splits': 1,\n",
-    "    'window_type': \"expanding\",\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "40f2411e",
-   "metadata": {},
-   "source": [
-    "We are using `DLT` here.  To use a multiplicative form, we need a natural log transformation of response.  Hence, we need to a wrapper for `DLT`.  We also need to build wrapper for signature prupose for `prophet` and `sarima`.\n",
-    "Note that prophet comes with its own multiplicative form."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "ac574fc4",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:44:45.403466Z",
-     "start_time": "2021-09-03T00:44:45.398075Z"
-    },
-    "id": "incorporated-buddy"
-   },
-   "outputs": [],
-   "source": [
-    "class DLTMAPWrapper(object):\n",
-    "    def __init__(self, response_col, date_col, **kwargs):\n",
-    "        kw_params = locals()['kwargs']\n",
-    "        for key, value in kw_params.items():\n",
-    "            setattr(self, key, value)\n",
-    "        self.response_col = response_col\n",
-    "        self.date_col = date_col\n",
-    "        self.model = DLT(\n",
-    "            response_col=response_col,\n",
-    "            date_col=date_col,\n",
-    "            estimator='stan-map',\n",
-    "            **kwargs)\n",
-    "\n",
-    "    def fit(self, df):\n",
-    "        df = df.copy()\n",
-    "        df[[self.response_col]] = df[[self.response_col]].apply(np.log1p)\n",
-    "        self.model.fit(df)\n",
-    "\n",
-    "    def predict(self, df):\n",
-    "        df = df.copy()\n",
-    "        pred_df = self.model.predict(df)\n",
-    "        pred_df['prediction'] = np.clip(np.expm1(pred_df['prediction']).values, 0, None)\n",
-    "        return pred_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "1d0ac828",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:44:20.932012Z",
-     "start_time": "2021-09-03T00:44:20.920257Z"
-    },
-    "id": "y3fP5z6ofG4C"
-   },
-   "outputs": [],
-   "source": [
-    "class SARIMAXWrapper(object):\n",
-    "    def __init__(self, response_col, date_col, **kwargs):\n",
-    "        kw_params = locals()['kwargs']\n",
-    "        for key, value in kw_params.items():\n",
-    "            setattr(self, key, value)\n",
-    "        self.response_col = response_col\n",
-    "        self.date_col = date_col\n",
-    "        self.model = None\n",
-    "        self.df = None\n",
-    "\n",
-    "    def fit(self, df):\n",
-    "\n",
-    "        df_copy = df.copy()\n",
-    "        infer_freq = pd.infer_freq(df_copy[self.date_col])\n",
-    "        df_copy = df_copy.set_index(self.date_col)\n",
-    "        df_copy = df_copy.asfreq(infer_freq)\n",
-    "        endog = df_copy[self.response_col]\n",
-    "        sig = inspect.signature(SARIMAX)\n",
-    "        all_params = dict()\n",
-    "        for key in sig.parameters.keys():\n",
-    "            if hasattr(self, key):\n",
-    "                all_params[key] = getattr(self, key)\n",
-    "        self.df = df_copy\n",
-    "        self.model = SARIMAX(endog=endog, **all_params).fit(disp=False)\n",
-    "\n",
-    "    def predict(self, df, **kwargs):\n",
-    "        df_copy = df.copy()\n",
-    "        infer_freq = pd.infer_freq(df_copy[self.date_col])\n",
-    "        df_copy = df_copy.set_index(self.date_col)\n",
-    "        df_copy = df_copy.asfreq(infer_freq)\n",
-    "\n",
-    "        pred_array = np.array(self.model.predict(start=df_copy.index[0],\n",
-    "                                                 end=df_copy.index[-1],\n",
-    "                                                 **kwargs))\n",
-    "\n",
-    "        out = pd.DataFrame({\n",
-    "            self.date_col: df[self.date_col],\n",
-    "            'prediction': pred_array\n",
-    "        })\n",
-    "        return out"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "7b03a8c3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:44:21.234126Z",
-     "start_time": "2021-09-03T00:44:21.226394Z"
-    },
-    "id": "Ehq9Ve2L6k0o"
-   },
-   "outputs": [],
-   "source": [
-    "class ProphetWrapper(object):\n",
-    "    def __init__(self, response_col, date_col, **kwargs):\n",
-    "        kw_params = locals()['kwargs']\n",
-    "        for key, value in kw_params.items():\n",
-    "            setattr(self, key, value)\n",
-    "        self.response_col = response_col\n",
-    "        self.date_col = date_col\n",
-    "        self.model = Prophet(**kwargs)\n",
-    "\n",
-    "    def fit(self, df):\n",
-    "        sig = inspect.signature(Prophet)\n",
-    "        all_params = dict()\n",
-    "        for key in sig.parameters.keys():\n",
-    "            if hasattr(self, key):\n",
-    "                all_params[key] = getattr(self, key)\n",
-    "        object_type = type(self.model)\n",
-    "        self.model = object_type(**all_params)\n",
-    "\n",
-    "        train_df = df.copy()\n",
-    "        train_df = train_df.rename(columns={self.date_col: \"ds\", self.response_col: \"y\"})\n",
-    "        self.model.fit(train_df)\n",
-    "\n",
-    "    def predict(self, df):\n",
-    "        df = df.copy()\n",
-    "        df = df.rename(columns={self.date_col: \"ds\"})\n",
-    "        pred_df = self.model.predict(df)\n",
-    "        pred_df = pred_df.rename(columns={'yhat': 'prediction', 'ds': self.date_col})\n",
-    "        pred_df = pred_df[[self.date_col, 'prediction']]\n",
-    "        return pred_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6bc1dfc",
-   "metadata": {},
-   "source": [
-    "Declare model objects and run backtest. Score shows in the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "db00bc70",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:44:48.700884Z",
-     "start_time": "2021-09-03T00:44:48.473609Z"
-    },
-    "id": "bound-occurrence"
-   },
-   "outputs": [],
-   "source": [
-    "dlt = DLTMAPWrapper(\n",
-    "    response_col=response_col,\n",
-    "    date_col=date_col,\n",
-    "    seasonality=seasonality,\n",
-    "    seed=seed,\n",
-    ")\n",
-    "\n",
-    "sarima = SARIMAXWrapper(\n",
-    "    response_col=response_col,\n",
-    "    date_col=date_col,\n",
-    "    seasonality=seasonality,\n",
-    "    seed=seed,\n",
-    ")\n",
-    "\n",
-    "prophet = ProphetWrapper(\n",
-    "    response_col=response_col,\n",
-    "    date_col=date_col,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "13f984c2",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:45:20.549713Z",
-     "start_time": "2021-09-03T00:44:50.214556Z"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "extra-robertson",
-    "outputId": "1b8a808c-aa64-46f1-ffb5-85709f5c9f5a"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/10 [00:00<?, ?it/s]/Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
-      "  warn('Non-stationary starting autoregressive parameters'\n",
-      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
-      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
-      " 10%|█         | 1/10 [00:03<00:29,  3.23s/it]/Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
-      "  warn('Non-stationary starting autoregressive parameters'\n",
-      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
-      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
-      " 20%|██        | 2/10 [00:06<00:25,  3.21s/it]INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
-      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
-      " 30%|███       | 3/10 [00:09<00:21,  3.14s/it]INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
-      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
-      " 40%|████      | 4/10 [00:12<00:18,  3.02s/it]INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
-      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
-      " 50%|█████     | 5/10 [00:15<00:14,  2.96s/it]/Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
-      "  warn('Non-stationary starting autoregressive parameters'\n",
-      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
-      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
-      " 60%|██████    | 6/10 [00:18<00:11,  2.99s/it]INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
-      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
-      " 70%|███████   | 7/10 [00:21<00:09,  3.12s/it]/Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
-      "  warn('Non-stationary starting autoregressive parameters'\n",
-      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
-      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
-      " 80%|████████  | 8/10 [00:24<00:06,  3.06s/it]/Users/zhishiw/.pyenv/versions/3.7.8/envs/orbit378/lib/python3.7/site-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
-      "  warn('Non-stationary starting autoregressive parameters'\n",
-      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
-      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
-      " 90%|█████████ | 9/10 [00:27<00:03,  3.03s/it]INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
-      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
-      "100%|██████████| 10/10 [00:30<00:00,  3.03s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "all_scores = []\n",
-    "\n",
-    "for key in tqdm.tqdm(sample_keys):\n",
-    "    # dlt\n",
-    "    df = data[data[key_col] == key]\n",
-    "    bt = BackTester(\n",
-    "        model=dlt,\n",
-    "        df=df,\n",
-    "        **backtest_args,\n",
-    "    )\n",
-    "    bt.fit_predict()\n",
-    "    scores_df = bt.score(metrics=[smape])\n",
-    "    scores_df[key_col] = key\n",
-    "    scores_df['model'] = 'dlt'\n",
-    "    all_scores.append(scores_df)\n",
-    "    # sarima\n",
-    "    df = data[data[key_col] == key]\n",
-    "    bt = BackTester(\n",
-    "        model=sarima,\n",
-    "        df=df,\n",
-    "        **backtest_args,\n",
-    "    )\n",
-    "    bt.fit_predict()\n",
