Generate Python docs from pytorch/pytorch@ff32f65

Author: pytorchbot

docs/master/__config__.html

@@ -188,7 +188,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ffe1744 &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ff32f65 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/index.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ffe1744 &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ff32f65 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ffe1744 &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ff32f65 &#x25BC</a>
                 </div>
 
 
@@ -645,7 +645,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span> <span class="ow">in</span> <span class="n">_storage_classes</span></div>
 
 
-<span class="k">def</span> <span class="nf">set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
+<div class="viewcode-block" id="set_default_tensor_type"><a class="viewcode-back" href="../generated/torch.set_default_tensor_type.html#torch.set_default_tensor_type">[docs]</a><span class="k">def</span> <span class="nf">set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Sets the default ``torch.Tensor`` type to floating point tensor type</span>
 <span class="sd">    ``t``. This type will also be used as default floating point type for</span>
 <span class="sd">    type inference in :func:`torch.tensor`.</span>
@@ -666,10 +666,10 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">_string_classes</span><span class="p">):</span>
         <span class="n">t</span> <span class="o">=</span> <span class="n">_import_dotted_name</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
-    <span class="n">_C</span><span class="o">.</span><span class="n">_set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+    <span class="n">_C</span><span class="o">.</span><span class="n">_set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span></div>
 
 
-<span class="k">def</span> <span class="nf">set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
+<div class="viewcode-block" id="set_default_dtype"><a class="viewcode-back" href="../generated/torch.set_default_dtype.html#torch.set_default_dtype">[docs]</a><span class="k">def</span> <span class="nf">set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Sets the default floating point dtype to :attr:`d`.</span>
 <span class="sd">    This dtype is:</span>
 
@@ -697,9 +697,9 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="sd">        torch.complex128</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">_C</span><span class="o">.</span><span class="n">_set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+    <span class="n">_C</span><span class="o">.</span><span class="n">_set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">)</span></div>
 
-<span class="k">def</span> <span class="nf">set_deterministic</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
+<div class="viewcode-block" id="set_deterministic"><a class="viewcode-back" href="../generated/torch.set_deterministic.html#torch.set_deterministic">[docs]</a><span class="k">def</span> <span class="nf">set_deterministic</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot; Sets whether PyTorch operations must use &quot;deterministic&quot;</span>
 <span class="sd">    algorithms. That is, algorithms which, given the same input, and when</span>
 <span class="sd">    run on the same software and hardware, always produce the same output.</span>
@@ -774,7 +774,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="sd">        d (:class:`bool`): If True, force operations to be deterministic.</span>
 <span class="sd">                           If False, allow non-deterministic operations.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">_C</span><span class="o">.</span><span class="n">_set_deterministic</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+    <span class="n">_C</span><span class="o">.</span><span class="n">_set_deterministic</span><span class="p">(</span><span class="n">d</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="is_deterministic"><a class="viewcode-back" href="../generated/torch.is_deterministic.html#torch.is_deterministic">[docs]</a><span class="k">def</span> <span class="nf">is_deterministic</span><span class="p">():</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns True if the global deterministic flag is turned on. Refer to</span>
@@ -926,14 +926,14 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="c1">################################################################################</span>
 
 <span class="c1"># needs to be before the submodule imports to avoid circular dependencies</span>
-<div class="viewcode-block" id="_assert"><a class="viewcode-back" href="../generated/torch._assert.html#torch._assert">[docs]</a><span class="k">def</span> <span class="nf">_assert</span><span class="p">(</span><span class="n">condition</span><span class="p">,</span> <span class="n">message</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">_assert</span><span class="p">(</span><span class="n">condition</span><span class="p">,</span> <span class="n">message</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;A wrapper around Python&#39;s assert which is symbolically traceable.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="kn">from</span> <span class="nn">.overrides</span> <span class="kn">import</span> <span class="n">has_torch_function</span><span class="p">,</span> <span class="n">handle_torch_function</span>
 
     <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">condition</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="ow">and</span> <span class="n">has_torch_function</span><span class="p">((</span><span class="n">condition</span><span class="p">,)):</span>
         <span class="k">return</span> <span class="n">handle_torch_function</span><span class="p">(</span><span class="n">_assert</span><span class="p">,</span> <span class="p">(</span><span class="n">condition</span><span class="p">,),</span> <span class="n">condition</span><span class="p">,</span> <span class="n">message</span><span class="p">)</span>
-    <span class="k">assert</span> <span class="n">condition</span><span class="p">,</span> <span class="n">message</span></div>
+    <span class="k">assert</span> <span class="n">condition</span><span class="p">,</span> <span class="n">message</span>
 
 <span class="c1">################################################################################</span>
 <span class="c1"># Import most common subpackages</span>
@@ -976,9 +976,9 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="k">del</span> <span class="n">_torch_docs</span><span class="p">,</span> <span class="n">_tensor_docs</span><span class="p">,</span> <span class="n">_storage_docs</span>
 
 
-<span class="k">def</span> <span class="nf">compiled_with_cxx11_abi</span><span class="p">():</span>
+<div class="viewcode-block" id="compiled_with_cxx11_abi"><a class="viewcode-back" href="../generated/torch.compiled_with_cxx11_abi.html#torch.compiled_with_cxx11_abi">[docs]</a><span class="k">def</span> <span class="nf">compiled_with_cxx11_abi</span><span class="p">():</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns whether PyTorch was built with _GLIBCXX_USE_CXX11_ABI=1&quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_GLIBCXX_USE_CXX11_ABI</span>
+    <span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_GLIBCXX_USE_CXX11_ABI</span></div>
 
