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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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<h1>torch.nextafter<a class="headerlink" href="#torch-nextafter" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="torch.nextafter">
<code class="sig-prename descclassname"><span class="pre">torch.</span></code><code class="sig-name descname"><span class="pre">nextafter</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> → <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><a class="headerlink" href="#torch.nextafter" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the next floating-point value after <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> towards <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code>, elementwise.</p>
<p>The shapes of <code class="docutils literal notranslate"><span class="pre">input</span></code> and <code class="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 first 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 second input tensor</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><p><strong>out</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the output tensor.</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">eps</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">eps</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">nextafter</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="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</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="mf">2.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]))</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="n">eps</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span> <span class="o">-</span> <span class="n">eps</span><span class="p">])</span>
<span class="go">tensor([True, True])</span>
</pre></div>
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