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docs/master/_modules/torch/nn/parallel/data_parallel.html

@@ -373,7 +373,7 @@ <h1>Source code for torch.nn.parallel.data_parallel</h1><div class="highlight"><
         <span class="k">return</span>
 
 
-<div class="viewcode-block" id="DataParallel"><a class="viewcode-back" href="../../../../generated/torch.nn.DataParallel.html#torch.nn.DataParallel">[docs]</a><span class="k">class</span> <span class="nc">DataParallel</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
+<span class="k">class</span> <span class="nc">DataParallel</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Implements data parallelism at the module level.</span>
 
 <span class="sd">    This container parallelizes the application of the given :attr:`module` by</span>
@@ -505,10 +505,10 @@ <h1>Source code for torch.nn.parallel.data_parallel</h1><div class="highlight"><
         <span class="k">return</span> <span class="n">parallel_apply</span><span class="p">(</span><span class="n">replicas</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">device_ids</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">replicas</span><span class="p">)])</span>
 
     <span class="k">def</span> <span class="nf">gather</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">output_device</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">gather</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">output_device</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">)</span></div>
+        <span class="k">return</span> <span class="n">gather</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">output_device</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">)</span>
 
 
-<span class="k">def</span> <span class="nf">data_parallel</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">device_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">output_device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">module_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<div class="viewcode-block" id="data_parallel"><a class="viewcode-back" href="../../../../nn.functional.html#torch.nn.parallel.data_parallel">[docs]</a><span class="k">def</span> <span class="nf">data_parallel</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">device_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">output_device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">module_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Evaluates module(input) in parallel across the GPUs given in device_ids.</span>
 
 <span class="sd">    This is the functional version of the DataParallel module.</span>
@@ -548,7 +548,7 @@ <h1>Source code for torch.nn.parallel.data_parallel</h1><div class="highlight"><
     <span class="n">used_device_ids</span> <span class="o">=</span> <span class="n">device_ids</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)]</span>
     <span class="n">replicas</span> <span class="o">=</span> <span class="n">replicate</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">used_device_ids</span><span class="p">)</span>
     <span class="n">outputs</span> <span class="o">=</span> <span class="n">parallel_apply</span><span class="p">(</span><span class="n">replicas</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">module_kwargs</span><span class="p">,</span> <span class="n">used_device_ids</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">gather</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">output_device</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">gather</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">output_device</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span></div>
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