Generate Python docs from pytorch/pytorch@f690a55

Author: pytorchbot

docs/master/_images/RReLU.png

@@ -866,7 +866,7 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
         <span class="s2">&quot;if this method is not scripted, copy the python method onto the scripted model&quot;</span>
 
 
-<span class="k">def</span> <span class="nf">export</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
+<div class="viewcode-block" id="export"><a class="viewcode-back" href="../../jit.html#torch.jit.export">[docs]</a><span class="k">def</span> <span class="nf">export</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    This decorator indicates that a method on an ``nn.Module`` is used as an entry point into a</span>
 <span class="sd">    :class:`ScriptModule` and should be compiled.</span>
@@ -909,10 +909,10 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
 <span class="sd">        m = torch.jit.script(MyModule())</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">fn</span><span class="o">.</span><span class="n">_torchscript_modifier</span> <span class="o">=</span> <span class="n">FunctionModifiers</span><span class="o">.</span><span class="n">EXPORT</span>
-    <span class="k">return</span> <span class="n">fn</span>
+    <span class="k">return</span> <span class="n">fn</span></div>
 
 
-<div class="viewcode-block" id="unused"><a class="viewcode-back" href="../../generated/torch.jit.unused.html#torch.jit.unused">[docs]</a><span class="k">def</span> <span class="nf">unused</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">unused</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    This decorator indicates to the compiler that a function or method should</span>
 <span class="sd">    be ignored and replaced with the raising of an exception. This allows you</span>
@@ -959,7 +959,7 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">prop</span>
 
     <span class="n">fn</span><span class="o">.</span><span class="n">_torchscript_modifier</span> <span class="o">=</span> <span class="n">FunctionModifiers</span><span class="o">.</span><span class="n">UNUSED</span>
-    <span class="k">return</span> <span class="n">fn</span></div>
+    <span class="k">return</span> <span class="n">fn</span>
 
 <span class="c1"># No op context manager from python side</span>
 <span class="k">class</span> <span class="nc">_IgnoreContextManager</span><span class="p">(</span><span class="n">contextlib</span><span class="o">.</span><span class="n">AbstractContextManager</span><span class="p">):</span>
@@ -969,7 +969,7 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
     <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">traceback</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">pass</span>
 
-<div class="viewcode-block" id="ignore"><a class="viewcode-back" href="../../generated/torch.jit.ignore.html#torch.jit.ignore">[docs]</a><span class="k">def</span> <span class="nf">ignore</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">ignore</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    This decorator indicates to the compiler that a function or method should</span>
 <span class="sd">    be ignored and left as a Python function. This allows you to leave code in</span>
@@ -1060,7 +1060,7 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">fn</span><span class="o">.</span><span class="n">_torchscript_modifier</span> <span class="o">=</span> <span class="n">FunctionModifiers</span><span class="o">.</span><span class="n">IGNORE</span>
         <span class="k">return</span> <span class="n">fn</span>
-    <span class="k">return</span> <span class="n">decorator</span></div>
+    <span class="k">return</span> <span class="n">decorator</span>
 
 
 <span class="k">def</span> <span class="nf">_copy_to_script_wrapper</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
@@ -1358,7 +1358,7 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
     <span class="nb">globals</span><span class="p">()[</span><span class="sa">f</span><span class="s2">&quot;BroadcastingList</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">BroadcastingList1</span>
 
 
-<span class="k">def</span> <span class="nf">is_scripting</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<div class="viewcode-block" id="is_scripting"><a class="viewcode-back" href="../../jit_language_reference.html#torch.jit.is_scripting">[docs]</a><span class="k">def</span> <span class="nf">is_scripting</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Function that returns True when in compilation and False otherwise. This</span>
 <span class="sd">    is useful especially with the @unused decorator to leave code in your</span>
@@ -1377,7 +1377,7 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
 <span class="sd">           else:</span>
 <span class="sd">              return unsupported_linear_op(x)</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="kc">False</span>
+    <span class="k">return</span> <span class="kc">False</span></div>
 
 
 <span class="c1"># Retrieves a fully-qualified name (module hierarchy + classname) for a given obj.</span>

docs/master/_modules/torch/_jit_internal.html

@@ -931,11 +931,11 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
 
-<div class="viewcode-block" id="Tensor.norm"><a class="viewcode-back" href="../../generated/torch.Tensor.norm.html#torch.Tensor.norm">[docs]</a>    <span class="k">def</span> <span class="nf">norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="s2">&quot;fro&quot;</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="s2">&quot;fro&quot;</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dtype</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;See :func:`torch.norm`&quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="n">has_torch_function_unary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
             <span class="k">return</span> <span class="n">handle_torch_function</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">norm</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="p">,),</span> <span class="bp">self</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="n">keepdim</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">keepdim</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span></div>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">keepdim</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
 
     <span class="k">def</span> <span class="nf">lu</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pivot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">get_infos</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
         <span class="sa">r</span><span class="sd">&quot;&quot;&quot;See :func:`torch.lu`&quot;&quot;&quot;</span>
@@ -1471,20 +1471,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
 <span class="sd">            25</span>
 
 <span class="sd">       &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_sparse</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_sparse_csr</span><span class="p">:</span>
-            <span class="n">crow_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">crow_indices</span><span class="p">()</span>
-            <span class="n">col_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">col_indices</span><span class="p">()</span>
-            <span class="n">indices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_convert_indices_from_csr_to_coo</span><span class="p">(</span><span class="n">crow_indices</span><span class="p">,</span> <span class="n">col_indices</span><span class="p">,</span>
-                                                             <span class="n">out_int32</span><span class="o">=</span><span class="n">crow_indices</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sparse_coo_tensor</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span>
-                                           <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span>
-                                           <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
-                                           <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
-                                           <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">to_sparse</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">to_sparse</span><span class="p">()</span>
 
     <span class="k">def</span> <span class="nf">to_sparse_csr</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot; Convert a tensor to compressed row storage format. Only works with 2D tensors.</span>