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<div class="section" id="torch-dist">
<h1>torch.dist<a class="headerlink" href="#torch-dist" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.dist">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">dist</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">other</em>, <em class="sig-param">p=2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.dist" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the p-norm of (<code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> - <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code>)</p>
<p>The shapes of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> must be
<a class="reference internal" href="../notes/broadcasting.html#broadcasting-semantics"><span class="std std-ref">broadcastable</span></a>.</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>other</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the Right-hand-side input tensor</p></li>
<li><p><strong>p</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.8)"><em>float</em></a><em>, </em><em>optional</em>) – the norm to be computed</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">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">tensor([-1.5393, -0.8675, 0.5916, 1.6321])</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span>
<span class="go">tensor([ 0.0967, -1.0511, 0.6295, 0.8360])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">dist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">)</span>
<span class="go">tensor(1.6727)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">dist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="go">tensor(1.6973)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">dist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="go">tensor(inf)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">dist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="go">tensor(2.6537)</span>
</pre></div>
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