-    "    scores_df = bt.score(metrics=[smape])\n",
-    "    scores_df[key_col] = key\n",
-    "    scores_df['model'] = 'sarima'\n",
-    "    all_scores.append(scores_df)\n",
-    "    # prophet\n",
-    "    df = data[data[key_col] == key]\n",
-    "    bt = BackTester(\n",
-    "        model=prophet,\n",
-    "        df=df,\n",
-    "        **backtest_args,\n",
-    "    )\n",
-    "    bt.fit_predict()\n",
-    "    scores_df = bt.score(metrics=[smape])\n",
-    "    scores_df[key_col] = key\n",
-    "    scores_df['model'] = 'prophet'\n",
-    "    all_scores.append(scores_df)\n",
-    "\n",
-    "\n",
-    "all_scores = pd.concat(all_scores, axis=0, ignore_index=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "1ac520a5",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:45:52.749454Z",
-     "start_time": "2021-09-03T00:45:52.735908Z"
-    },
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 142
-    },
-    "id": "bigger-insulation",
-    "outputId": "56676af4-b62c-43b4-ca3d-508fae4a3550"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>metric_values</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>dlt</td>\n",
-       "      <td>0.056457</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>prophet</td>\n",
-       "      <td>0.111645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>sarima</td>\n",
-       "      <td>0.097403</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     model  metric_values\n",
-       "0      dlt       0.056457\n",
-       "1  prophet       0.111645\n",
-       "2   sarima       0.097403"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_scores.groupby('model')['metric_values'].apply(np.mean).reset_index()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "orbit_m3_backtest.ipynb",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
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-   },
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-   "mimetype": "text/x-python",
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-   "pygments_lexer": "ipython3",
-   "version": "3.7.8"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/regression1.ipynb b/examples/regression1.ipynb
deleted file mode 100644
index 6530ac21..00000000
--- a/examples/regression1.ipynb
+++ /dev/null
@@ -1,676 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Regression with Orbit - Basic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this demo, we want to demonstartate how to use the different arguments in the model classes (LGT or DLT) to realize different setups for the regressors. Those could be very useful in practice when tuning the models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:41:23.063368Z",
-     "start_time": "2021-09-03T00:41:22.909776Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "from orbit.models import LGT, DLT\n",
-    "from orbit.diagnostics.plot import plot_predicted_data, plot_predicted_components\n",
-    "from orbit.utils.dataset import load_iclaims"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:41:23.686116Z",
-     "start_time": "2021-09-03T00:41:23.500425Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "week              datetime64[ns]\n",
-       "claims                   float64\n",
-       "trend.unemploy           float64\n",
-       "trend.filling            float64\n",
-       "trend.job                float64\n",
-       "sp500                    float64\n",
-       "vix                      float64\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_df = load_iclaims()\n",
-    "raw_df.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:41:23.815601Z",
-     "start_time": "2021-09-03T00:41:23.800241Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "      <th>sp500</th>\n",
-       "      <th>vix</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.318452</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.417633</td>\n",
-       "      <td>0.122654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>13.624218</td>\n",
-       "      <td>0.219882</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.168794</td>\n",
-       "      <td>-0.425480</td>\n",
-       "      <td>0.110445</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>13.398741</td>\n",
-       "      <td>0.236143</td>\n",
-       "      <td>-0.292477</td>\n",
-       "      <td>0.117500</td>\n",
-       "      <td>-0.465229</td>\n",
-       "      <td>0.532339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>13.137549</td>\n",
-       "      <td>0.203353</td>\n",
-       "      <td>-0.194838</td>\n",
-       "      <td>0.106918</td>\n",
-       "      <td>-0.481751</td>\n",
-       "      <td>0.428645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>13.196760</td>\n",
-       "      <td>0.134360</td>\n",
-       "      <td>-0.242466</td>\n",
-       "      <td>0.074483</td>\n",
-       "      <td>-0.488929</td>\n",
-       "      <td>0.487404</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week     claims  trend.unemploy  trend.filling  trend.job     sp500  \\\n",
-       "0 2010-01-03  13.386595        0.219882      -0.318452   0.117500 -0.417633   \n",
-       "1 2010-01-10  13.624218        0.219882      -0.194838   0.168794 -0.425480   \n",
-       "2 2010-01-17  13.398741        0.236143      -0.292477   0.117500 -0.465229   \n",
-       "3 2010-01-24  13.137549        0.203353      -0.194838   0.106918 -0.481751   \n",
-       "4 2010-01-31  13.196760        0.134360      -0.242466   0.074483 -0.488929   \n",
-       "\n",
-       "        vix  \n",
-       "0  0.122654  \n",
-       "1  0.110445  \n",
-       "2  0.532339  \n",
-       "3  0.428645  \n",
-       "4  0.487404  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:41:24.042700Z",
-     "start_time": "2021-09-03T00:41:24.039864Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df=raw_df.copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Use regressors and specify their signs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The regressor columns can be supplied via argument `regressor_col`. Their signs can be specified via `regressor_sign`, with values either '=' (regular, no restriction) or '+' (positive). These two lists should be of the same lenght. The default values of `regressor_sign` is all '='."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Also, note that in general, a better performance can be acheived in regressions with `infer_method=mcmc` due to potential high dimensional distributions of parameters. We will use `mcmc` as sample method in following examples."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:41:25.019561Z",
-     "start_time": "2021-09-03T00:41:25.016260Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "DATE_COL=\"week\"\n",
-    "RESPONSE_COL=\"claims\"\n",
-    "REGRESSOR_COL=['trend.unemploy', 'trend.filling', 'trend.job']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:42:00.324877Z",
-     "start_time": "2021-09-03T00:41:54.272036Z"
-    },
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
-     ]
-    }
-   ],
-   "source": [
-    "dlt_mod=DLT(response_col=RESPONSE_COL,\n",
-    "            date_col=DATE_COL,\n",
-    "            regressor_col=REGRESSOR_COL,\n",
-    "            regressor_sign=[\"+\", '+', '='], \n",
-    "            seasonality=52,\n",
-    "            seed=1)\n",
-    "\n",
-    "dlt_mod.fit(df=df, point_method='median')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The estimated regressor coefficients can be retrieved via `.aggregated_posteriors`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:42:00.340629Z",
-     "start_time": "2021-09-03T00:42:00.327784Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>regressor</th>\n",
-       "      <th>regressor_sign</th>\n",
-       "      <th>coefficient</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>trend.unemploy</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>0.054465</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>trend.filling</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>0.065368</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>trend.job</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>-0.074599</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        regressor regressor_sign  coefficient\n",
-       "0  trend.unemploy       Positive     0.054465\n",
-       "1   trend.filling       Positive     0.065368\n",
-       "2       trend.job        Regular    -0.074599"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dlt_mod.get_regression_coefs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Adjust pirors for regressor beta and regressor standard deviation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the model, it is assumed $$\\beta \\sim Gaussian(\\beta_{prior}, \\sigma_{prior})$$\n",
-    "\n",
-    "The default values for $\\beta_{prior}$ and $\\sigma_{prior}$ are 0 and 1, respectively.\n",
-    "\n",
-    "Users could adjust them via arguments `regressor_beta_prior` and `regressor_sigma_prior`. These two lists should be of the same lenght as `regressor_col`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-07-13T22:37:21.046707Z",
-     "start_time": "2021-07-13T22:37:21.037386Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "dlt_mod=DLT(response_col=RESPONSE_COL,\n",
-    "            date_col=DATE_COL,\n",
-    "            regressor_col=REGRESSOR_COL,\n",
-    "            regressor_sign=[\"+\", '+', '='], \n",
-    "            regressor_beta_prior=[0.05, 0.05, 0],\n",
-    "            regressor_sigma_prior=[0.1, 0.1, 0.1],\n",
-    "            seasonality=52,\n",
-    "            seed=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:42:36.223469Z",
-     "start_time": "2021-09-03T00:42:30.286378Z"
-    },
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
-     ]
-    }
-   ],
-   "source": [
-    "dlt_mod.fit(df=df, point_method='median')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One can notice the significant changes in the estimted coefficients by using different priors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:42:36.245155Z",
