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@@ -1563,35 +1563,11 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
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<span class="p">[</span><span class="n">name</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">names</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">is_ellipsis</span><span class="p">(</span><span class="n">name</span><span class="p">)],</span> <span class="n">ellipsis_idx</span>
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<span class="p">)</span></div>
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<div class="viewcode-block" id="Tensor.unflatten"><a class="viewcode-back" href="../../named_tensor.html#torch.Tensor.unflatten">[docs]</a> <span class="k">def</span> <span class="nf">unflatten</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">sizes</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Expands the dimension :attr:`dim` of the :attr:`self` tensor over multiple dimensions</span>
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<span class="sd"> of sizes given by :attr:`sizes`.</span>
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<span class="sd"> * :attr:`sizes` is the new shape of the unflattened dimension and it can be a `Tuple[int]` as well</span>
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<span class="sd"> as `torch.Size` if :attr:`self` is a `Tensor`, or `namedshape` (Tuple[(name: str, size: int)])</span>
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<span class="sd"> if :attr:`self` is a `NamedTensor`. The total number of elements in sizes must match the number</span>
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<span class="sd"> of elements in the original dim being unflattened.</span>
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<div class="viewcode-block" id="Tensor.unflatten"><a class="viewcode-back" href="../../generated/torch.Tensor.unflatten.html#torch.Tensor.unflatten">[docs]</a> <span class="k">def</span> <span class="nf">unflatten</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">sizes</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> unflatten(dim, sizes) -&gt; Tensor</span>
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<span class="sd"> Args:</span>
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<span class="sd"> dim (Union[int, str]): Dimension to unflatten</span>
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<span class="sd"> sizes (Union[Tuple[int] or torch.Size, Tuple[Tuple[str, int]]]): New shape of the unflattened dimension</span>
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<span class="sd"> Examples:</span>
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<span class="sd"> &gt;&gt;&gt; torch.randn(3, 4, 1).unflatten(1, (2, 2)).shape</span>
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<span class="sd"> torch.Size([3, 2, 2, 1])</span>
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<span class="sd"> &gt;&gt;&gt; torch.randn(3, 4, 1).unflatten(1, (-1, 2)).shape # the size -1 is inferred from the size of dimension 1</span>
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<span class="sd"> torch.Size([3, 2, 2, 1])</span>
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<span class="sd"> &gt;&gt;&gt; torch.randn(2, 4, names=(&#39;A&#39;, &#39;B&#39;)).unflatten(&#39;B&#39;, ((&#39;B1&#39;, 2), (&#39;B2&#39;, 2)))</span>
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<span class="sd"> tensor([[[-1.1772, 0.0180],</span>
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<span class="sd"> [ 0.2412, 0.1431]],</span>
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<span class="sd"> [[-1.1819, -0.8899],</span>
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<span class="sd"> [ 1.5813, 0.2274]]], names=(&#39;A&#39;, &#39;B1&#39;, &#39;B2&#39;))</span>
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<span class="sd"> &gt;&gt;&gt; torch.randn(2, names=(&#39;A&#39;,)).unflatten(&#39;A&#39;, ((&#39;B1&#39;, -1), (&#39;B2&#39;, 1)))</span>
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<span class="sd"> tensor([[-0.8591],</span>
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<span class="sd"> [ 0.3100]], names=(&#39;B1&#39;, &#39;B2&#39;))</span>
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<span class="sd"> .. warning::</span>
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<span class="sd"> The named tensor API is experimental and subject to change.</span>
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<span class="sd"> See :func:`torch.unflatten`.</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="k">if</span> <span class="n">has_torch_function_unary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="nb">isinstance</span><span class="p">(</span><span class="n">sizes</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">))</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sizes</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">))</span>
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<span class="p">):</span>
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<span class="n">names</span><span class="p">,</span> <span class="n">sizes</span> <span class="o">=</span> <span class="n">unzip_namedshape</span><span class="p">(</span><span class="n">sizes</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">unflatten</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">sizes</span><span class="p">,</span> <span class="n">names</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">unflatten</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">sizes</span><span class="p">,</span> <span class="n">names</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">unflatten</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">sizes</span><span class="p">)</span></div>
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<div class="viewcode-block" id="Tensor.rename_"><a class="viewcode-back" href="../../named_tensor.html#torch.Tensor.rename_">[docs]</a> <span class="k">def</span> <span class="nf">rename_</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">names</span><span class="p">,</span> <span class="o">**</span><span class="n">rename_map</span><span class="p">):</span>
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<span class="sd">&quot;&quot;&quot;In-place version of :meth:`~Tensor.rename`.&quot;&quot;&quot;</span>
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<span class="c1"># See Note [rename_ / rename API]</span>
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<span class="k">return</span> <span class="n">update_names</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">names</span><span class="p">,</span> <span class="n">rename_map</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div>
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<span class="k">def</span> <span class="nf">to_sparse_coo</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="sd">&quot;&quot;&quot;Convert a tensor to :ref:`coordinate format &lt;sparse-coo-docs&gt;`.</span>
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<span class="sd"> Examples::</span>
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<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">to_sparse</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">to_sparse</span><span class="p">()</span></div>
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<span class="k">def</span> <span class="nf">_update_names</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">names</span><span class="p">,</span> <span class="n">inplace</span><span class="p">):</span>
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<span class="k">if</span> <span class="n">has_torch_function_unary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>

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<span class="sd"> Args:</span>
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<span class="sd"> device_type(str, required): Whether to use &#39;cuda&#39; or &#39;cpu&#39; device</span>
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<span class="sd"> enabled(bool, optional, default=True): Whether autocasting should be enabled in the region.</span>
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<span class="sd"> Default: ``True``</span>
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<span class="sd"> Default: ``True``</span>
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<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">device_type</span> <span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
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