 
 <span class="c1"># Import the ops &quot;namespace&quot;</span>

docs/master/_modules/torch/__config__.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ffe1744 &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ff32f65 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_jit_internal.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ffe1744 &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ff32f65 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_lobpcg.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ffe1744 &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ff32f65 &#x25BC</a>
                 </div>
 
 
@@ -638,7 +638,7 @@ <h1>Source code for torch._lobpcg</h1><div class="highlight"><pre>
 <span class="k">class</span> <span class="nc">LOBPCGAutogradFunction</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span>
 
     <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span>  <span class="c1"># type: ignore[override]</span>
                 <span class="n">A</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span>
                 <span class="n">k</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">B</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>
@@ -982,7 +982,7 @@ <h1>Source code for torch._lobpcg</h1><div class="highlight"><pre>
         <span class="n">bparams</span><span class="p">[</span><span class="s1">&#39;ortho_use_drop&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">bparams</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;ortho_use_drop&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
 
     <span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">is_scripting</span><span class="p">():</span>
-        <span class="n">LOBPCG</span><span class="o">.</span><span class="n">call_tracker</span> <span class="o">=</span> <span class="n">LOBPCG_call_tracker</span>
+        <span class="n">LOBPCG</span><span class="o">.</span><span class="n">call_tracker</span> <span class="o">=</span> <span class="n">LOBPCG_call_tracker</span>  <span class="c1"># type: ignore</span>
 
     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
         <span class="n">N</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">prod</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="n">A</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">])))</span>
@@ -1004,7 +1004,7 @@ <h1>Source code for torch._lobpcg</h1><div class="highlight"><pre>
             <span class="n">bXret</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">worker</span><span class="o">.</span><span class="n">X</span><span class="p">[:,</span> <span class="p">:</span><span class="n">k</span><span class="p">]</span>
 
         <span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">is_scripting</span><span class="p">():</span>
-            <span class="n">LOBPCG</span><span class="o">.</span><span class="n">call_tracker</span> <span class="o">=</span> <span class="n">LOBPCG_call_tracker_orig</span>
+            <span class="n">LOBPCG</span><span class="o">.</span><span class="n">call_tracker</span> <span class="o">=</span> <span class="n">LOBPCG_call_tracker_orig</span>  <span class="c1"># type: ignore</span>
 
         <span class="k">return</span> <span class="n">bE</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">k</span><span class="p">,)),</span> <span class="n">bXret</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">k</span><span class="p">))</span>
 
@@ -1016,7 +1016,7 @@ <h1>Source code for torch._lobpcg</h1><div class="highlight"><pre>
     <span class="n">worker</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
 
     <span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">is_scripting</span><span class="p">():</span>
-        <span class="n">LOBPCG</span><span class="o">.</span><span class="n">call_tracker</span> <span class="o">=</span> <span class="n">LOBPCG_call_tracker_orig</span>
+        <span class="n">LOBPCG</span><span class="o">.</span><span class="n">call_tracker</span> <span class="o">=</span> <span class="n">LOBPCG_call_tracker_orig</span>  <span class="c1"># type: ignore</span>
 
     <span class="k">return</span> <span class="n">worker</span><span class="o">.</span><span class="n">E</span><span class="p">[:</span><span class="n">k</span><span class="p">],</span> <span class="n">worker</span><span class="o">.</span><span class="n">X</span><span class="p">[:,</span> <span class="p">:</span><span class="n">k</span><span class="p">]</span>
 

docs/master/_modules/torch/_lowrank.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ffe1744 &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ff32f65 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_tensor_str.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ffe1744 &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ff32f65 &#x25BC</a>
                 </div>
 
 

docs/master/_modules/torch/_utils.html

@@ -187,7 +187,7 @@
 
 
                 <div class="version">
-                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ffe1744 &#x25BC</a>
+                  <a href='http://pytorch.org/docs/versions.html'>1.8.0a0+ff32f65 &#x25BC</a>
                 </div>
 
 
@@ -383,6 +383,7 @@ <h1>Source code for torch._utils</h1><div class="highlight"><pre>
 <span class="kn">import</span> <span class="nn">traceback</span>
 
 
+
 <span class="k">def</span> <span class="nf">_type</span><span class="p">(</span><span class="bp">self</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="n">non_blocking</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;Returns the type if `dtype` is not provided, else casts this object to</span>
 <span class="sd">    the specified type.</span>
@@ -867,6 +868,15 @@ <h1>Source code for torch._utils</h1><div class="highlight"><pre>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Expected a torch.device with a specified index &#39;</span>
                              <span class="s1">&#39;or an integer, but got:</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">device</span><span class="p">))</span>
     <span class="k">return</span> <span class="n">device_idx</span>
+
+
+<span class="k">def</span> <span class="nf">_handle_complex</span><span class="p">(</span><span class="n">tensor</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns a real view of a tensor if complex dtype else just the tensor</span>
+<span class="sd">    need to check if a UninitializedParameter because otherwise checking is_complex is an error for a LazyModule</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">view_as_real</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span>
+                                                        <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">UninitializedParameter</span><span class="p">)</span> <span class="ow">and</span> <span class="n">tensor</span><span class="o">.</span><span class="n">is_complex</span><span class="p">()</span> <span class="k">else</span> <span class="n">tensor</span>
 </pre></div>
 
              </article>