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<div class="section" id="torch-div">
<h1>torch.div<a class="headerlink" href="#torch-div" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.div">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">div</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">other</em>, <em class="sig-param">*</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.div" title="Permalink to this definition">¶</a></dt>
<dd><p>Divides each element of the input <code class="docutils literal notranslate"><span class="pre">input</span></code> by the corresponding element of
<code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code>.</p>
<div class="math">
<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mtext>out</mtext><mi>i</mi></msub><mo>=</mo><mfrac><msub><mtext>input</mtext><mi>i</mi></msub><msub><mtext>other</mtext><mi>i</mi></msub></mfrac></mrow><annotation encoding="application/x-tex">\text{out}_i = \frac{\text{input}_i}{\text{other}_i}
</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.76508em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord text"><span class="mord">out</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.31166399999999994em;"><span style="top:-2.5500000000000003em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:2.18086em;vertical-align:-0.8360000000000001em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.3448600000000002em;"><span style="top:-2.3139999999999996em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord text"><span class="mord">other</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.31166399999999994em;"><span style="top:-2.5500000000000003em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.6769999999999996em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord"><span class="mord text"><span class="mord">input</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.21752399999999997em;"><span style="top:-2.4558600000000004em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.24414em;"><span></span></span></span></span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.8360000000000001em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span>
</div><div class="admonition note">
<p class="admonition-title">Note</p>
<p>Performs a “true” division like Python 3. See <a class="reference internal" href="torch.floor_divide.html#torch.floor_divide" title="torch.floor_divide"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.floor_divide()</span></code></a>
for floor division.</p>
</div>
<p>Supports <a class="reference internal" href="../notes/broadcasting.html#broadcasting-semantics"><span class="std std-ref">broadcasting to a common shape</span></a>,
<a class="reference internal" href="../tensor_attributes.html#type-promotion-doc"><span class="std std-ref">type promotion</span></a>, and integer, float, and complex inputs.
Always promotes integer types to the default scalar type.</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 dividend</p></li>
<li><p><strong>other</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em> or </em><em>Number</em>) – the divisor</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>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</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">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([ 0.3810, 1.2774, -0.2972, -0.3719, 0.4637])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="go">tensor([ 0.7620, 2.5548, -0.5944, -0.7439, 0.9275])</span>
<span class="gp">>>> </span><span class="n">a</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="mi">4</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([[-0.3711, -1.9353, -0.4605, -0.2917],</span>
<span class="go"> [ 0.1815, -1.0111, 0.9805, -1.5923],</span>
<span class="go"> [ 0.1062, 1.4581, 0.7759, -1.2344],</span>
<span class="go"> [-0.1830, -0.0313, 1.1908, -1.4757]])</span>
<span class="gp">>>> </span><span class="n">b</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">b</span>
<span class="go">tensor([ 0.8032, 0.2930, -0.8113, -0.2308])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
<span class="go">tensor([[-0.4620, -6.6051, 0.5676, 1.2637],</span>
<span class="go"> [ 0.2260, -3.4507, -1.2086, 6.8988],</span>
<span class="go"> [ 0.1322, 4.9764, -0.9564, 5.3480],</span>
<span class="go"> [-0.2278, -0.1068, -1.4678, 6.3936]])</span>
</pre></div>
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