Fix gap

soulitzer · soulitzer · commit a5a3c24f0e0e · 2021-08-12T15:10:58.000-04:00
diff --git a/index.rst b/index.rst
@@ -670,11 +670,8 @@ Additional Resources
    :hidden:
    :caption: Extending PyTorch
 
-<<<<<<< HEAD
-   intermediate/custom_function_conv_bn_tutorial
-=======
    intermediate/custom_function_double_backward
->>>>>>> 4749c7db6... Add images
+   intermediate/custom_function_conv_bn_tutorial
    advanced/cpp_extension
    advanced/torch_script_custom_ops
    advanced/torch_script_custom_classes
diff --git a/intermediate_source/custom_function_double_backward_tutorial.py b/intermediate_source/custom_function_double_backward_tutorial.py
@@ -31,7 +31,7 @@
 # we can explore a couple examples:
 
 ######################################################################
-# Saving the inputs
+# Saving the Inputs
 # -------------------------------------------------------------------
 # Consider this simple squaring function. It saves an input tensor
 # for backward. Double backward works automatically when autograd
@@ -168,7 +168,7 @@ def sinh(x):
 
 ######################################################################
 # Use torchviz to visualize the graph:
-
+#
 # .. code-block:: python
 #
 #    out = sinh(x)
@@ -205,7 +205,7 @@ def backward(ctx, grad_out):
 ######################################################################
 # Use torchviz to visualize the graph. Notice that `grad_x` is not
 # part of the graph!
-
+#
 # .. code-block:: python
 #
 #    out = SinhBad.apply(x)
@@ -216,7 +216,7 @@ def backward(ctx, grad_out):
 #    :width: 232
 
 ######################################################################
-# When Backward is not able to be Tracked by Autograd
+# When Backward is not Tracked
 # -------------------------------------------------------------------
 # Finally, let's consider an example when it may not be possible for
 # autograd to track gradients for a functions backward at all.
@@ -269,7 +269,7 @@ def backward(ctx, grad_out):
 
 ######################################################################
 # Use torchviz to visualize the graph:
-
+#
 # .. code-block:: python
 #
 #    out = Cube.apply(x)
