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diff --git a/‎master/_downloads/85b8fb55326bd48ee7f24d0d0091c7c1/plot_outlier_detection_with_COOT_and_unbalanced_COOT.zip
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@@ -100,7 +100,7 @@ <h1>Source code for ot.unbalanced._lbfgs</h1><div class="highlight"><pre>
 
 
 <span class="k">def</span><span class="w"> </span><span class="nf">_get_loss_unbalanced</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span> <span class="n">reg</span><span class="p">,</span> <span class="n">reg_m1</span><span class="p">,</span> <span class="n">reg_m2</span><span class="p">,</span> <span class="n">reg_div</span><span class="o">=</span><span class="s2">&quot;kl&quot;</span><span class="p">,</span> <span class="n">regm_div</span><span class="o">=</span><span class="s2">&quot;kl&quot;</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Return loss function for the L-BFGS-B solver</span>
 
 <span class="sd">    .. note:: This function will be fed into scipy.optimize, so all input arrays must be Numpy arrays.</span>