Generate Python docs from pytorch/pytorch@ff47dcd

Author: pytorchbot

docs/master/__config__.html

@@ -194,7 +194,7 @@
           <div class="pytorch-left-menu-search">
 
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>master (1.11.0a0+gitef07820 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.11.0a0+gitff47dcd ) &#x25BC</a>
     </div>
 
 

docs/master/_images/RReLU.png

@@ -193,7 +193,7 @@
           <div class="pytorch-left-menu-search">
 
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>master (1.11.0a0+gitef07820 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.11.0a0+gitff47dcd ) &#x25BC</a>
     </div>
 
 
@@ -518,6 +518,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="torch/distributions/utils.html">torch.distributions.utils</a></li>
 <li><a href="torch/distributions/von_mises.html">torch.distributions.von_mises</a></li>
 <li><a href="torch/distributions/weibull.html">torch.distributions.weibull</a></li>
+<li><a href="torch/distributions/wishart.html">torch.distributions.wishart</a></li>
 <li><a href="torch/functional.html">torch.functional</a></li>
 <li><a href="torch/futures.html">torch.futures</a></li>
 <li><a href="torch/fx/_symbolic_trace.html">torch.fx._symbolic_trace</a></li>
@@ -628,6 +629,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html">torch.utils.benchmark.utils.valgrind_wrapper.timer_interface</a></li>
 <li><a href="torch/utils/checkpoint.html">torch.utils.checkpoint</a></li>
 <li><a href="torch/utils/cpp_extension.html">torch.utils.cpp_extension</a></li>
+<li><a href="torch/utils/data/_utils/collate.html">torch.utils.data._utils.collate</a></li>
 <li><a href="torch/utils/data/_utils/worker.html">torch.utils.data._utils.worker</a></li>
 <li><a href="torch/utils/data/dataloader.html">torch.utils.data.dataloader</a></li>
 <li><a href="torch/utils/data/dataset.html">torch.utils.data.dataset</a></li>

docs/master/_modules/index.html

@@ -193,7 +193,7 @@
           <div class="pytorch-left-menu-search">
 
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>master (1.11.0a0+gitef07820 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.11.0a0+gitff47dcd ) &#x25BC</a>
     </div>
 
 

docs/master/_modules/torch.html

@@ -193,7 +193,7 @@
           <div class="pytorch-left-menu-search">
 
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>master (1.11.0a0+gitef07820 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.11.0a0+gitff47dcd ) &#x25BC</a>
     </div>
 
 
@@ -856,7 +856,7 @@ <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>
@@ -899,10 +899,10 @@ <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>
@@ -949,7 +949,7 @@ <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>
@@ -959,7 +959,7 @@ <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>
@@ -1050,7 +1050,7 @@ <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>
@@ -1348,7 +1348,7 @@ <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>
@@ -1367,7 +1367,7 @@ <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/__config__.html

@@ -193,7 +193,7 @@
           <div class="pytorch-left-menu-search">
 
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>master (1.11.0a0+gitef07820 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.11.0a0+gitff47dcd ) &#x25BC</a>
     </div>
 
 

docs/master/_modules/torch/_jit_internal.html

@@ -193,7 +193,7 @@
           <div class="pytorch-left-menu-search">
 
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>master (1.11.0a0+gitef07820 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.11.0a0+gitff47dcd ) &#x25BC</a>
     </div>
 
 
@@ -476,7 +476,7 @@ <h1>Source code for torch._lowrank</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">Q</span>
 
 
-<div class="viewcode-block" id="svd_lowrank"><a class="viewcode-back" href="../../generated/torch.svd_lowrank.html#torch.svd_lowrank">[docs]</a><span class="k">def</span> <span class="nf">svd_lowrank</span><span class="p">(</span><span class="n">A</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">q</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="mi">6</span><span class="p">,</span> <span class="n">niter</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+<span class="k">def</span> <span class="nf">svd_lowrank</span><span class="p">(</span><span class="n">A</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">q</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="mi">6</span><span class="p">,</span> <span class="n">niter</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
                 <span class="n">M</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">]:</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Return the singular value decomposition ``(U, S, V)`` of a matrix,</span>
 <span class="sd">    batches of matrices, or a sparse matrix :math:`A` such that</span>
@@ -521,7 +521,7 @@ <h1>Source code for torch._lowrank</h1><div class="highlight"><pre>
         <span class="n">tensor_ops</span> <span class="o">=</span> <span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">M</span><span class="p">)</span>
         <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="nb">set</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">type</span><span class="p">,</span> <span class="n">tensor_ops</span><span class="p">))</span><span class="o">.</span><span class="n">issubset</span><span class="p">((</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="kc">None</span><span class="p">)))</span> <span class="ow">and</span> <span class="n">has_torch_function</span><span class="p">(</span><span class="n">tensor_ops</span><span class="p">)):</span>
             <span class="k">return</span> <span class="n">handle_torch_function</span><span class="p">(</span><span class="n">svd_lowrank</span><span class="p">,</span> <span class="n">tensor_ops</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="n">q</span><span class="p">,</span> <span class="n">niter</span><span class="o">=</span><span class="n">niter</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="n">M</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">_svd_lowrank</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="n">q</span><span class="p">,</span> <span class="n">niter</span><span class="o">=</span><span class="n">niter</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="n">M</span><span class="p">)</span></div>
+    <span class="k">return</span> <span class="n">_svd_lowrank</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="n">q</span><span class="p">,</span> <span class="n">niter</span><span class="o">=</span><span class="n">niter</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="n">M</span><span class="p">)</span>
 
 
 <span class="k">def</span> <span class="nf">_svd_lowrank</span><span class="p">(</span><span class="n">A</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">q</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="mi">6</span><span class="p">,</span> <span class="n">niter</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
@@ -571,7 +571,7 @@ <h1>Source code for torch._lowrank</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">U</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">V</span>
 
 
-<span class="k">def</span> <span class="nf">pca_lowrank</span><span class="p">(</span><span class="n">A</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">q</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">center</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<div class="viewcode-block" id="pca_lowrank"><a class="viewcode-back" href="../../generated/torch.pca_lowrank.html#torch.pca_lowrank">[docs]</a><span class="k">def</span> <span class="nf">pca_lowrank</span><span class="p">(</span><span class="n">A</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">q</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">center</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
                 <span class="n">niter</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">]:</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Performs linear Principal Component Analysis (PCA) on a low-rank</span>
 <span class="sd">    matrix, batches of such matrices, or sparse matrix.</span>
@@ -672,7 +672,7 @@ <h1>Source code for torch._lowrank</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">_svd_lowrank</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">niter</span><span class="o">=</span><span class="n">niter</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="n">M</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">C</span> <span class="o">=</span> <span class="n">A</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,),</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">_svd_lowrank</span><span class="p">(</span><span class="n">A</span> <span class="o">-</span> <span class="n">C</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">niter</span><span class="o">=</span><span class="n">niter</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">_svd_lowrank</span><span class="p">(</span><span class="n">A</span> <span class="o">-</span> <span class="n">C</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">niter</span><span class="o">=</span><span class="n">niter</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></div>
 </pre></div>
 
              </article>