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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>
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<li class="toctree-l1"><a class="reference internal" href="../notes/broadcasting.html">Broadcasting semantics</a></li>
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<h1>torch.broadcast_to<a class="headerlink" href="#torch-broadcast-to" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.broadcast_to">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">broadcast_to</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">shape</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.broadcast_to" title="Permalink to this definition">¶</a></dt>
<dd><p>Broadcasts <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> to the shape <code class="xref py py-attr docutils literal notranslate"><span class="pre">shape</span></code>.
Equivalent to calling <code class="docutils literal notranslate"><span class="pre">input.expand(shape)</span></code>. See <a class="reference internal" href="../tensors.html#torch.Tensor.expand" title="torch.Tensor.expand"><code class="xref py py-meth docutils literal notranslate"><span class="pre">expand()</span></code></a> for details.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the input tensor.</p></li>
<li><p><strong>shape</strong> (list, tuple, or <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Size</span></code>) – the new shape.</p></li>
</ul>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="go">tensor([[1, 2, 3],</span>
<span class="go"> [1, 2, 3],</span>
<span class="go"> [1, 2, 3]])</span>
</pre></div>
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