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<div class="section" id="torch-tensor-bernoulli">
<h1>torch.Tensor.bernoulli_<a class="headerlink" href="#torch-tensor-bernoulli" title="Permalink to this heading">¶</a></h1>
<dl class="py method">
<dt class="sig sig-object py" id="torch.Tensor.bernoulli_">
<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">bernoulli_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</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">generator</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="headerlink" href="#torch.Tensor.bernoulli_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fills each location of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> with an independent sample from
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Bernoulli</mtext><mo stretchy="false">(</mo><mtext mathvariant="monospace">p</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Bernoulli}(\texttt{p})</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 text"><span class="mord">Bernoulli</span></span><span class="mopen">(</span><span class="mord text"><span class="mord texttt">p</span></span><span class="mclose">)</span></span></span></span></span>. <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> can have integral
<code class="docutils literal notranslate"><span class="pre">dtype</span></code>.</p>
<p><code class="xref py py-attr docutils literal notranslate"><span class="pre">p</span></code> should either be a scalar or tensor containing probabilities to be
used for drawing the binary random number.</p>
<p>If it is a tensor, the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mtext>i</mtext><mrow><mi>t</mi><mi>h</mi></mrow></msup></mrow><annotation encoding="application/x-tex">\text{i}^{th}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.9069em;"></span><span class="mord"><span class="mord text"><span class="mord">i</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.9069em;"><span style="top:-3.1208em;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 mathnormal mtight">t</span><span class="mord mathnormal mtight">h</span></span></span></span></span></span></span></span></span></span></span></span></span> element of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor
will be set to a value sampled from
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Bernoulli</mtext><mo stretchy="false">(</mo><mtext mathvariant="monospace">p_tensor[i]</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Bernoulli}(\texttt{p\_tensor[i]})</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 text"><span class="mord">Bernoulli</span></span><span class="mopen">(</span><span class="mord text"><span class="mord texttt">p_tensor[i]</span></span><span class="mclose">)</span></span></span></span></span>. In this case <cite>p</cite> must have
floating point <code class="docutils literal notranslate"><span class="pre">dtype</span></code>.</p>
<p>See also <a class="reference internal" href="torch.Tensor.bernoulli.html#torch.Tensor.bernoulli" title="torch.Tensor.bernoulli"><code class="xref py py-meth docutils literal notranslate"><span class="pre">bernoulli()</span></code></a> and <a class="reference internal" href="torch.bernoulli.html#torch.bernoulli" title="torch.bernoulli"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bernoulli()</span></code></a></p>
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