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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="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">&#x2192;</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>Returns a result tensor where each <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="monospace">result[i]</mtext></mrow><annotation encoding="application/x-tex">\texttt{result[i]}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord text"><span class="mord texttt">result[i]</span></span></span></span></span></span> is independently
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">self[i]</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Bernoulli}(\texttt{self[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">self[i]</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> must have
floating point <code class="docutils literal notranslate"><span class="pre">dtype</span></code>, and the result will have the same <code class="docutils literal notranslate"><span class="pre">dtype</span></code>.</p>
<p>See <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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