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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
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<li class="toctree-l1"><a class="reference internal" href="notes/ddp.html">Distributed Data Parallel</a></li>
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<div class="section" id="torch-overrides">
<h1>torch.overrides<a class="headerlink" href="#torch-overrides" title="Permalink to this headline">¶</a></h1>
<p>This module exposes various helper functions for the <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code>
protocol. See <a class="reference internal" href="notes/extending.html#extending-torch"><span class="std std-ref">Extending torch</span></a> for more detail on the
<code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code> protocol.</p>
<div class="section" id="functions">
<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="torch.overrides.get_ignored_functions">
<code class="sig-prename descclassname"><span class="pre">torch.overrides.</span></code><code class="sig-name descname"><span class="pre">get_ignored_functions</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_ignored_functions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_ignored_functions" title="Permalink to this definition">¶</a></dt>
<dd><p>Return public functions that cannot be overridden by <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A tuple of functions that are publicly available in the torch API but cannot
be overridden with <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code>. Mostly this is because none of the
arguments of these functions are tensors or tensor-likes.</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>Set[Callable]</p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">as_subclass</span> <span class="ow">in</span> <span class="n">torch</span><span class="o">.</span><span class="n">overrides</span><span class="o">.</span><span class="n">get_ignored_functions</span><span class="p">()</span>
<span class="go">True</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">add</span> <span class="ow">in</span> <span class="n">torch</span><span class="o">.</span><span class="n">overrides</span><span class="o">.</span><span class="n">get_ignored_functions</span><span class="p">()</span>
<span class="go">False</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt id="torch.overrides.get_overridable_functions">
<code class="sig-prename descclassname"><span class="pre">torch.overrides.</span></code><code class="sig-name descname"><span class="pre">get_overridable_functions</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_overridable_functions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_overridable_functions" title="Permalink to this definition">¶</a></dt>
<dd><p>List functions that are overridable via __torch_function__</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A dictionary that maps namespaces that contain overridable functions
to functions in that namespace that can be overridden.</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>Dict[Any, List[Callable]]</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="torch.overrides.get_testing_overrides">
<code class="sig-prename descclassname"><span class="pre">torch.overrides.</span></code><code class="sig-name descname"><span class="pre">get_testing_overrides</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_testing_overrides"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_testing_overrides" title="Permalink to this definition">¶</a></dt>
<dd><p>Return a dict containing dummy overrides for all overridable functions</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A dictionary that maps overridable functions in the PyTorch API to
lambda functions that have the same signature as the real function
and unconditionally return -1. These lambda functions are useful
for testing API coverage for a type that defines <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code>.</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>Dict[Callable, Callable]</p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">inspect</span>
<span class="gp">>>> </span><span class="n">my_add</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">overrides</span><span class="o">.</span><span class="n">get_testing_overrides</span><span class="p">()[</span><span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">inspect</span><span class="o">.</span><span class="n">signature</span><span class="p">(</span><span class="n">my_add</span><span class="p">)</span>
<span class="go"><Signature (input, other, out=None)></span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt id="torch.overrides.handle_torch_function">
<code class="sig-prename descclassname"><span class="pre">torch.overrides.</span></code><code class="sig-name descname"><span class="pre">handle_torch_function</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">public_api</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relevant_args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#handle_torch_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.handle_torch_function" title="Permalink to this definition">¶</a></dt>
<dd><p>Implement a function with checks for <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code> overrides.</p>
<p>See torch::autograd::handle_torch_function for the equivalent of this
function in the C++ implementation.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>public_api</strong> (<em>function</em>) – Function exposed by the public torch API originally called like
<code class="docutils literal notranslate"><span class="pre">public_api(*args,</span> <span class="pre">**kwargs)</span></code> on which arguments are now being
checked.</p></li>
<li><p><strong>relevant_args</strong> (<em>iterable</em>) – Iterable of arguments to check for __torch_function__ methods.</p></li>
<li><p><strong>args</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.10)"><em>tuple</em></a>) – Arbitrary positional arguments originally passed into <code class="docutils literal notranslate"><span class="pre">public_api</span></code>.</p></li>
<li><p><strong>kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.10)"><em>tuple</em></a>) – Arbitrary keyword arguments originally passed into <code class="docutils literal notranslate"><span class="pre">public_api</span></code>.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Result from calling <code class="docutils literal notranslate"><span class="pre">implementation</span></code> or an <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code>
method, as appropriate.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.10)">object</a></p>
</dd>
</dl>
<p>:raises TypeError : if no implementation is found.:</p>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">a</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">a</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="p">:</span> <span class="c1"># This will make func dispatchable by __torch_function__</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">handle_torch_function</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="p">(</span><span class="n">a</span><span class="p">,),</span> <span class="n">a</span><span class="p">)</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">a</span> <span class="o">+</span> <span class="mi">0</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt id="torch.overrides.has_torch_function">
<code class="sig-prename descclassname"><span class="pre">torch.overrides.</span></code><code class="sig-name descname"><span class="pre">has_torch_function</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.overrides.has_torch_function" title="Permalink to this definition">¶</a></dt>
<dd><p>Check for __torch_function__ implementations in the elements of an iterable.