-     "start_time": "2021-09-03T00:42:36.227260Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>regressor</th>\n",
-       "      <th>regressor_sign</th>\n",
-       "      <th>coefficient</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>trend.unemploy</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>0.052746</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>trend.filling</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>0.067916</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>trend.job</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>-0.059771</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        regressor regressor_sign  coefficient\n",
-       "0  trend.unemploy       Positive     0.052746\n",
-       "1   trend.filling       Positive     0.067916\n",
-       "2       trend.job        Regular    -0.059771"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dlt_mod.get_regression_coefs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Use data-driven sigma for each coefficients"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Instead of using fixed standard deviations for regressors, a hyperprior can be assigned to them, i.e.\n",
-    "$$\\sigma_\\beta \\sim \\text{Half-Cauchy}(0, \\text{ridge_scale})$$\n",
-    "\n",
-    "This can be done by setting `regression_penalty=\"auto_ridge\"`.  Notice there is hyperprior `auto_ridge_scale` for tuning with a default of `0.5`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:43:22.525566Z",
-     "start_time": "2021-09-03T00:43:13.819068Z"
-    },
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
-     ]
-    }
-   ],
-   "source": [
-    "dlt_mod=DLT(response_col=RESPONSE_COL,\n",
-    "            date_col=DATE_COL,\n",
-    "            regressor_col=REGRESSOR_COL,\n",
-    "            regressor_sign=[\"+\", '+', '='],\n",
-    "            seasonality=52,\n",
-    "            seed=1,\n",
-    "            regression_penalty=\"auto_ridge\",\n",
-    "            auto_ridge_scale=0.5)\n",
-    "\n",
-    "\n",
-    "dlt_mod.fit(df=df, point_method='median')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:43:22.543616Z",
-     "start_time": "2021-09-03T00:43:22.528539Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>regressor</th>\n",
-       "      <th>regressor_sign</th>\n",
-       "      <th>coefficient</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>trend.unemploy</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>0.048657</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>trend.filling</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>0.064906</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>trend.job</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>-0.040714</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        regressor regressor_sign  coefficient\n",
-       "0  trend.unemploy       Positive     0.048657\n",
-       "1   trend.filling       Positive     0.064906\n",
-       "2       trend.job        Regular    -0.040714"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dlt_mod.get_regression_coefs()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.8"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/regression2.ipynb b/examples/regression2.ipynb
deleted file mode 100644
index ec719eb6..00000000
--- a/examples/regression2.ipynb
+++ /dev/null
@@ -1,1209 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Regression with Orbit"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this notebook, we want to demonstartate how to use the different arguments in DLT model to realize different setups for the regressors. Those could be very useful in practice when tuning the models. Here includes two examples, one with the icliams dataset and one for the simulation data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:40:14.487394Z",
-     "start_time": "2021-09-03T00:40:14.477969Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from pylab import rcParams\n",
-    "\n",
-    "from orbit.models import DLT\n",
-    "from orbit.diagnostics.plot import plot_predicted_data, plot_predicted_components\n",
-    "from orbit.utils.simulation import make_regression\n",
-    "from orbit.constants.palette import OrbitPalette\n",
-    "from orbit.utils.dataset import load_iclaims\n",
-    "from orbit.diagnostics.metrics import smape\n",
-    "\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.preprocessing import minmax_scale\n",
-    "\n",
-    "rcParams['figure.figsize'] = 14, 8\n",
-    "from orbit.utils.plot import get_orbit_style\n",
-    "plt.style.use(get_orbit_style())\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "toc-hr-collapsed": false
-   },
-   "source": [
-    "## iclaims Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*iclaims_example* is a dataset containing the weekly initial claims for US unemployment benefits against a few related google trend queries (unemploy, filling and job) from Jan 2010 - June 2018. This aims to mimick the dataset from the paper [Predicting the Present with Bayesian Structural Time Series](https://people.ischool.berkeley.edu/~hal/Papers/2013/pred-present-with-bsts.pdf) by SCOTT and VARIAN (2014).\n",
-    "\n",
-    "Number of claims are obtained from [Federal Reserve Bank of St. Louis](https://fred.stlouisfed.org/series/ICNSA) while google queries are obtained through [Google Trends API](https://trends.google.com/trends/?geo=US).\n",
-    "\n",
-    "In order to use this data to nowcast the US unemployment claims during COVID-19 period, we extended the dataset to Jan 2021 and added the [S&P 500 (^GSPC)](https://finance.yahoo.com/quote/%5EGSPC/history?period1=1264032000&period2=1611187200&interval=1wk&filter=history&frequency=1wk&includeAdjustedClose=true) and [VIX](https://finance.yahoo.com/quote/%5EVIX/history?p=%5EVIX) Index historical data for the same period.\n",
-    "\n",
-    "The data is standardized and log-transformed for the model fitting purpose."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:39:09.414819Z",
-     "start_time": "2021-09-03T00:39:08.592706Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "week              datetime64[ns]\n",
-       "claims                   float64\n",
-       "trend.unemploy           float64\n",
-       "trend.filling            float64\n",
-       "trend.job                float64\n",
-       "sp500                    float64\n",
-       "vix                      float64\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# load data\n",
-    "df = load_iclaims(end_date='2021-01-03')\n",
-    "\n",
-    "df.dtypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:39:09.427343Z",
-     "start_time": "2021-09-03T00:39:09.416664Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>week</th>\n",
-       "      <th>claims</th>\n",
-       "      <th>trend.unemploy</th>\n",
-       "      <th>trend.filling</th>\n",
-       "      <th>trend.job</th>\n",
-       "      <th>sp500</th>\n",
-       "      <th>vix</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2010-01-03</td>\n",
-       "      <td>13.386595</td>\n",
-       "      <td>0.016351</td>\n",
-       "      <td>-0.345855</td>\n",
-       "      <td>0.130569</td>\n",
-       "      <td>-0.543044</td>\n",
-       "      <td>0.082087</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2010-01-10</td>\n",
-       "      <td>13.624218</td>\n",
-       "      <td>0.016351</td>\n",
-       "      <td>-0.222241</td>\n",
-       "      <td>0.181862</td>\n",
-       "      <td>-0.550891</td>\n",
-       "      <td>0.069878</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2010-01-17</td>\n",
-       "      <td>13.398741</td>\n",
-       "      <td>0.032611</td>\n",
-       "      <td>-0.319879</td>\n",
-       "      <td>0.130569</td>\n",
-       "      <td>-0.590640</td>\n",
-       "      <td>0.491772</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2010-01-24</td>\n",
-       "      <td>13.137549</td>\n",
-       "      <td>-0.000179</td>\n",
-       "      <td>-0.222241</td>\n",
-       "      <td>0.119987</td>\n",
-       "      <td>-0.607162</td>\n",
-       "      <td>0.388078</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2010-01-31</td>\n",
-       "      <td>13.196760</td>\n",
-       "      <td>-0.069172</td>\n",
-       "      <td>-0.269869</td>\n",
-       "      <td>0.087552</td>\n",
-       "      <td>-0.614339</td>\n",
-       "      <td>0.446838</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        week     claims  trend.unemploy  trend.filling  trend.job     sp500  \\\n",
-       "0 2010-01-03  13.386595        0.016351      -0.345855   0.130569 -0.543044   \n",
-       "1 2010-01-10  13.624218        0.016351      -0.222241   0.181862 -0.550891   \n",
-       "2 2010-01-17  13.398741        0.032611      -0.319879   0.130569 -0.590640   \n",
-       "3 2010-01-24  13.137549       -0.000179      -0.222241   0.119987 -0.607162   \n",
-       "4 2010-01-31  13.196760       -0.069172      -0.269869   0.087552 -0.614339   \n",
-       "\n",
-       "        vix  \n",
-       "0  0.082087  \n",
-       "1  0.069878  \n",
-       "2  0.491772  \n",
-       "3  0.388078  \n",
-       "4  0.446838  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see form the plot below, there are seasonlity trend and as well as a huge changpoint due the impact of COVID-19."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:39:10.757995Z",
-     "start_time": "2021-09-03T00:39:10.180687Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x576 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axs = plt.subplots(2, 2,figsize=(20,8))\n",
-    "axs[0, 0].plot(df['week'], df['claims'])\n",
-    "axs[0, 0].set_title('Unemployment Claims')\n",
-    "axs[0, 1].plot(df['week'], df['trend.unemploy'], 'tab:orange')\n",
-    "axs[0, 1].set_title('Google trend - unemploy')\n",
