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<div class="section" id="torch-erfinv">
<h1>torch.erfinv<a class="headerlink" href="#torch-erfinv" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.erfinv">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">erfinv</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.erfinv" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the inverse error function of each element of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.
The inverse error function is defined in the range <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mo>−</mo><mn>1</mn><mo separator="true">,</mo><mn>1</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(-1, 1)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">−</span><span class="mord">1</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">1</span><span class="mclose">)</span></span></span></span>
</span> as:</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><mrow><mi mathvariant="normal">e</mi><mi mathvariant="normal">r</mi><mi mathvariant="normal">f</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">n</mi><mi mathvariant="normal">v</mi></mrow><mo stretchy="false">(</mo><mrow><mi mathvariant="normal">e</mi><mi mathvariant="normal">r</mi><mi mathvariant="normal">f</mi></mrow><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo stretchy="false">)</mo><mo>=</mo><mi>x</mi></mrow><annotation encoding="application/x-tex">\mathrm{erfinv}(\mathrm{erf}(x)) = x
</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathrm">e</span><span class="mord mathrm">r</span><span class="mord mathrm" style="margin-right:0.07778em;">f</span><span class="mord mathrm">i</span><span class="mord mathrm">n</span><span class="mord mathrm" style="margin-right:0.01389em;">v</span></span><span class="mopen">(</span><span class="mord"><span class="mord mathrm">e</span><span class="mord mathrm">r</span><span class="mord mathrm" style="margin-right:0.07778em;">f</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mclose">)</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:0.43056em;vertical-align:0em;"></span><span class="mord mathnormal">x</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>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>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></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">torch</span><span class="o">.</span><span class="n">erfinv</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="mi">0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">]))</span>
<span class="go">tensor([ 0.0000, 0.4769, -inf])</span>
</pre></div>
</div>
</dd></dl>
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