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Merge pull request pytorch#458 from pytorch/site-v1.6.0
v1.6.0 docs redo for stable (0.7) vision
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‎docs/stable/.buildinfo

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: 93625c989914b71802289037a0f16437
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config: 892a2ec27a03fe01b7f360c8920a2882
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tags: 645f666f9bcd5a90fca523b33c5a78b7

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‎docs/stable/_modules/index.html

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@@ -494,7 +494,6 @@ <h1>All modules for which code is available</h1>
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<li><a href="torch/utils/data/distributed.html">torch.utils.data.distributed</a></li>
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<li><a href="torch/utils/data/sampler.html">torch.utils.data.sampler</a></li>
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<li><a href="torch/utils/mobile_optimizer.html">torch.utils.mobile_optimizer</a></li>
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<li><a href="torch/utils/tensorboard/writer.html">torch.utils.tensorboard.writer</a></li>
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</ul><li><a href="torchvision.html">torchvision</a></li>
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<ul><li><a href="torchvision/datasets/celeba.html">torchvision.datasets.celeba</a></li>
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<li><a href="torchvision/datasets/cifar.html">torchvision.datasets.cifar</a></li>

‎docs/stable/_modules/torch.html

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@@ -838,9 +838,9 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
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<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>
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<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>
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<span class="k">def</span> <span class="nf">compiled_with_cxx11_abi</span><span class="p">():</span>
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<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>
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<span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_GLIBCXX_USE_CXX11_ABI</span></div>
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<span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_GLIBCXX_USE_CXX11_ABI</span>
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<span class="c1"># Import the ops &quot;namespace&quot;</span>

‎docs/stable/_modules/torch/_jit_internal.html

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<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>
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<span class="k">return</span> <span class="n">fn</span></div>
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<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>
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<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>
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<span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> This decorator indicates to the compiler that a function or method should</span>
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<span class="sd"> be ignored and left as a Python function. This allows you to leave code in</span>
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<span class="k">else</span><span class="p">:</span>
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<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>
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<span class="k">return</span> <span class="n">fn</span>
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<span class="k">return</span> <span class="n">decorator</span>
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<span class="k">return</span> <span class="n">decorator</span></div>
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<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>

‎docs/stable/_modules/torch/_lowrank.html

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<span class="k">return</span> <span class="n">Q</span>
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<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">q</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">niter</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
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<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">q</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">niter</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
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<span class="c1"># type: (Tensor, Optional[int], Optional[int], Optional[Tensor]) -&gt; Tuple[Tensor, Tensor, Tensor]</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Return the singular value decomposition ``(U, S, V)`` of a matrix,</span>
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<span class="sd"> batches of matrices, or a sparse matrix :math:`A` such that</span>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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">q</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">niter</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">M</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>

‎docs/stable/_modules/torch/autograd/grad_mode.html

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<span class="k">return</span> <span class="n">generator_context</span>
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<span class="k">class</span> <span class="nc">no_grad</span><span class="p">(</span><span class="n">_DecoratorContextManager</span><span class="p">):</span>
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<div class="viewcode-block" id="no_grad"><a class="viewcode-back" href="../../../autograd.html#torch.autograd.no_grad">[docs]</a><span class="k">class</span> <span class="nc">no_grad</span><span class="p">(</span><span class="n">_DecoratorContextManager</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Context-manager that disabled gradient calculation.</span>
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<span class="sd"> Disabling gradient calculation is useful for inference, when you are sure</span>
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<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
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<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="o">*</span><span class="n">args</span><span class="p">):</span>
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<span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prev</span><span class="p">)</span>
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<span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prev</span><span class="p">)</span></div>
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<div class="viewcode-block" id="enable_grad"><a class="viewcode-back" href="../../../generated/torch.enable_grad.html#torch.enable_grad">[docs]</a><span class="k">class</span> <span class="nc">enable_grad</span><span class="p">(</span><span class="n">_DecoratorContextManager</span><span class="p">):</span>
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<div class="viewcode-block" id="enable_grad"><a class="viewcode-back" href="../../../autograd.html#torch.autograd.enable_grad">[docs]</a><span class="k">class</span> <span class="nc">enable_grad</span><span class="p">(</span><span class="n">_DecoratorContextManager</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Context-manager that enables gradient calculation.</span>
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<span class="sd"> Enables gradient calculation, if it has been disabled via :class:`~no_grad`</span>
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<span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prev</span><span class="p">)</span></div>
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<span class="k">class</span> <span class="nc">set_grad_enabled</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
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<div class="viewcode-block" id="set_grad_enabled"><a class="viewcode-back" href="../../../autograd.html#torch.autograd.set_grad_enabled">[docs]</a><span class="k">class</span> <span class="nc">set_grad_enabled</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Context-manager that sets gradient calculation to on or off.</span>
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<span class="sd"> ``set_grad_enabled`` will enable or disable grads based on its argument :attr:`mode`.</span>
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<span class="k">pass</span>
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<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="o">*</span><span class="n">args</span><span class="p">):</span>
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<span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prev</span><span class="p">)</span>
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<span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prev</span><span class="p">)</span></div>
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</pre></div>
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