-    "axs[1, 0].plot(df['week'], df['vix'], 'tab:green')\n",
-    "axs[1, 0].set_title('VIX')\n",
-    "axs[1, 1].plot(df['week'], df['sp500'], 'tab:red')\n",
-    "axs[1, 1].set_title('S&P500');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:36:22.801122Z",
-     "start_time": "2021-09-03T00:36:22.796675Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# split the dataset\n",
-    "# only use the data after 2018 as we observed from above chart that it has the more clear relationship between trend.unemploy and claims\n",
-    "df = df[df['week'] > '2018-01-01'].reset_index(drop=True)\n",
-    "\n",
-    "test_size = 26\n",
-    "\n",
-    "train_df = df[:-test_size]\n",
-    "test_df = df[-test_size:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Without Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will use the DLT models here to compare the model performance without vs. with regression."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:36:24.545777Z",
-     "start_time": "2021-09-03T00:36:24.543159Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "DATE_COL = 'week'\n",
-    "RESPONSE_COL = 'claims'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:36:24.827701Z",
-     "start_time": "2021-09-03T00:36:24.825247Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "np.random.seed(8888)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:36:40.797385Z",
-     "start_time": "2021-09-03T00:36:40.794433Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "dlt = DLT(response_col=RESPONSE_COL,\n",
-    "          date_col=DATE_COL,\n",
-    "          seasonality=52,\n",
-    "          seed=8888,\n",
-    "          num_warmup=4000,\n",
-    "          n_bootstrap_draws=4000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:36:56.900953Z",
-     "start_time": "2021-09-03T00:36:41.312549Z"
-    },
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:n_eff / iter below 0.001 indicates that the effective sample size has likely been overestimated\n",
-      "WARNING:pystan:Rhat above 1.1 or below 0.9 indicates that the chains very likely have not mixed\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 152 ms, sys: 87.3 ms, total: 239 ms\n",
-      "Wall time: 15.6 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "dlt.fit(df=train_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:36:57.069990Z",
-     "start_time": "2021-09-03T00:36:56.904022Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "predicted_df = dlt.predict(df=test_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:36:57.489044Z",
-     "start_time": "2021-09-03T00:36:57.073614Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_predicted_data(training_actual_df=train_df,\n",
-    "                        predicted_df=predicted_df, \n",
-    "                        date_col=DATE_COL,\n",
-    "                        actual_col=RESPONSE_COL,  \n",
-    "                        test_actual_df=test_df,\n",
-    "                        title='Prediction without Regression - SMAPE:{:.3f}'.format(\n",
-    "                                smape(predicted_df['prediction'].values, test_df['claims'].values)\n",
-    "                            )\n",
-    "                       )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "toc-hr-collapsed": false
-   },
-   "source": [
-    "### With Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The regressor columns can be supplied via argument `regressor_col`. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Regressor with the sign"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:37:24.304186Z",
-     "start_time": "2021-09-03T00:37:10.309469Z"
-    },
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:n_eff / iter below 0.001 indicates that the effective sample size has likely been overestimated\n",
-      "WARNING:pystan:Rhat above 1.1 or below 0.9 indicates that the chains very likely have not mixed\n"
-     ]
-    }
-   ],
-   "source": [
-    "REGRESSOR_COL = ['trend.unemploy', 'trend.filling', 'trend.job', 'sp500', 'vix']\n",
-    "\n",
-    "dlt_reg = DLT(response_col=RESPONSE_COL, \n",
-    "              date_col=DATE_COL,\n",
-    "              regressor_col=REGRESSOR_COL,\n",
-    "              seasonality=52,\n",
-    "              seed=8888,\n",
-    "              num_warmup=4000,\n",
-    "              n_bootstrap_draws=4000)\n",
-    "\n",
-    "\n",
-    "dlt_reg.fit(df=train_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The estimated regressor coefficients can be retrieved via `.get_regression_coefs()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:37:24.320268Z",
-     "start_time": "2021-09-03T00:37:24.306504Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>regressor</th>\n",
-       "      <th>regressor_sign</th>\n",
-       "      <th>coefficient</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>trend.unemploy</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>1.061864</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>trend.filling</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>0.442926</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>trend.job</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>-0.002502</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>sp500</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>-0.449663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>vix</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>0.045691</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        regressor regressor_sign  coefficient\n",
-       "0  trend.unemploy        Regular     1.061864\n",
-       "1   trend.filling        Regular     0.442926\n",
-       "2       trend.job        Regular    -0.002502\n",
-       "3           sp500        Regular    -0.449663\n",
-       "4             vix        Regular     0.045691"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dlt_reg.get_regression_coefs()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:37:24.488075Z",
-     "start_time": "2021-09-03T00:37:24.324262Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "predicted_df_reg = dlt_reg.predict(test_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:37:24.868147Z",
-     "start_time": "2021-09-03T00:37:24.490194Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_predicted_data(training_actual_df=train_df,\n",
-    "                        predicted_df=predicted_df_reg, \n",
-    "                        date_col=DATE_COL,\n",
-    "                        actual_col=RESPONSE_COL,  \n",
-    "                        test_actual_df=test_df,\n",
-    "                        title='Prediction with Regression - SMAPE:{:.4f}'.format(\n",
-    "                                smape(predicted_df_reg['prediction'].values, test_df['claims'].values)\n",
-    "                            )\n",
-    "                       )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After adding the regressor, prediction results are more closer to the real response compared to without using regression. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Adjust pirors for regressor beta and standard deviation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the model, it is assumed $$\\beta \\sim Gaussian(\\beta_{prior}, \\sigma_{prior})$$\n",
-    "\n",
-    "The default values for $\\beta_{prior}$ and $\\sigma_{prior}$ are 0 and 1, respectively.\n",
-    "\n",
-    "Assuming users obtain further knowledge on each regressor, they could adjust prior knowledge on the coefficients via arguments `regressor_beta_prior` and `regressor_sigma_prior`. These two lists should be of the same lenght as `regressor_col`.\n",
-    "\n",
-    "Also, assuming users have some prior knowledge on direction of impact by each regressor, users can specifiy them via `regressor_sign`, with a list of values consist '=' (regular, no restriction), '+' (positive) or '-' (negative). Such list should be of the same length. The default values of `regressor_sign` is all '='."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:38:18.208301Z",
-     "start_time": "2021-09-03T00:38:03.313509Z"
-    },
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:n_eff / iter below 0.001 indicates that the effective sample size has likely been overestimated\n",
-      "WARNING:pystan:Rhat above 1.1 or below 0.9 indicates that the chains very likely have not mixed\n"
-     ]
-    }
-   ],
-   "source": [
-    "dlt_reg_adjust = DLT(response_col=RESPONSE_COL,\n",
-    "                     date_col=DATE_COL,\n",
-    "                     regressor_col=REGRESSOR_COL,\n",
-    "                     regressor_sign=[\"+\", '+', '=', '-', '+'], \n",
-    "                     regressor_beta_prior=[0.5, 0.25, 0.07, -0.3, 0.03],\n",
-    "                     regressor_sigma_prior=[0.05] * 5,\n",
-    "                     seasonality=52,\n",
-    "                     seed=8888,\n",
-    "                     num_warmup=4000,\n",
-    "                     n_bootstrap_draws=4000)\n",
-    "\n",
-    "\n",
-    "dlt_reg_adjust.fit(df=train_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:38:18.233350Z",
-     "start_time": "2021-09-03T00:38:18.211091Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>regressor</th>\n",
-       "      <th>regressor_sign</th>\n",
-       "      <th>coefficient</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>trend.unemploy</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>0.457481</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>trend.filling</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>0.279099</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>vix</td>\n",
-       "      <td>Positive</td>\n",
-       "      <td>0.044241</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>sp500</td>\n",
-       "      <td>Negative</td>\n",
-       "      <td>-0.307547</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>trend.job</td>\n",
-       "      <td>Regular</td>\n",
