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<div class="section" id="torch-pow">
<h1>torch.pow<a class="headerlink" href="#torch-pow" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.pow">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">pow</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">exponent</em>, <em class="sig-param">*</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.pow" title="Permalink to this definition">¶</a></dt>
<dd><p>Takes the power of each element in <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> with <code class="xref py py-attr docutils literal notranslate"><span class="pre">exponent</span></code> and
returns a tensor with the result.</p>
<p><code class="xref py py-attr docutils literal notranslate"><span class="pre">exponent</span></code> can be either a single <code class="docutils literal notranslate"><span class="pre">float</span></code> number or a <cite>Tensor</cite>
with the same number of elements as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p>
<p>When <code class="xref py py-attr docutils literal notranslate"><span class="pre">exponent</span></code> is a scalar value, the operation applied is:</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><msubsup><mi>x</mi><mi>i</mi><mtext>exponent</mtext></msubsup></mrow><annotation encoding="application/x-tex">\text{out}_i = x_i ^ \text{exponent}
</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:1.1883279999999998em;vertical-align:-0.276864em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.9114639999999998em;"><span style="top:-2.4231360000000004em;margin-left:0em;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 style="top:-3.180908em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord text mtight"><span class="mord mtight">exponent</span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.276864em;"><span></span></span></span></span></span></span></span></span></span></span>
</div><p>When <code class="xref py py-attr docutils literal notranslate"><span class="pre">exponent</span></code> is a tensor, the operation applied is:</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><msubsup><mi>x</mi><mi>i</mi><msub><mtext>exponent</mtext><mi>i</mi></msub></msubsup></mrow><annotation encoding="application/x-tex">\text{out}_i = x_i ^ {\text{exponent}_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:1.2383279999999999em;vertical-align:-0.276864em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.9614639999999999em;"><span style="top:-2.4231360000000004em;margin-left:0em;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 style="top:-3.2309080000000003em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">exponent</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.20521714285714282em;"><span style="top:-2.2341314285714287em;margin-right:0.07142857142857144em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 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:0.26586857142857145em;"><span></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:0.276864em;"><span></span></span></span></span></span></span></span></span></span></span>
</div><p>When <code class="xref py py-attr docutils literal notranslate"><span class="pre">exponent</span></code> is a tensor, 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">exponent</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>exponent</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em> or </em><em>tensor</em>) – the exponent value</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">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="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([ 0.4331, 1.2475, 0.6834, -0.2791])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="go">tensor([ 0.1875, 1.5561, 0.4670, 0.0779])</span>
<span class="gp">>>> </span><span class="n">exp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">)</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">arange</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([ 1., 2., 3., 4.])</span>
<span class="gp">>>> </span><span class="n">exp</span>
<span class="go">tensor([ 1., 2., 3., 4.])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">exp</span><span class="p">)</span>
<span class="go">tensor([ 1., 4., 27., 256.])</span>
</pre></div>
</div>
<dl class="function">
<dt>
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">pow</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">exponent</em>, <em class="sig-param">*</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor</dt>
<dd></dd></dl>
<p><code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> is a scalar <code class="docutils literal notranslate"><span class="pre">float</span></code> value, and <code class="xref py py-attr docutils literal notranslate"><span class="pre">exponent</span></code> is a tensor.
The returned tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">out</span></code> is of the same shape as <code class="xref py py-attr docutils literal notranslate"><span class="pre">exponent</span></code></p>
<p>The operation applied is:</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><msup><mtext>self</mtext><msub><mtext>exponent</mtext><mi>i</mi></msub></msup></mrow><annotation encoding="application/x-tex">\text{out}_i = \text{self} ^ {\text{exponent}_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:0.8778959999999998em;vertical-align:0em;"></span><span class="mord"><span class="mord text"><span class="mord">self</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8778959999999998em;"><span style="top:-3.1473400000000002em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">exponent</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.20521714285714282em;"><span style="top:-2.2341314285714287em;margin-right:0.07142857142857144em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 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:0.26586857142857145em;"><span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span>
</div><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>self</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – the scalar base value for the power operation</p></li>
<li><p><strong>exponent</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the exponent 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">exp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">base</span> <span class="o">=</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">exp</span><span class="p">)</span>
<span class="go">tensor([ 2., 4., 8., 16.])</span>
</pre></div>
</div>
</dd></dl>
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