Generate Python docs from pytorch/pytorch@c99e75c

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 (1.10.0a0+gitaab14ff ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.10.0a0+gitc99e75c ) &#x25BC</a>
     </div>
 
 

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 (1.10.0a0+gitaab14ff ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.10.0a0+gitc99e75c ) &#x25BC</a>
     </div>
 
 
@@ -404,6 +404,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="torch/_tensor_str.html">torch._tensor_str</a></li>
 <li><a href="torch/_utils.html">torch._utils</a></li>
 <li><a href="torch/_vmap_internals.html">torch._vmap_internals</a></li>
+<li><a href="torch/ao/quantization/quantize.html">torch.ao.quantization.quantize</a></li>
 <li><a href="torch/autocast_mode.html">torch.autocast_mode</a></li>
 <li><a href="torch/autograd.html">torch.autograd</a></li>
 <ul><li><a href="torch/autograd/anomaly_mode.html">torch.autograd.anomaly_mode</a></li>
@@ -421,12 +422,14 @@ <h1>All modules for which code is available</h1>
 <li><a href="torch/cuda.html">torch.cuda</a></li>
 <ul><li><a href="torch/cuda/amp/autocast_mode.html">torch.cuda.amp.autocast_mode</a></li>
 <li><a href="torch/cuda/amp/grad_scaler.html">torch.cuda.amp.grad_scaler</a></li>
+<li><a href="torch/cuda/graphs.html">torch.cuda.graphs</a></li>
 <li><a href="torch/cuda/memory.html">torch.cuda.memory</a></li>
 <li><a href="torch/cuda/nvtx.html">torch.cuda.nvtx</a></li>
 <li><a href="torch/cuda/random.html">torch.cuda.random</a></li>
 <li><a href="torch/cuda/streams.html">torch.cuda.streams</a></li>
 </ul><li><a href="torch/distributed.html">torch.distributed</a></li>
-<ul><li><a href="torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html">torch.distributed.algorithms.ddp_comm_hooks.default_hooks</a></li>
+<ul><li><a href="torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.html">torch.distributed.algorithms.ddp_comm_hooks.debugging_hooks</a></li>
+<li><a href="torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html">torch.distributed.algorithms.ddp_comm_hooks.default_hooks</a></li>
 <li><a href="torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook</a></li>
 <li><a href="torch/distributed/algorithms/join.html">torch.distributed.algorithms.join</a></li>
 <li><a href="torch/distributed/autograd.html">torch.distributed.autograd</a></li>
@@ -603,14 +606,14 @@ <h1>All modules for which code is available</h1>
 <li><a href="torch/quantization/fuse_modules.html">torch.quantization.fuse_modules</a></li>
 <li><a href="torch/quantization/observer.html">torch.quantization.observer</a></li>
 <li><a href="torch/quantization/qconfig.html">torch.quantization.qconfig</a></li>
-<li><a href="torch/quantization/quantize.html">torch.quantization.quantize</a></li>
 <li><a href="torch/quantization/stubs.html">torch.quantization.stubs</a></li>
 </ul><li><a href="torch/quasirandom.html">torch.quasirandom</a></li>
 <li><a href="torch/random.html">torch.random</a></li>
 <li><a href="torch/serialization.html">torch.serialization</a></li>
 <li><a href="torch/sparse.html">torch.sparse</a></li>
 <li><a href="torch/storage.html">torch.storage</a></li>
 <li><a href="torch/testing/_asserts.html">torch.testing._asserts</a></li>
+<li><a href="torch/testing/_creation.html">torch.testing._creation</a></li>
 <li><a href="torch/utils/benchmark/utils/common.html">torch.utils.benchmark.utils.common</a></li>
 <li><a href="torch/utils/benchmark/utils/timer.html">torch.utils.benchmark.utils.timer</a></li>
 <li><a href="torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html">torch.utils.benchmark.utils.valgrind_wrapper.timer_interface</a></li>

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 (1.10.0a0+gitaab14ff ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.10.0a0+gitc99e75c ) &#x25BC</a>
     </div>
 
 
@@ -688,7 +688,7 @@ <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 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="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>
@@ -709,10 +709,10 @@ <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></div>
+    <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 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="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;</span>
 
 <span class="sd">    Sets the default floating point dtype to :attr:`d`. Supports torch.float32</span>
@@ -755,9 +755,9 @@ <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></div>
+    <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="k">def</span> <span class="nf">use_deterministic_algorithms</span><span class="p">(</span><span class="n">mode</span><span class="p">):</span>
+<div class="viewcode-block" id="use_deterministic_algorithms"><a class="viewcode-back" href="../generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms">[docs]</a><span class="k">def</span> <span class="nf">use_deterministic_algorithms</span><span class="p">(</span><span class="n">mode</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>
@@ -872,15 +872,15 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="sd">        ...</span>
 <span class="sd">        RuntimeError: index_add_cuda_ does not have a deterministic implementation...</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">_C</span><span class="o">.</span><span class="n">_set_deterministic_algorithms</span><span class="p">(</span><span class="n">mode</span><span class="p">)</span>
+    <span class="n">_C</span><span class="o">.</span><span class="n">_set_deterministic_algorithms</span><span class="p">(</span><span class="n">mode</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="are_deterministic_algorithms_enabled"><a class="viewcode-back" href="../generated/torch.are_deterministic_algorithms_enabled.html#torch.are_deterministic_algorithms_enabled">[docs]</a><span class="k">def</span> <span class="nf">are_deterministic_algorithms_enabled</span><span class="p">():</span>
+<span class="k">def</span> <span class="nf">are_deterministic_algorithms_enabled</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>
 <span class="sd">    :func:`torch.use_deterministic_algorithms` documentation for more details.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_deterministic_algorithms</span><span class="p">()</span></div>
+    <span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_deterministic_algorithms</span><span class="p">()</span>
 
-<div class="viewcode-block" id="set_warn_always"><a class="viewcode-back" href="../generated/torch.set_warn_always.html#torch.set_warn_always">[docs]</a><span class="k">def</span> <span class="nf">set_warn_always</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">set_warn_always</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;When this flag is False (default) then some PyTorch warnings may only</span>
 <span class="sd">    appear once per process. This helps avoid excessive warning information.</span>
 <span class="sd">    Setting it to True causes these warnings to always appear, which may be</span>
@@ -890,7 +890,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
 <span class="sd">        b (:class:`bool`): If True, force warnings to always be emitted</span>
 <span class="sd">                           If False, set to the default behaviour</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">_C</span><span class="o">.</span><span class="n">_set_warnAlways</span><span class="p">(</span><span class="n">b</span><span class="p">)</span></div>
+    <span class="n">_C</span><span class="o">.</span><span class="n">_set_warnAlways</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
 
 <span class="k">def</span> <span class="nf">is_warn_always_enabled</span><span class="p">():</span>
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns True if the global warn_always flag is turned on. Refer to</span>
@@ -1051,14 +1051,14 @@ <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>
@@ -1115,9 +1115,9 @@ <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

@@ -193,7 +193,7 @@
           <div class="pytorch-left-menu-search">
 
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>master (1.10.0a0+gitaab14ff ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>master (1.10.0a0+gitc99e75c ) &#x25BC</a>
     </div>