-       "      <td>0.061649</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        regressor regressor_sign  coefficient\n",
-       "0  trend.unemploy       Positive     0.457481\n",
-       "1   trend.filling       Positive     0.279099\n",
-       "2             vix       Positive     0.044241\n",
-       "3           sp500       Negative    -0.307547\n",
-       "4       trend.job        Regular     0.061649"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dlt_reg_adjust.get_regression_coefs()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:38:18.393223Z",
-     "start_time": "2021-09-03T00:38:18.236957Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "predicted_df_reg_adjust = dlt_reg_adjust.predict(test_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:38:18.789428Z",
-     "start_time": "2021-09-03T00:38:18.395690Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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yWv2RXV1dbb761a8aSWbhwoUBHx9zU+gQK8xNwSJ2mJs6K9wxM3nyZCPJzJs3r9VJp/Xr1zcnHp577rnm2zdu3GgkmT59+rQaz2uvvWaysrJMRkaGqaqqCvj4/v3vfxtJZvTo0ebAgQPNt//zn/80drvd9O7d21RXV/t87MaNG01xcXFzjJPQ9iJm2sbMvHnzjCTz7LPPBryfZEPctI6bUM+PiSjWYqalv//9783J7GAT2l2JwR07dphzzz23ef8ktBPIpEmT2p1Q9u/f31zVceLECfPyyy8bSWbixIlttt26dauxLMv079+/1R/AxhizatWq5gAaPHhwh3/MP/nkk0aSueSSS9rc9+abbxpJ5owzzgj4+O644w4jydx2221t7lu8eLGRZK677rpWt7vdbrN48WLTu3fvVmNO9qQBsdI2Vv77v//bSDIVFRUB7ycZETutY6cpsXHllVf6fL6ZM2caSWbDhg0BjyHRxFrMnHfeeUaS+d3vftfmvqNHj5rCwkJTWFjot0KzCXNT6BArzE3BInaYmzornDGzZcuW5i/rdXV1bR5z7733Gknm2muvbb7tuuuuM5LMH//4xzbb//SnPzWSzB133BHw8TVVry1btqzNfU3VkadXwR09etQsWLDApKenG0lm0KBBJLRbIGbaxszEiRONJLN79+6A95NsiJvWcRPq+TERxVrMGGPM9u3bm084ZGZmNlfUB5PQDiYG6+rqzL333mvy8vJa/R1GQjuBXH755WbEiBGtzoy15HQ6jSTz+eefmx/84AdG8lZt+DJmzBgjybz77rutbm8KoGuuucYcPHiwwz/mn3rqKSPJnHXWWW2+FDSdyZs8eXLAx9d05umtt95qc5/L5TKWZZmCgoJWt2/YsMFIMunp6eZXv/qVeemll0gaGGLFV6w0VUl+8sknAe8nGRE7rWPn0ksvNZLM4sWLfT7f3XffbSSZu+66K+AxJJpYi5nMzEwjyWzfvt3nPpo+C1avXh3Q8TE3hQ6xwtwULGKHuamzwhkzb775ppkwYYK54YYbfG6/dOlSI8lcdNFFzbd169bNSGp1GXmTpjnj7LPPDujYDh8+bGw2m8nOzu7wb6errrqq1e3333+/kWR69+5tnnrqKXPnnXeS0G6BmGkdM26322RlZZk+ffoEtI9kRdy0jptQz4+JKNZixhhjvvjFLxrJ27Ls/fffNxdccEHQCe1gYrAplvLy8syf/vQn8+ijj8ZFQptFITvhmWee0ccff6xu3bq1uW/btm1yuVxyOBzq3r27PvzwQ0nSmWee6fO5zjjjDEnS+++/3+r2yy67TKtXr9Zjjz0mp9PZ4XimTJmirKwsvffee/r2t7+tzz77TNXV1Vq+fLnmz58vy7L0gx/8IKBjM8boo48+anfMBQUF6tWrlw4dOqSqqqrm29PT0/Wd73xHn3zyiRYsWCC73R7Q/hIdsdI2VjZs2KCsrCy99dZbmjRpkvLy8lRQUKArrrhCa9asCWjfyYDYaR07Ho9HkpSbm+vzOVNSUiRJH3/8cUBjSESxFjOhfM+Ym0KLWGFuChaxw9zUWeGMmYkTJ2r16tVatGiRz+2bfneLiookSXv27NHBgwdVWFionj17ttl+5MiRsixLH330kdxut99j+/jjj+XxeDRixIjm97qj8Tbp2bOnfvOb32jLli268sor/e4n2RAzrWNm8+bNqq6uVnFxsX79619r9OjRyszMVN++fXXjjTe2msuSGXHTOm6Yn/yLpZhpctZZZ+lvf/ub3n777Xb3FYhgYzAvL0+33nqrtm3bphtvvDHo/UcaCe0Q+Z//+R9J0uWXX6709HTt2rVLktS7d2+f2zfdvnfv3la3P/744wGvVtytWzc98cQT6tatmx5++GENHDhQ2dnZmjVrljwej5YvX66rrroqoOc6dOiQamtrlZOTo6ysrIDHPGLECD300EPq169fQPtBcsZKVVWV9u7dq+rqan3jG9+Qx+PRtGnT5HQ69eyzz2rSpEn6xz/+EdD+k1kyxs6IESMkSa+++qrP7V9//XVJ0v79+wMaQ7KJRsx09J7V1tY2r8geyHvG3BQ5yRgrzE2hkYyxw9zUNaGKGV/27Nmj3//+95KkL3/5y5Lk9/nT0tJUUFCguro6HTlyxO8+gh1vWVmZ/vu//7vdOEP7kjFm1q9fL0latWqVfvnLX6pPnz664IILVF9fr4cffljnnHNO88k4+JaMcRPK+TEZRTpmmvziF79QWVmZLMsKeuxS8DE4bdo03X333T6T/LGMhHYI3H///XriiSeUmZmpX//615Kk6upqSVJmZqbPx2RkZEiSjh8/3qV9jx07Vl/60peUkpKiiRMn6rLLLlOPHj1UVVWle+65Ry6XK6Dn8TfeUI45mSVrrGzYsEGSVFhYqDfffFOrV6/W008/ra1bt+q+++5TY2OjZs+ercrKyq4cYkJL1tj55je/Kcuy9OCDD+qf//xnq20ffvhhPf3005Kkurq6zh5WwotWzFx//fWSpO9973utKkbq6uo0b9487d69u/lnf5ibIiNZY4W5qeuSNXaYm4IXzpiprq7Wl770JR09elQXXXSRrrjiioCevzP76Mx4m7ZD1yRrzDTNUSUlJdq+fbuWL1+u559/Xp9++qnKysq0d+9elZWVyRjjd//JKFnjJpTzY7KJRsyEWrJ9dyKh3UW/+93v9IMf/ECWZemRRx5pPiPWdHmzvzMsTZeEBGP79u0aP368nn76ab355pt688039eyzz2rHjh267rrr9OKLLwZcORnoeLs65mSWzLFy2WWXqbKyUuvXr9fEiROb77csS9///vd15ZVXqqamRo888kgQR5f4kjl2xo4dq7vuuktut1tf+cpXdNZZZ+mqq67SqFGj9O1vf1s33XSTJCk1NTXII0xM0YyZ+fPn6/LLL1dlZaXGjRunyZMnq7S0VIMHD9YTTzyhb37zm5ICe8+Ym8IvmWOFualrkjl2mJuCE86YOXr0qC699FK9+eabGjRokP72t7813xfquaQz4yXZ2DXJHDN33XWXtmzZouXLl6tPnz7N22RlZWnRokXq27ev3nvvPa1atcrv/pNNMsdNKOfHZBKtmAm1ZPvu1LYRDwJijNGtt96qe+65R3a7XY888oi+9rWvNd+fnZ0tSaqpqfH5+Kbbm7YLxu23366qqiotWbJEJSUlzbdnZmbqz3/+s15//XWtWrVKr732mqZMmaJf//rXPnslLViwQH379u1wvKEaczIiVrwfqE2P8+WKK67Q008/rXfeeSeo40tUxI7XrbfeqlGjRumee+7RunXr9Nlnn+m8885TeXm50tLS9OCDDyo/Pz/oY0wksRAzdrtdTz/9tH7/+9/rkUce0dq1a5Wfn6+ZM2fqZz/7mf7+979LUvN7xtwUHcQKc1OwiB0v5qbAhTtmKisrdfnll+vdd9/VkCFD9PLLL7fqHerv+X3t49prr/W53WOPPRbweLOysrp8+XiyIma8Ccfi4mKf22dmZuqiiy7SX//6V73zzju64IIL2h1nMiFuOj8/Jrtox0wwvv/97/tsGfO73/0uqBiMa5FdgzIxnDhxwlx99dVGksnIyDBPP/10m20uv/xyI8k8//zzPp+jrKzMSDKPPfZYh/tSByu89+zZ00gyu3fv9vnYG264wUgyDzzwgDHGNK+Uevp/r7zyivF4PCYnJ8dIMidOnPD5fL179zaSTGVlZbvjfeWVV4wkM2nSpA6PK1kQK+3HSkvPPfecz9V+kxmxE1jsPPLII0aS+clPfhLQ9oksVmLGn+uuu85IMv/5z3+MMcxN0UCsMDcFi9hhbuqscMfM+vXrTZ8+fYwkM27cOLNnz5422xw4cMBIMj179vT5/LW1tcayLONwOIzb7TbGGJ/x0vTV+Z133jGSzHnnnefz+TZv3mwkmeLiYt8vykl33nmnkWQWLFjQ4XbJhpgJzI9//GMjyfziF78I+DGJjLgJzOnzYzKLhZhpT9PfLS+++GKb+wYMGOAzZnbs2BFUDPry6KOPGknmmmuuCXjM0UDLkU46evSoLr74Yv3rX/9S9+7d9corr+iLX/xim+2aViZtb6GGptVSR48eHfRYDh8+LEk+V7xteXt9fb0kaeXKlTLGtPnvwgsvlGVZGjVqlCTfK966XC7t2bNHBQUFHVYz4RRi5VSs/PnPf9bXvvY1Pfvssz73v337dkltV/tNVsTOqdjZv3+/VqxYoc2bN/vc/4oVKySpVfV4MoqlmNm2bZuWL1/ebm/1V155RZZl6ZxzzpHE3BRpxApzU7CIHeamzgp3zLzyyiuaOnWqdu3apUsvvVSvvvqqz8q3bt26qVevXtq7d68OHjzY5v6PPvpIxhiNGjVKNpv367GveDEnL+kfOXKkbDabNm3a5POS7VDEeLIiZk6N9wc/+IGuvvpqffbZZz6PkTnqFOLm1Hg7Oz8mq1iJmWB8+umnPmNm4MCBQcVgXAtZajwJ1NfXm8mTJxtJZsiQIWbr1q3tbvvqq68aSWbKlClt7tu6dauxLMv069fPeDyeDvepDqpTxo4daySZ//3f/21zX0NDgxk+fHi7Z3V8+fnPf24kmTvuuKPNfU2VJv7O0FAF50WstI6V//mf/zGSzBe/+MU223s8HjN+/HgjyTz++OMB7T+RETutY2f58uVGkvnyl7/cZvs9e/aYzMxMk5+fb44ePRrQ/hNRrMXMf//3fxtJpry8vM19zzzzTKcrXpmbQodYYW4KFrHD3NRZ4Y6ZtWvXmqysLCPJfOtb3zKNjY0djmf27NlGknnkkUfa3HfHHXd0ulJ62rRpRpJ5+eWX29zXVAH58MMPd/gcVGi3Rsy0jpnzzz/fSDL3339/m+13795tcnJyjN1uN59//nnAY0hExE3ruAn1/JiIYi1mfOmoQtufUMRgvFRok9DuhKYvPr169fJ7yaHH42lOBDVdim+MMUeOHDFTp05tc3t7Ovpj/o9//GPz5QQbN25svr2urs5897vfNZLMmWee2eGlBC3t3LnTZGZmmqysLPPGG280375p0ybTq1cvI6nVfnwhaeBFrLSOla1btxqHw2EkmcWLFzff7na7ze23324kmTPOOMPU19cHtP9ERuy0jp0TJ06YgoICY7fbzcqVK5tvP3ToUPNE/6tf/SqgfSeqWIuZ1atXG0mmX79+rVrVfPjhh82X3b3++usBHx9zU+gQK8xNwSJ2mJs6K5wxU1NTY4YMGWIkmeuuuy6g8axdu9bYbDbTq1cvs2nTpubb33jjDZOVlWXS09M7dTn4v/71LyPJjBw5slUMPvnkk8Zut5vevXub2traDp+DhHZrxEzrmPnLX/5iJJm8vDyzfv365tuPHj1qZs2a1ZwsS3bETeu4CfX8mIhiLWZ86UpCOxQxGC8JbcsYll4OxMGDB9W/f3+dOHFCY8aMab70wJff/va36tmzpzZu3KgLLrhAR48e1bhx4zRo0CC99tpr2rdvny677DI9/fTT7V7G36SpuX9DQ0ObbY0x+uY3v6m//vWvstlsmjx5svLz87Vu3TpVVVWpd+/eWrFiRfMKrYF45JFHNHfuXNlsNk2bNk1paWl6+eWXVVtbq7vuuku33XZbh49fuXKlpk2bpkmTJun1118PeL+JhFjxHSuLFi3St7/9bXk8Ho0ZM0ZDhw7Vxo0btXXrVvXq1UsrV67U8OHDA95/IiJ2fMfOk08+qS9/+cuy2+264IILlJ2drVWrVunw4cP68pe/rIqKCr/HmKhiMWYk6ZZbbtEf/vAH5eXlacqUKaqpqdGrr76qxsZG3X///fqv//qvTh0nc1PXESvMTcEidpibOivcMfPQQw/ppptukiRdffXVysjI8Pncw4cP1x133NH8809/+lP98pe/VHp6ui6++GLV1dXplVdekcfj0WOPPaavf/3rnTrOb3zjG/rrX/+q3NxcXXTRRTpw4IDeeOMNORwOLVu2TBdeeGGHj//Zz36mn//851qwYIF+9atfdWrfiYaYaRszxhhdf/31+stf/qKUlBRNmjRJTqdTq1at0sGDBzV58mQtW7ZMWVlZnRpDIiFufH/WhHp+TCSxGjOnu/DCC/Xqq6/qxRdf1Be+8IVOH2dXY3DJkiWaPXu2rrnmGj322GOd3n/ERDObHk+efPLJ5koRf/9t2bKl+XGbN282X/3qV01hYaHJyMgwZ555prnnnnv8nrFv0vScHS2I8/jjj5sLLrjA5ObmGofDYYYMGWK+973vderMX0vLli0zF154ocnOzjZ5eXnm/PPPN08++WRAj6UKjljpKFZee+01c8UVVxin02lSU1PNwIEDzS233GL27dsX1P4TDbHTfuw899xzZsqUKc2XcU+YMME8+uijQV3ClUhiNWbcbre5//77zRlnnGEcDofp1auXueyyy1pVMnYWc1PXECvMTcEidpibOivcMXPFFVcE9Ny+PvMff/xxM378eJOZmWkKCwvNF77wBfPKK68EdZxut9v84Q9/MGeddZZJT083vXr1Ml/84hfNhg0bAno8FdqnEDMb2n3MkiVLzMSJE01WVpbJyMgwY8aMMffeey9XDxnipr24Ccf8mChiOWZa6kqFdpOuxCAV2gAAAAAAAAAAhFACLGsJAAAAAAAAAEgGJLQBAAAAAAAAAHGBhDYAAAAAAAAAIC6Q0AYAAAAAAAAAxAUS2gAAAAAAAACAuEBCGwAAAAAAAAAQF0hoAwAAAAAAAADiAgltAAAAAAAAAEBcIKENAAAAAAAAAIgLJLQBAAAAAAAAAHGBhDYAAAAAAAAAIC6Q0AYAAAAAAAAAxIX/D+K2K7pNVY12AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = plot_predicted_data(training_actual_df=train_df,\n",
-    "                        predicted_df=predicted_df_reg_adjust, \n",
-    "                        date_col=DATE_COL,\n",
-    "                        actual_col=RESPONSE_COL,  \n",
-    "                        test_actual_df=test_df,\n",
-    "                        title='Prediction with Regression using adjust priors - SMAPE:{:.4f}'.format(\n",
-    "                        smape(predicted_df_reg_adjust['prediction'].values, test_df['claims'].values))\n",
-    "                       )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Regression on Simulated Dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Leverage the simulation function to generate a dateset with regression terms"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:39:20.711488Z",
-     "start_time": "2021-09-03T00:39:20.706626Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.15602066,  0.05231785,  0.16325487, -0.11182046,  0.0027203 ])"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# To scale regressor values in a nicer way\n",
-    "SEED = 2020\n",
-    "NUM_OF_REGRESSORS = 5\n",
-    "COEFS = np.random.default_rng(SEED).normal(.03, .1, NUM_OF_REGRESSORS)\n",
-    "\n",
-    "COEFS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:39:21.051265Z",
-     "start_time": "2021-09-03T00:39:21.047565Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "x, y, coefs = make_regression(200, COEFS, seed=SEED)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:39:21.364494Z",
-     "start_time": "2021-09-03T00:39:21.355177Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(x)\n",
-    "regressor_cols = [f\"regressor_{x}\" for x in range(1, NUM_OF_REGRESSORS + 1)]\n",
-    "df.columns = regressor_cols\n",
-    "#df[regressor_cols] = df[regressor_cols]/REG_BASE\n",
-    "#df[regressor_cols] = df[regressor_cols].apply(np.log1p)\n",
-    "\n",
-    "# min max scale to avoid negetive values\n",
-    "response_col = \"response\"\n",
-    "df[response_col] = y \n",
-    "df[response_col] = minmax_scale(df[response_col])\n",
-    "\n",
-    "# add the date column\n",