Considers exact <code class="docutils literal notranslate"><span class="pre">Tensor</span></code> s and <code class="docutils literal notranslate"><span class="pre">Parameter</span></code> s non-dispatchable.
:param relevant_args: Iterable or aguments to check for __torch_function__ methods.
:type relevant_args: iterable</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>True if any of the elements of relevant_args have __torch_function__
implementations, False otherwise.</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)">bool</a></p>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.is_tensor_like</span></code></dt><dd><p>Checks if something is a Tensor-like, including an exact <code class="docutils literal notranslate"><span class="pre">Tensor</span></code>.</p>
</dd>
</dl>
</div>
</dd></dl>
<dl class="py function">
<dt id="torch.overrides.is_tensor_like">
<code class="sig-prename descclassname"><span class="pre">torch.overrides.</span></code><code class="sig-name descname"><span class="pre">is_tensor_like</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inp</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#is_tensor_like"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.is_tensor_like" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if the passed-in input is a Tensor-like.</p>
<p>Currently, this occurs whenever there’s a <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code>
attribute on the type of the input.</p>
<p class="rubric">Examples</p>
<p>A subclass of tensor is generally a Tensor-like.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">class</span> <span class="nc">SubTensor</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="o">...</span>
<span class="gp">>>> </span><span class="n">is_tensor_like</span><span class="p">(</span><span class="n">SubTensor</span><span class="p">([</span><span class="mi">0</span><span class="p">]))</span>
<span class="go">True</span>
</pre></div>
</div>
<p>Built-in or user types aren’t usually Tensor-like.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">is_tensor_like</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span>
<span class="go">False</span>
<span class="gp">>>> </span><span class="n">is_tensor_like</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
<span class="go">False</span>
<span class="gp">>>> </span><span class="k">class</span> <span class="nc">NotATensor</span><span class="p">:</span> <span class="o">...</span>
<span class="gp">>>> </span><span class="n">is_tensor_like</span><span class="p">(</span><span class="n">NotATensor</span><span class="p">())</span>
<span class="go">False</span>
</pre></div>
</div>
<p>But, they can be made Tensor-like by implementing __torch_function__.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">class</span> <span class="nc">TensorLike</span><span class="p">:</span>
<span class="gp">... </span> <span class="nd">@classmethod</span>
<span class="gp">... </span> <span class="k">def</span> <span class="nf">__torch_function__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">types</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
<span class="gp">... </span> <span class="k">return</span> <span class="o">-</span><span class="mi">1</span>
<span class="gp">>>> </span><span class="n">is_tensor_like</span><span class="p">(</span><span class="n">TensorLike</span><span class="p">())</span>
<span class="go">True</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt id="torch.overrides.is_tensor_method_or_property">
<code class="sig-prename descclassname"><span class="pre">torch.overrides.</span></code><code class="sig-name descname"><span class="pre">is_tensor_method_or_property</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#is_tensor_method_or_property"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.is_tensor_method_or_property" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns True if the function passed in is a handler for a
method or property belonging to <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>, as passed
into <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>For properties, their <code class="docutils literal notranslate"><span class="pre">__get__</span></code> method must be passed in.</p>
</div>
<p>This may be needed, in particular, for the following reasons:</p>
<ol class="arabic simple">
<li><p>Methods/properties sometimes don’t contain a <cite>__module__</cite> slot.</p></li>
<li><p>They require that the first passed-in argument is an instance
of <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>.</p></li>
</ol>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">is_tensor_method_or_property</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">add</span><span class="p">)</span>
<span class="go">True</span>
<span class="gp">>>> </span><span class="n">is_tensor_method_or_property</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">)</span>
<span class="go">False</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt id="torch.overrides.wrap_torch_function">
<code class="sig-prename descclassname"><span class="pre">torch.overrides.</span></code><code class="sig-name descname"><span class="pre">wrap_torch_function</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dispatcher</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#wrap_torch_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.wrap_torch_function" title="Permalink to this definition">¶</a></dt>
<dd><p>Wraps a given function with <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code> -related functionality.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>dispatcher</strong> (<em>Callable</em>) – A callable that returns an iterable of Tensor-likes passed into the function.</p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This decorator may reduce the performance of your code. Generally, it’s enough to express
your code as a series of functions that, themselves, support __torch_function__. If you
find yourself in the rare situation where this is not the case, e.g. if you’re wrapping a
low-level library and you also need it to work for Tensor-likes, then this function is available.</p>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">dispatcher</span><span class="p">(</span><span class="n">a</span><span class="p">):</span> <span class="c1"># Must have the same signature as func</span>
<span class="gp">... </span> <span class="k">return</span> <span class="p">(</span><span class="n">a</span><span class="p">,)</span>
<span class="gp">>>> </span><span class="nd">@torch</span><span class="o">.</span><span class="n">overrides</span><span class="o">.</span><span class="n">wrap_torch_function</span><span class="p">(</span><span class="n">dispatcher</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">a</span><span class="p">):</span> <span class="c1"># This will make func dispatchable by __torch_function__</span>
<span class="gp">... </span> <span class="k">return</span> <span class="n">a</span> <span class="o">+</span> <span class="mi">0</span>
</pre></div>
</div>
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