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‎docs/1.9.1/__config__.html

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@@ -654,7 +654,7 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
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<span class="k">return</span> <span class="n">module</span> <span class="o">+</span> <span class="n">class_name</span>
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<div class="viewcode-block" id="is_tensor"><a class="viewcode-back" href="../generated/torch.is_tensor.html#torch.is_tensor">[docs]</a><span class="k">def</span> <span class="nf">is_tensor</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
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<span class="k">def</span> <span class="nf">is_tensor</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns True if `obj` is a PyTorch tensor.</span>
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<span class="sd"> Note that this function is simply doing ``isinstance(obj, Tensor)``.</span>
@@ -671,19 +671,19 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
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<span class="k">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span></div>
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<span class="k">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span>
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<div class="viewcode-block" id="is_storage"><a class="viewcode-back" href="../generated/torch.is_storage.html#torch.is_storage">[docs]</a><span class="k">def</span> <span class="nf">is_storage</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
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<span class="k">def</span> <span class="nf">is_storage</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns True if `obj` is a PyTorch storage object.</span>
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<span class="sd"> Args:</span>
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<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>
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<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>
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<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>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Sets the default ``torch.Tensor`` type to floating point tensor type</span>
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<span class="sd"> ``t``. This type will also be used as default floating point type for</span>
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<span class="sd"> type inference in :func:`torch.tensor`.</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<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>
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<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>
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<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>
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<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>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Sets the default floating point dtype to :attr:`d`.</span>
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<span class="sd"> This dtype is:</span>
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<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>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot; Sets whether PyTorch operations must use &quot;deterministic&quot;</span>
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<span class="sd"> run on the same software and hardware, always produce the same output.</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;This function is deprecated and will be removed in a future release.</span>
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<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>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;When this flag is False (default) then some PyTorch warnings may only</span>
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<span class="sd"> appear once per process. This helps avoid excessive warning information.</span>
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<span class="sd"> Setting it to True causes these warnings to always appear, which may be</span>
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<span class="sd"> b (:class:`bool`): If True, force warnings to always be emitted</span>
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<span class="sd"> If False, set to the default behaviour</span>
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<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>
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<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>
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<span class="k">def</span> <span class="nf">is_warn_always_enabled</span><span class="p">():</span>
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<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>
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<span class="c1">################################################################################</span>
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<span class="c1"># Define Storage and Tensor classes</span>
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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="c1"># Import the ops &quot;namespace&quot;</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">EXPORT</span>
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<span class="sd"> be ignored and replaced with the raising of an exception. This allows you</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">UNUSED</span>
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<span class="k">return</span> <span class="n">fn</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="n">exc_type</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">traceback</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
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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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<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">def</span> <span class="nf">_copy_to_script_wrapper</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
@@ -1266,7 +1266,7 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
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<span class="nb">globals</span><span class="p">()[</span><span class="sa">f</span><span class="s2">&quot;BroadcastingList</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">BroadcastingList1</span>
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<span class="sd"> Function that returns True when in compilation and False otherwise. This</span>
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@@ -1285,7 +1285,7 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
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<span class="sd"> else:</span>
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<span class="sd"> return unsupported_linear_op(x)</span>
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<span class="k">return</span> <span class="kc">False</span>
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<span class="k">return</span> <span class="kc">False</span></div>
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<span class="c1"># Retrieves a fully-qualified name (module hierarchy + classname) for a given obj.</span>

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‎docs/1.9.1/_modules/torch/_lowrank.html

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<div class="pytorch-left-menu-search">
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<div class="version">
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<a href='https://pytorch.org/docs/versions.html'>1.9.0a0+gite2cb357 &#x25BC</a>
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<a href='https://pytorch.org/docs/versions.html'>1.9.0a0+gitdfbd030 &#x25BC</a>
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</div>
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