-    "df['date'] = pd.date_range(start='2016-01-04', periods=200, freq=\"1W\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:39:21.646916Z",
-     "start_time": "2021-09-03T00:39:21.635103Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>regressor_1</th>\n",
-       "      <th>regressor_2</th>\n",
-       "      <th>regressor_3</th>\n",
-       "      <th>regressor_4</th>\n",
-       "      <th>regressor_5</th>\n",
-       "      <th>response</th>\n",
-       "      <th>date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.630103</td>\n",
-       "      <td>0.111589</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.709102</td>\n",
-       "      <td>-0.136399</td>\n",
-       "      <td>0.724856</td>\n",
-       "      <td>2016-01-10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.125509</td>\n",
-       "      <td>0.136342</td>\n",
-       "      <td>-0.880252</td>\n",
-       "      <td>0.543985</td>\n",
-       "      <td>0.535811</td>\n",
-       "      <td>2016-01-17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.292064</td>\n",
-       "      <td>0.192392</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.042711</td>\n",
-       "      <td>0.713783</td>\n",
-       "      <td>2016-01-24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.666333</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>-0.240312</td>\n",
-       "      <td>-0.408502</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.248319</td>\n",
-       "      <td>2016-01-31</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.593637</td>\n",
-       "      <td>-0.256724</td>\n",
-       "      <td>-0.000000</td>\n",
-       "      <td>0.031756</td>\n",
-       "      <td>0.546676</td>\n",
-       "      <td>0.440959</td>\n",
-       "      <td>2016-02-07</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   regressor_1  regressor_2  regressor_3  regressor_4  regressor_5  response  \\\n",
-       "0     0.630103     0.111589     0.000000    -0.709102    -0.136399  0.724856   \n",
-       "1     0.000000     0.125509     0.136342    -0.880252     0.543985  0.535811   \n",
-       "2    -0.000000     0.292064     0.192392     0.000000     0.042711  0.713783   \n",
-       "3     0.666333    -0.000000    -0.240312    -0.408502     0.000000  0.248319   \n",
-       "4     0.593637    -0.256724    -0.000000     0.031756     0.546676  0.440959   \n",
-       "\n",
-       "        date  \n",
-       "0 2016-01-10  \n",
-       "1 2016-01-17  \n",
-       "2 2016-01-24  \n",
-       "3 2016-01-31  \n",
-       "4 2016-02-07  "
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:39:22.420338Z",
-     "start_time": "2021-09-03T00:39:22.278637Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(df['response']);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Use data-driven sigma for each coefficients"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Instead of using fixed standard deviations for regressors, a hyperprior can be assigned to them, i.e.\n",
-    "$$\\sigma_\\beta \\sim \\text{Half-Cauchy}(0, \\text{ridge_scale})$$\n",
-    "\n",
-    "This can be done by setting `regression_penalty=\"auto_ridge\"`.  Notice there is a hyperprior `auto_ridge_scale` for tuning with a default of `0.5`. We can also supply stan config such as `adapt_delta` to reduce divergence see [here](https://mc-stan.org/rstanarm/reference/adapt_delta.html) for details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:38:51.380712Z",
-     "start_time": "2021-09-03T00:38:40.021830Z"
-    },
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "WARNING:pystan:22 of 1000 iterations ended with a divergence (2.2 %).\n",
-      "WARNING:pystan:Try running with adapt_delta larger than 0.9 to remove the divergences.\n"
-     ]
-    }
-   ],
-   "source": [
-    "mod_auto_ridge = DLT(response_col=\"response\",\n",
-    "                     date_col=\"date\",\n",
-    "                     regressor_col=regressor_cols,\n",
-    "                     seasonality=52,\n",
-    "                     seed=SEED,\n",
-    "                     regression_penalty='auto_ridge',\n",
-    "                     num_warmup=4000,\n",
-    "                     num_sample=1000,\n",
-    "                     stan_mcmc_control={'adapt_delta':0.9})\n",
-    "\n",
-    "\n",
-    "mod_auto_ridge.fit(df=df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:38:51.387517Z",
-     "start_time": "2021-09-03T00:38:51.383428Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "coef_auto_ridge = np.median(mod_auto_ridge._posterior_samples['beta'], axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:40:49.358201Z",
-     "start_time": "2021-09-03T00:40:49.134955Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lw = 3\n",
-    "plt.figure(figsize=(20, 8))\n",
-    "plt.title(\"Copmparsion of the Coefficients from Different Models\")\n",
-    "plt.plot(coef_auto_ridge, color=OrbitPalette.GREEN.value, linewidth=lw, label=\"Auto Ridge\", alpha=0.5, linestyle='--')\n",
-    "plt.plot(coefs, color=\"black\", linewidth=lw, label=\"Ground truth\")\n",
-    "plt.legend()\n",
-    "plt.grid()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result looks reasonable comparing to the true coefficients."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.8"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/utilities_simulation.ipynb b/examples/utilities_simulation.ipynb
deleted file mode 100644
index 74e96293..00000000
--- a/examples/utilities_simulation.ipynb
+++ /dev/null
@@ -1,371 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Simulation Data\n",
-    "\n",
-    "Orbit provide the functions to generate the simulation data including: \n",
-    "\n",
-    "1. Generate the data with time-series trend:\n",
-    "     - random walk\n",
-    "     - arima\n",
-    "2. Generate the data with seasonality\n",
-    "     - discrete\n",
-    "     - fourier series\n",
-    "3. Generate regression data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:35:46.950159Z",
-     "start_time": "2021-09-03T00:35:45.779061Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
-    "from orbit.utils.simulation import make_trend, make_seasonality, make_regression\n",
-    "\n",
-    "from orbit.utils.plot import get_orbit_style\n",
-    "plt.style.use(get_orbit_style())\n",
-    "\n",
-    "from orbit.constants.palette import OrbitPalette\n",
-    "\n",
-    "plt.rcParams['figure.figsize'] = [8, 8]\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Trend"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Random Walk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:35:47.138503Z",
-     "start_time": "2021-09-03T00:35:46.951857Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "rw = make_trend(200, rw_loc=0.02, rw_scale=0.1, seed=2020)\n",
-    "plt.plot(rw, color=OrbitPalette.BLUE.value);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ARMA\n",
-    "\n",
-    "reference for the ARMA process: https://www.statsmodels.org/stable/generated/statsmodels.tsa.arima_process.ArmaProcess.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:35:47.286673Z",
-     "start_time": "2021-09-03T00:35:47.141193Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "arma_trend =  make_trend(200, method='arma', arma=[.8, -.1], seed=2020)\n",
-    "plt.plot(arma_trend, color=OrbitPalette.BLUE.value);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Seasonality"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Discrete"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "generating a weekly seasonality(=7) where seasonality wihtin a day is constant(duration=24) on an hourly time-series"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:35:47.470647Z",
-     "start_time": "2021-09-03T00:35:47.289064Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds = make_seasonality(500, seasonality=7, duration=24, method='discrete', seed=2020)\n",
-    "plt.plot(ds, color=OrbitPalette.BLUE.value);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Fourier"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "generating a sine-cosine wave seasonality for a annual seasonality(=365) using fourier series"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:35:47.588607Z",
-     "start_time": "2021-09-03T00:35:47.472433Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fs = make_seasonality(365 * 3, seasonality=365, method='fourier', order=5, seed=2020)\n",
-    "plt.plot(fs, color=OrbitPalette.BLUE.value);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:35:47.595597Z",
-     "start_time": "2021-09-03T00:35:47.590975Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.01162034, 0.00739299, 0.00282248, ..., 0.02173615, 0.01883928,\n",
-       "       0.01545216])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "generating multiplicative time-series with trend, seasonality and regression components"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:35:47.603042Z",
-     "start_time": "2021-09-03T00:35:47.600375Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# define the regression coefficients\n",
-    "coefs = [0.1, -.33, 0.8]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:35:47.611425Z",
-     "start_time": "2021-09-03T00:35:47.608250Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "x, y, coefs = make_regression(200, coefs, scale=2.0, seed=2020)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:35:47.787760Z",
-     "start_time": "2021-09-03T00:35:47.613829Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(y, color=OrbitPalette.BLUE.value);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-09-03T00:35:47.795722Z",
-     "start_time": "2021-09-03T00:35:47.790568Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# check if get the coefficients as set up \n",
-    "reg = LinearRegression().fit(x, y)\n",
-    "print(reg.coef_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "env_orbit",
-   "language": "python",
