Generate Python docs from pytorch/pytorch@bcee215

Author: pytorchbot

docs/master/_images/RReLU.png

@@ -217,7 +217,7 @@
           <div class="pytorch-left-menu-search">
 
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>master (1.12.0a0+git48ea440 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.12.0a0+gitbcee215 ) &#x25BC</a>
     </div>
 
 

docs/master/_modules/index.html

@@ -217,7 +217,7 @@
           <div class="pytorch-left-menu-search">
 
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>master (1.12.0a0+git48ea440 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.12.0a0+gitbcee215 ) &#x25BC</a>
     </div>
 
 

docs/master/_modules/torch.html

@@ -217,7 +217,7 @@
           <div class="pytorch-left-menu-search">
 
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>master (1.12.0a0+git48ea440 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.12.0a0+gitbcee215 ) &#x25BC</a>
     </div>
 
 

docs/master/_modules/torch/__config__.html

@@ -217,7 +217,7 @@
           <div class="pytorch-left-menu-search">
 
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>master (1.12.0a0+git48ea440 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.12.0a0+gitbcee215 ) &#x25BC</a>
     </div>
 
 

docs/master/_modules/torch/_jit_internal.html

@@ -217,7 +217,7 @@
           <div class="pytorch-left-menu-search">
 
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>master (1.12.0a0+git48ea440 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.12.0a0+gitbcee215 ) &#x25BC</a>
     </div>
 
 

docs/master/_modules/torch/_lobpcg.html

@@ -217,7 +217,7 @@
           <div class="pytorch-left-menu-search">
 
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>master (1.12.0a0+git48ea440 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.12.0a0+gitbcee215 ) &#x25BC</a>
     </div>
 
 

docs/master/_modules/torch/_lowrank.html

@@ -217,7 +217,7 @@
           <div class="pytorch-left-menu-search">
 
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>master (1.12.0a0+git48ea440 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.12.0a0+gitbcee215 ) &#x25BC</a>
     </div>
 
 
@@ -519,7 +519,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
             <span class="c1"># doesn&#39;t work because of</span>
             <span class="c1"># https://github.com/pytorch/pytorch/issues/47442</span>
             <span class="c1"># Update the test in test_serialization if you remove &#39;meta&#39; from here</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_sparse</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;lazy&#39;</span><span class="p">,</span> <span class="s1">&#39;xla&#39;</span><span class="p">,</span> <span class="s1">&#39;mlc&#39;</span><span class="p">,</span> <span class="s1">&#39;ort&#39;</span><span class="p">,</span> <span class="s1">&#39;meta&#39;</span><span class="p">,</span> <span class="s1">&#39;hpu&#39;</span><span class="p">]</span> <span class="ow">or</span> \
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_sparse</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;lazy&#39;</span><span class="p">,</span> <span class="s1">&#39;xla&#39;</span><span class="p">,</span> <span class="s1">&#39;mps&#39;</span><span class="p">,</span> <span class="s1">&#39;ort&#39;</span><span class="p">,</span> <span class="s1">&#39;meta&#39;</span><span class="p">,</span> <span class="s1">&#39;hpu&#39;</span><span class="p">]</span> <span class="ow">or</span> \
                     <span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">Tensor</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
                 <span class="n">new_tensor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
                 <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">new_tensor</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -635,7 +635,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
         <span class="c1"># See Note [Don&#39;t serialize hooks]</span>
         <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">hooks</span><span class="o">.</span><span class="n">warn_if_has_hooks</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
         <span class="n">backward_hooks</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
-        <span class="c1"># Note: Numpy array is chosen to be the rebuild component for XLA, ORT, MLC Tensors.</span>
+        <span class="c1"># Note: Numpy array is chosen to be the rebuild component for XLA, ORT Tensors.</span>
         <span class="c1"># We considered a few options:</span>
         <span class="c1"># 1. CPU tensor can&#39;t be used here.</span>
         <span class="c1">#    Otherwise in torch.load CPU storage is reconstructed with randomly</span>
@@ -645,7 +645,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
         <span class="c1"># 2. Python list is not a good fit due to performance reason.</span>
         <span class="c1">#    `tolist()` converts every single element in the tensor into python objects</span>
         <span class="c1">#    and serialize them one by one.</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;xla&#39;</span><span class="p">,</span> <span class="s1">&#39;ort&#39;</span><span class="p">,</span> <span class="s1">&#39;mlc&#39;</span><span class="p">,</span> <span class="s1">&#39;hpu&#39;</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;xla&#39;</span><span class="p">,</span> <span class="s1">&#39;ort&#39;</span><span class="p">,</span> <span class="s1">&#39;mps&#39;</span><span class="p">,</span> <span class="s1">&#39;hpu&#39;</span><span class="p">]:</span>
             <span class="k">return</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_utils</span><span class="o">.</span><span class="n">_rebuild_device_tensor_from_numpy</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span>
                                                                      <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
                                                                      <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">),</span>
@@ -756,11 +756,12 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
         <span class="c1"># See Note [Don&#39;t serialize hooks]</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_backward_hooks</span> <span class="o">=</span> <span class="n">state</span>
 
-    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">tensor_contents</span><span class="o">=</span><span class="kc">None</span><span class="p">):</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="fm">__repr__</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="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="fm">__repr__</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">tensor_contents</span><span class="o">=</span><span class="n">tensor_contents</span><span class="p">)</span>
         <span class="c1"># All strings are unicode in Python 3.</span>
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_tensor_str</span><span class="o">.</span><span class="n">_str</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_tensor_str</span><span class="o">.</span><span class="n">_str</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tensor_contents</span><span class="o">=</span><span class="n">tensor_contents</span><span class="p">)</span>
 
 <div class="viewcode-block" id="Tensor.backward"><a class="viewcode-back" href="../../generated/torch.Tensor.backward.html#torch.Tensor.backward">[docs]</a>    <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">create_graph</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">inputs</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;Computes the gradient of current tensor w.r.t. graph leaves.</span>
@@ -949,11 +950,11 @@ <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>