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<div class="section" id="torch-add">
<h1>torch.add<a class="headerlink" href="#torch-add" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.add">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">add</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">other</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.add" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds the scalar <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> to each element of the input <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>
and returns a new resulting tensor.</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><mtext>out</mtext><mo>=</mo><mtext>input</mtext><mo>+</mo><mtext>other</mtext></mrow><annotation encoding="application/x-tex">\text{out} = \text{input} + \text{other}
</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.61508em;vertical-align:0em;"></span><span class="mord text"><span class="mord">out</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:0.8623000000000001em;vertical-align:-0.19444em;"></span><span class="mord text"><span class="mord">input</span></span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord text"><span class="mord">other</span></span></span></span></span></span>
</div><p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> is of type FloatTensor or DoubleTensor, <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> must be
a real number, otherwise it should be an integer.</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>value</strong> (<em>Number</em>) – the number to be added to each element of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code></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([ 0.0202, 1.0985, 1.3506, -0.6056])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="go">tensor([ 20.0202, 21.0985, 21.3506, 19.3944])</span>
</pre></div>
</div>
<dl class="function">
<dt>
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">add</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">alpha=1</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span></dt>
<dd></dd></dl>
<p>Each element of the tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> is multiplied by the scalar
<code class="xref py py-attr docutils literal notranslate"><span class="pre">alpha</span></code> and added to each element of the tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.
The resulting tensor is returned.</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>
<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><mtext>out</mtext><mo>=</mo><mtext>input</mtext><mo>+</mo><mtext>alpha</mtext><mo>×</mo><mtext>other</mtext></mrow><annotation encoding="application/x-tex">\text{out} = \text{input} + \text{alpha} \times \text{other}
</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.61508em;vertical-align:0em;"></span><span class="mord text"><span class="mord">out</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:0.8623000000000001em;vertical-align:-0.19444em;"></span><span class="mord text"><span class="mord">input</span></span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.8888799999999999em;vertical-align:-0.19444em;"></span><span class="mord text"><span class="mord">alpha</span></span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord text"><span class="mord">other</span></span></span></span></span></span>
</div><p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> is of type FloatTensor or DoubleTensor, <code class="xref py py-attr docutils literal notranslate"><span class="pre">alpha</span></code> must be
a real number, otherwise it should be an integer.</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>
<li><p><strong>alpha</strong> (<em>Number</em>) – the scalar multiplier for <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code></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([-0.9732, -0.3497, 0.6245, 0.4022])</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="mi">1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">b</span>
<span class="go">tensor([[ 0.3743],</span>
<span class="go"> [-1.7724],</span>
<span class="go"> [-0.5811],</span>
<span class="go"> [-0.8017]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">add</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="n">alpha</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="go">tensor([[ 2.7695, 3.3930, 4.3672, 4.1450],</span>
<span class="go"> [-18.6971, -18.0736, -17.0994, -17.3216],</span>
<span class="go"> [ -6.7845, -6.1610, -5.1868, -5.4090],</span>
<span class="go"> [ -8.9902, -8.3667, -7.3925, -7.6147]])</span>
</pre></div>
</div>
</dd></dl>
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