Generate Python docs from pytorch/pytorch@064a650

Author: pytorchbot

docs/main/_images/RReLU.png

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@@ -2351,8 +2351,8 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">_C</span><span class="o">.</span><span class="n">_log_api_usage_once</span><span class="p">(</span><span class="s2">&quot;torch.compile&quot;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">12</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Dynamo is not supported on Python 3.12+&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">13</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Dynamo is not supported on Python 3.13+&quot;</span><span class="p">)</span>
 
     <span class="c1"># Decorator mode</span>
     <span class="k">if</span> <span class="n">model</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>

docs/main/_modules/torch.html

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docs/main/_modules/torch/__config__.html

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docs/main/_modules/torch/__future__.html

@@ -242,7 +242,7 @@
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@@ -528,7 +528,6 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
 <span class="kn">from</span> <span class="nn">torch._awaits</span> <span class="kn">import</span> <span class="n">_Await</span>
 <span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="n">_Await</span> <span class="k">as</span> <span class="n">CAwait</span><span class="p">,</span> <span class="n">Future</span> <span class="k">as</span> <span class="n">CFuture</span>
 <span class="kn">from</span> <span class="nn">torch._sources</span> <span class="kn">import</span> <span class="n">fake_range</span><span class="p">,</span> <span class="n">get_source_lines_and_file</span><span class="p">,</span> <span class="n">parse_def</span>
-<span class="kn">from</span> <span class="nn">torch._utils_internal</span> <span class="kn">import</span> <span class="n">log_torchscript_usage</span>
 <span class="kn">from</span> <span class="nn">torch.futures</span> <span class="kn">import</span> <span class="n">Future</span>
 
 <span class="n">IS_PY39_PLUS</span><span class="p">:</span> <span class="n">Final</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">)</span>
@@ -1065,7 +1064,6 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
 <span class="sd">        # any compiled methods and wasn&#39;t decorated with `@torch.jit.export`</span>
 <span class="sd">        m = torch.jit.script(MyModule())</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">log_torchscript_usage</span><span class="p">(</span><span class="s2">&quot;export&quot;</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">EXPORT</span>
     <span class="k">return</span> <span class="n">fn</span></div>
 
@@ -1107,7 +1105,6 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
 <span class="sd">            # exception raised</span>
 <span class="sd">            m(torch.rand(100))</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">log_torchscript_usage</span><span class="p">(</span><span class="s2">&quot;unused&quot;</span><span class="p">)</span>
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fn</span><span class="p">,</span> <span class="nb">property</span><span class="p">):</span>
         <span class="n">prop</span> <span class="o">=</span> <span class="n">fn</span>
         <span class="nb">setattr</span><span class="p">(</span>  <span class="c1"># noqa: B010</span>
@@ -1195,7 +1192,6 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
 <span class="sd">        import os</span>
 <span class="sd">        os.remove(&#39;m.pt&#39;)</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">log_torchscript_usage</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
 
     <span class="k">if</span> <span class="nb">callable</span><span class="p">(</span><span class="n">drop</span><span class="p">):</span>
         <span class="c1"># used without any args, so drop is actually a function</span>

docs/main/_modules/torch/_jit_internal.html

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docs/main/_modules/torch/_lobpcg.html

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