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<div class="section" id="module-torch.random">
<span id="torch-random"></span><h1>torch.random<a class="headerlink" href="#module-torch.random" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.random.fork_rng">
<code class="sig-prename descclassname">torch.random.</code><code class="sig-name descname">fork_rng</code><span class="sig-paren">(</span><em class="sig-param">devices=None</em>, <em class="sig-param">enabled=True</em>, <em class="sig-param">_caller='fork_rng'</em>, <em class="sig-param">_devices_kw='devices'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#fork_rng"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.random.fork_rng" title="Permalink to this definition">¶</a></dt>
<dd><p>Forks the RNG, so that when you return, the RNG is reset
to the state that it was previously in.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>devices</strong> (<em>iterable of CUDA IDs</em>) – CUDA devices for which to fork
the RNG. CPU RNG state is always forked. By default, <a class="reference internal" href="#torch.random.fork_rng" title="torch.random.fork_rng"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fork_rng()</span></code></a> operates
on all devices, but will emit a warning if your machine has a lot
of devices, since this function will run very slowly in that case.
If you explicitly specify devices, this warning will be suppressed</p></li>
<li><p><strong>enabled</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – if <code class="docutils literal notranslate"><span class="pre">False</span></code>, the RNG is not forked. This is a convenience
argument for easily disabling the context manager without having
to delete it and unindent your Python code under it.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.random.get_rng_state">
<code class="sig-prename descclassname">torch.random.</code><code class="sig-name descname">get_rng_state</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → torch.Tensor<a class="reference internal" href="_modules/torch/random.html#get_rng_state"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.random.get_rng_state" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the random number generator state as a <cite>torch.ByteTensor</cite>.</p>
</dd></dl>
<dl class="function">
<dt id="torch.random.initial_seed">
<code class="sig-prename descclassname">torch.random.</code><code class="sig-name descname">initial_seed</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → int<a class="reference internal" href="_modules/torch/random.html#initial_seed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.random.initial_seed" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the initial seed for generating random numbers as a
Python <cite>long</cite>.</p>
</dd></dl>
<dl class="function">
<dt id="torch.random.manual_seed">
<code class="sig-prename descclassname">torch.random.</code><code class="sig-name descname">manual_seed</code><span class="sig-paren">(</span><em class="sig-param">seed</em><span class="sig-paren">)</span> → torch._C.Generator<a class="reference internal" href="_modules/torch/random.html#manual_seed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.random.manual_seed" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the seed for generating random numbers. Returns a
<cite>torch.Generator</cite> object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – The desired seed. Value must be within the inclusive range
<cite>[-0x8000_0000_0000_0000, 0xffff_ffff_ffff_ffff]</cite>. Otherwise, a RuntimeError
is raised. Negative inputs are remapped to positive values with the formula
<cite>0xffff_ffff_ffff_ffff + seed</cite>.</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torch.random.seed">
<code class="sig-prename descclassname">torch.random.</code><code class="sig-name descname">seed</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → int<a class="reference internal" href="_modules/torch/random.html#seed"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.random.seed" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the seed for generating random numbers to a non-deterministic
random number. Returns a 64 bit number used to seed the RNG.</p>
</dd></dl>
<dl class="function">
<dt id="torch.random.set_rng_state">
<code class="sig-prename descclassname">torch.random.</code><code class="sig-name descname">set_rng_state</code><span class="sig-paren">(</span><em class="sig-param">new_state</em><span class="sig-paren">)</span> → None<a class="reference internal" href="_modules/torch/random.html#set_rng_state"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.random.set_rng_state" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the random number generator state.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>new_state</strong> (<em>torch.ByteTensor</em>) – The desired state</p>
</dd>
</dl>
</dd></dl>
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