-   "name": "env_orbit"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/wbic.ipynb b/examples/wbic.ipynb
deleted file mode 100644
index 1ef42ce9..00000000
--- a/examples/wbic.ipynb
+++ /dev/null
@@ -1,751 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# WBIC/BIC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook gives a tutorial on how to use Watanabe-Bayesian information criterion (WBIC) and Bayesian information criterion (BIC) for feature selection (Watanabe[2010], McElreath[2015], and Vehtari[2016]). The WBIC or BIC is an information criterion. Similar to other criteria (AIC, DIC), the WBIC/BIC endeavors to find the most parsimonious model, i.e., the model that balances fit with complexity. In other words a model (or set of features) that optimizes WBIC/BIC should neither over nor under fit the available data. \n",
-    "\n",
-    "In this tutorial a data set is simulated using the damped linear trend (DLT) model. This data set is then used to fit DLT models with varying number of features as well as a global local trend model (GLT), and a Error-Trend-Seasonal (ETS) model. The WBIC/BIC criteria is then show to find the true model. \n",
-    "\n",
-    "Note that we recommend the use of WBIC for full Bayesian and SVI estimators and BIC for MAP estimator."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-25T01:00:59.543313Z",
-     "start_time": "2022-03-25T01:00:57.082822Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from datetime import timedelta\n",
-    "\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "import orbit\n",
-    "from orbit.models import DLT,ETS, KTRLite, LGT\n",
-    "from orbit.utils.simulation import make_trend, make_regression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-25T01:00:59.549375Z",
-     "start_time": "2022-03-25T01:00:59.545300Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.1.3dev\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(orbit.__version__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "toc-hr-collapsed": false
-   },
-   "source": [
-    "## Data Simulation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This block of code creats random data set (365 observations with 10 features) assuming a DLT model. Of the 10 features 5 are effective regressors; i.e., they are used in the true model to create the data.\n",
-    "\n",
-    "As an exerise left to the user once you have run the code once try changing the `NUM_OF_EFFECTIVE_REGRESSORS` (line 2), the `SERIES_LEN` (line 3), and the `SEED` (line 4) to see how it effects the results. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-25T01:00:59.566063Z",
-     "start_time": "2022-03-25T01:00:59.553063Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "NUM_OF_REGRESSORS = 10\n",
-    "NUM_OF_EFFECTIVE_REGRESSORS = 4\n",
-    "SERIES_LEN = 365\n",
-    "SEED = 1\n",
-    "# sample some coefficients\n",
-    "COEFS = np.random.default_rng(SEED).uniform(-1, 1, NUM_OF_EFFECTIVE_REGRESSORS)\n",
-    "trend = make_trend(SERIES_LEN, rw_loc=0.01, rw_scale=0.1)\n",
-    "x, regression, coefs = make_regression(series_len=SERIES_LEN, coefs=COEFS)\n",
-    "\n",
-    "# combine trend and the regression\n",
-    "y = trend + regression\n",
-    "y = y - y.min()\n",
-    "\n",
-    "\n",
-    "x_extra = np.random.normal(0, 1, (SERIES_LEN, NUM_OF_REGRESSORS - NUM_OF_EFFECTIVE_REGRESSORS))\n",
-    "x = np.concatenate([x, x_extra], axis=-1)\n",
-    "\n",
-    "x_cols = [f\"x{x}\" for x in range(1, NUM_OF_REGRESSORS + 1)]\n",
-    "response_col = \"y\"\n",
-    "dt_col = \"date\"\n",
-    "obs_matrix = np.concatenate([y.reshape(-1, 1), x], axis=1)\n",
-    "# make a data frame for orbit inputs\n",
-    "df = pd.DataFrame(obs_matrix, columns=[response_col] + x_cols)\n",
-    "# make some dummy date stamp\n",
-    "dt = pd.date_range(start='2016-01-04', periods=SERIES_LEN, freq=\"1W\")\n",
-    "df['date'] = dt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-25T01:00:59.577457Z",
-     "start_time": "2022-03-25T01:00:59.568215Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(365, 12)\n",
-      "          y        x1        x2        x3        x4        x5        x6  \\\n",
-      "0  4.426242  0.172792  0.000000  0.165219 -0.000000 -0.302926  0.617589   \n",
-      "1  5.580432  0.452678  0.223187 -0.000000  0.290559 -0.825977 -0.987437   \n",
-      "2  5.031773  0.182286  0.147066  0.014211  0.273356 -0.643615  0.764404   \n",
-      "3  3.264027 -0.368227 -0.081455 -0.241060  0.299423  0.391518 -0.422211   \n",
-      "4  5.246511  0.019861 -0.146228 -0.390954 -0.128596 -1.274526 -0.543362   \n",
-      "\n",
-      "         x7        x8        x9       x10       date  \n",
-      "0  1.378729 -0.498484  1.787853 -0.000874 2016-01-10  \n",
-      "1 -1.237446 -1.290934 -0.907493 -2.128289 2016-01-17  \n",
-      "2 -0.127634  0.652224 -0.217592 -0.973654 2016-01-24  \n",
-      "3 -1.219627  0.206127  0.055080 -2.220398 2016-01-31  \n",
-      "4 -1.604274  0.387340  0.203849 -1.064123 2016-02-07  \n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df.shape)\n",
-    "print(df.head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## WBIC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section, we use DLT model as an example. Different DLT models (the number of features used changes) are fitted and their WBIC values are calculated respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-25T01:05:42.822823Z",
-     "start_time": "2022-03-25T01:00:59.579560Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n",
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "INFO:orbit:Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WBIC value with 0 regressors: 1202.085\n",
-      "------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "INFO:orbit:Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WBIC value with 1 regressors: 1149.624\n",
-      "------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "INFO:orbit:Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WBIC value with 2 regressors: 1103.699\n",
-      "------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "INFO:orbit:Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WBIC value with 3 regressors: 1054.588\n",
-      "------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "INFO:orbit:Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WBIC value with 4 regressors: 1062.043\n",
-      "------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "INFO:orbit:Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WBIC value with 5 regressors: 1065.990\n",
-      "------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "INFO:orbit:Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WBIC value with 6 regressors: 1070.707\n",
-      "------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "INFO:orbit:Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WBIC value with 7 regressors: 1078.931\n",
-      "------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "INFO:orbit:Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 1000 and samples(per chain): 1000.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WBIC value with 8 regressors: 1085.957\n",
-      "------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WBIC value with 9 regressors: 1086.744\n",
-      "------------------------------------------------------------------\n",
-      "CPU times: user 1.46 s, sys: 1.1 s, total: 2.56 s\n",
-      "Wall time: 1min 45s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "WBIC_ls = []\n",
-    "for k in range(0, NUM_OF_REGRESSORS):\n",
-    "    regressor_col = x_cols[:k + 1]\n",
-    "    dlt_mod = DLT(\n",
-    "        response_col=response_col,\n",
-    "        date_col=dt_col,\n",
-    "        regressor_col=regressor_col,\n",
-    "        seed=2022,\n",
-    "        # fixing the smoothing parameters to learn regression coefficients more effectively\n",
-    "        level_sm_input=0.01,\n",
-    "        slope_sm_input=0.01,\n",
-    "        num_warmup=4000,\n",
-    "        num_sample=4000,\n",
-    "        )\n",
-    "    WBIC_temp = dlt_mod.fit_wbic(df=df) \n",
-    "    print(\"WBIC value with {:d} regressors: {:.3f}\".format(k, WBIC_temp))\n",
-    "    print('------------------------------------------------------------------')\n",
-    "    WBIC_ls.append(WBIC_temp)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is also interesting to see if WBIC can distinguish between model types; not just do feature selection for a given type of model. To that end the next block fits an LGT and ETS model to the data; the WBIC values for both models are then calculated.  \n",
-    "\n",
-    "Note that WBIC is supported for both  the 'stan-mcmc' and 'pyro-svi' estimators. Currently only the LGT model has both. Thus WBIC is calculated for GLT for both estimators. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-25T01:06:42.858826Z",
-     "start_time": "2022-03-25T01:05:42.825873Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 225 and samples(per chain): 25.\n",
-      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
-      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
-      "INFO:orbit:Using SVI (Pyro) with steps: 301, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
-      "INFO:root:Guessed max_plate_nesting = 2\n",
-      "INFO:orbit:step    0 loss = 308.1, scale = 0.11515\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WBIC value for LGT model (stan MCMC): 1138.472\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:step  100 loss = 116.48, scale = 0.50688\n",
-      "INFO:orbit:step  200 loss = 116.66, scale = 0.50021\n",
-      "INFO:orbit:step  300 loss = 116.68, scale = 0.51586\n",
-      "INFO:orbit:Sampling (PyStan) with chains: 4, cores: 8, temperature: 5.900, warmups (per chain): 225 and samples(per chain): 25.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WBIC value for LGT model (pyro SVI): 1126.767\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:pystan:n_eff / iter below 0.001 indicates that the effective sample size has likely been overestimated\n",
