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@@ -640,7 +640,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
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<span class="c1"># All strings are unicode in Python 3.</span>
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<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>
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<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>
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<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>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Computes the gradient of current tensor w.r.t. graph leaves.</span>
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<span class="sd"> The graph is differentiated using the chain rule. If the tensor is</span>
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<span class="n">retain_graph</span><span class="o">=</span><span class="n">retain_graph</span><span class="p">,</span>
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<span class="n">create_graph</span><span class="o">=</span><span class="n">create_graph</span><span class="p">,</span>
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<span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">)</span>
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<span class="k">def</span> <span class="nf">register_hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hook</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Registers a backward hook.</span>
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<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">is_shared</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>
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<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">storage</span><span class="p">()</span><span class="o">.</span><span class="n">is_shared</span><span class="p">()</span></div>
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<span class="k">def</span> <span class="fm">__reversed__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="k">return</span> <span class="n">LU</span><span class="p">,</span> <span class="n">pivots</span>
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<div class="viewcode-block" id="Tensor.stft"><a class="viewcode-back" href="../../generated/torch.Tensor.stft.html#torch.Tensor.stft">[docs]</a> <span class="k">def</span> <span class="nf">stft</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_fft</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">hop_length</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>
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<span class="k">def</span> <span class="nf">stft</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_fft</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">hop_length</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>
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<span class="n">center</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">pad_mode</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s1">&#39;reflect&#39;</span><span class="p">,</span> <span class="n">normalized</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
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@@ -862,7 +862,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
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<span class="n">onesided</span><span class="o">=</span><span class="n">onesided</span><span class="p">,</span> <span class="n">return_complex</span><span class="o">=</span><span class="n">return_complex</span>
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<span class="p">)</span>
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<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stft</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_fft</span><span class="p">,</span> <span class="n">hop_length</span><span class="p">,</span> <span class="n">win_length</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> <span class="n">center</span><span class="p">,</span>
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<div class="viewcode-block" id="Tensor.istft"><a class="viewcode-back" href="../../generated/torch.Tensor.istft.html#torch.Tensor.istft">[docs]</a> <span class="k">def</span> <span class="nf">istft</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_fft</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">hop_length</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>
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@@ -893,7 +893,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
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<span class="kn">from</span> <span class="nn">torch.autograd._functions</span> <span class="kn">import</span> <span class="n">Resize</span>
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<span class="k">return</span> <span class="n">Resize</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
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<div class="viewcode-block" id="Tensor.split"><a class="viewcode-back" href="../../generated/torch.Tensor.split.html#torch.Tensor.split">[docs]</a> <span class="k">def</span> <span class="nf">split</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">split_size</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
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<span class="k">def</span> <span class="nf">split</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">split_size</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;See :func:`torch.split`</span>
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<span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">Tensor</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">split_with_sizes</span><span class="p">(</span><span class="n">split_size</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span>
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<span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">Tensor</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">split_with_sizes</span><span class="p">(</span><span class="n">split_size</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span></div>
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<span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">Tensor</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">split_with_sizes</span><span class="p">(</span><span class="n">split_size</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span>
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<span class="k">def</span> <span class="nf">unique</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">sorted</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">return_inverse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns the unique elements of the input tensor.</span>

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