-      "WARNING:pystan:Rhat above 1.1 or below 0.9 indicates that the chains very likely have not mixed\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WBIC value for ETS model: 1199.142\n",
-      "CPU times: user 22.9 s, sys: 3.9 s, total: 26.8 s\n",
-      "Wall time: 26.6 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "lgt = LGT(response_col=response_col,\n",
-    "          date_col=dt_col,\n",
-    "          regressor_col=regressor_col,\n",
-    "          seasonality=52,\n",
-    "          estimator='stan-mcmc',\n",
-    "          seed=8888)\n",
-    "WBIC_lgt_mcmc = lgt.fit_wbic(df=df) \n",
-    "print(\"WBIC value for LGT model (stan MCMC): {:.3f}\".format( WBIC_lgt_mcmc))\n",
-    "\n",
-    "lgt = LGT(response_col=response_col,\n",
-    "          date_col=dt_col,\n",
-    "          regressor_col=regressor_col,\n",
-    "          seasonality=52,\n",
-    "          estimator='pyro-svi',\n",
-    "          seed=8888)\n",
-    "WBIC_lgt_pyro = lgt.fit_wbic(df=df) \n",
-    "print(\"WBIC value for LGT model (pyro SVI): {:.3f}\".format( WBIC_lgt_pyro))\n",
-    "\n",
-    "ets = ETS(\n",
-    "        response_col=response_col,\n",
-    "        date_col=dt_col,\n",
-    "        seed=2020,\n",
-    "        # fixing the smoothing parameters to learn regression coefficients more effectively\n",
-    "        level_sm_input=0.01,\n",
-    "    )\n",
-    "\n",
-    "WBIC_ets = ets.fit_wbic(df=df) \n",
-    "print(\"WBIC value for ETS model: {:.3f}\".format( WBIC_ets))\n",
-    "\n",
-    "WBIC_ls.append(WBIC_lgt_mcmc)\n",
-    "WBIC_ls.append(WBIC_lgt_pyro)\n",
-    "WBIC_ls.append(WBIC_ets)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The plot below shows the WBIC vs the number of features / model type (blue line). The true model is indicated by the vertical red line. The horizontal gray line shows the minimum (optimal) value. The minimum is at the true value. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-25T01:06:43.064656Z",
-     "start_time": "2022-03-25T01:06:42.861185Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 960x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "labels = [\"DLT_{}\".format(x) for x in range(1, NUM_OF_REGRESSORS + 1)] + ['LGT_MCMC', 'LGT_SVI','ETS']\n",
-    "fig, ax = plt.subplots(1, 1,figsize=(12, 6), dpi=80)\n",
-    "markerline, stemlines, baseline = ax.stem(\n",
-    "    np.arange(len(labels)), np.array(WBIC_ls), label='WBIC', markerfmt='D')\n",
-    "baseline.set_color('none')\n",
-    "markerline.set_markersize(12)\n",
-    "ax.set_ylim(1020, )\n",
-    "ax.set_xticks(np.arange(len(labels)))\n",
-    "ax.set_xticklabels(labels)\n",
-    "# because list type is mixed index from 1;\n",
-    "ax.axvline(x=NUM_OF_EFFECTIVE_REGRESSORS - 1, color='red', linewidth=3, alpha=0.5, linestyle='-', label='truth') \n",
-    "ax.set_ylabel(\"WBIC\")\n",
-    "ax.set_xlabel(\"# of Features / Model Type\")\n",
-    "ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## BIC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section, we use DLT model as an example. Different DLT models (the number of features used changes) are fitted and their BIC values are calculated respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-25T01:06:43.874445Z",
-     "start_time": "2022-03-25T01:06:43.069491Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BIC value with 1 regressors: 1247.445\n",
-      "------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BIC value with 2 regressors: 1191.890\n",
-      "------------------------------------------------------------------\n",
-      "BIC value with 3 regressors: 1139.408\n",
-      "------------------------------------------------------------------\n",
-      "BIC value with 4 regressors: 1079.648\n",
-      "------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BIC value with 5 regressors: 1082.445\n",
-      "------------------------------------------------------------------\n",
-      "BIC value with 6 regressors: 1082.294\n",
-      "------------------------------------------------------------------\n",
-      "BIC value with 7 regressors: 1079.913\n",
-      "------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n",
-      "INFO:orbit:Optimizing (PyStan) with algorithm: LBFGS.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BIC value with 8 regressors: 1079.903\n",
-      "------------------------------------------------------------------\n",
-      "BIC value with 9 regressors: 1078.984\n",
-      "------------------------------------------------------------------\n",
-      "BIC value with 10 regressors: 1186.574\n",
-      "------------------------------------------------------------------\n",
-      "CPU times: user 830 ms, sys: 1.17 s, total: 2 s\n",
-      "Wall time: 701 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "BIC_ls = []\n",
-    "for k in range(0, NUM_OF_REGRESSORS):\n",
-    "    regressor_col = x_cols[:k + 1]\n",
-    "    dlt_mod = DLT(\n",
-    "        estimator='stan-map',\n",
-    "        response_col=response_col,\n",
-    "        date_col=dt_col,\n",
-    "        regressor_col=regressor_col,\n",
-    "        seed=2022,\n",
-    "        # fixing the smoothing parameters to learn regression coefficients more effectively\n",
-    "        level_sm_input=0.01,\n",
-    "        slope_sm_input=0.01,\n",
-    "        )\n",
-    "    dlt_mod.fit(df=df)\n",
-    "    BIC_temp = dlt_mod.get_bic() \n",
-    "    print(\"BIC value with {:d} regressors: {:.3f}\".format(k + 1, BIC_temp))\n",
-    "    print('------------------------------------------------------------------')\n",
-    "    BIC_ls.append(BIC_temp)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The plot below shows the BIC vs the number of features (blue line). The true model is indicated by the vertical red line. The horizontal gray line shows the minimum (optimal) value. The minimum is at the true value. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-03-25T01:06:44.054469Z",
-     "start_time": "2022-03-25T01:06:43.876697Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 960x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "labels = [\"DLT_{}\".format(x) for x in range(1, NUM_OF_REGRESSORS + 1)]\n",
-    "fig, ax = plt.subplots(1, 1,figsize=(12, 6), dpi=80)\n",
-    "markerline, stemlines, baseline = ax.stem(\n",
-    "    np.arange(len(labels)), np.array(BIC_ls), label='BIC', markerfmt='D')\n",
-    "baseline.set_color('none')\n",
-    "markerline.set_markersize(12)\n",
-    "ax.set_ylim(1020, )\n",
-    "ax.set_xticks(np.arange(len(labels)))\n",
-    "ax.set_xticklabels(labels)\n",
-    "# because list type is mixed index from 1;\n",
-    "ax.axvline(x=NUM_OF_EFFECTIVE_REGRESSORS - 1, color='red', linewidth=3, alpha=0.5, linestyle='-', label='truth') \n",
-    "ax.set_ylabel(\"BIC\")\n",
-    "ax.set_xlabel(\"# of Features\")\n",
-    "ax.legend();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## References\n",
-    "\n",
-    "1. Watanabe Sumio (2010). \"Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory\". Journal of Machine Learning Research. 11: 3571–3594.\n",
-    "2. McElreath Richard (2015). \"Statistical Rethinking: A Bayesian course with examples in R and Stan\" Secound Ed. 193-221.\n",
-    "3. Vehtari Aki, Gelman Andrew, Gabry Jonah (2016) \"Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC\"\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/orbit/estimators/stan_estimator.py b/orbit/estimators/stan_estimator.py
index ce023fa0..dae83548 100644
--- a/orbit/estimators/stan_estimator.py
+++ b/orbit/estimators/stan_estimator.py
@@ -121,7 +121,7 @@ def fit(
         data_input.update({"T_STAR": sampling_temperature})
         if self.verbose:
             msg_template = (
-                "Sampling (PyStan) with chains: {:d}, cores: {:d}, temperature: {:.3f}, "
+                "Sampling (CmdStanPy) with chains: {:d}, cores: {:d}, temperature: {:.3f}, "
                 "warmups (per chain): {:d} and samples(per chain): {:d}."
             )
             msg = msg_template.format(
@@ -161,14 +161,7 @@ def fit(
 
 
 class StanEstimatorMAP(StanEstimator):
-    """Stan Estimator for MAP Posteriors
-
-    Parameters
-    ----------
-    stan_map_args : dict
-        Supplemental stan vi args to pass to PyStan.optimizing()
-
-    """
+    """Stan Estimator for MAP Posteriors"""
 
     def __init__(self, stan_map_args=None, **kwargs):
         super().__init__(**kwargs)
@@ -185,7 +178,7 @@ def _set_computed_stan_map_configs(self):
         self._stan_map_args = update_dict(default_stan_map_args, self._stan_map_args)
         # TODO: should it be within the fit block?
         if self.verbose:
-            msg_template = "Optimizing (PyStan) with algorithm: {}."
+            msg_template = "Optimizing (CmdStanPy) with algorithm: {}."
             if self.algorithm is None:
                 algorithm = "LBFGS"
             else:
diff --git a/orbit/template/model_template.py b/orbit/template/model_template.py
index 21c999d8..95e04cdc 100644
--- a/orbit/template/model_template.py
+++ b/orbit/template/model_template.py
@@ -41,9 +41,7 @@ def set_dynamic_attributes(self, df, training_meta):
         pass
 
     def set_init_values(self):
-        """Optional; set init as a callable (for Stan ONLY)
-        See: https://pystan.readthedocs.io/en/latest/api.htm
-        """
+        """Optional; set init as a callable (for Stan ONLY)"""
         pass
 
     def get_init_values(self):
diff --git a/orbit/utils/knots.py b/orbit/utils/knots.py
index 0bedd0cc..53f92787 100644
--- a/orbit/utils/knots.py
+++ b/orbit/utils/knots.py
@@ -109,7 +109,7 @@ def get_knot_idx(
             ]
         )
 
-        time_delta = date_array.diff().mean()
+        time_delta = np.diff(date_array).mean()
 
         knot_idx = get_dates_delta(
             start_date=date_array[0], end_date=_knot_dates, time_delta=time_delta