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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
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<div class="section" id="generator">
<h1>Generator<a class="headerlink" href="#generator" title="Permalink to this headline">¶</a></h1>
<dl class="py class">
<dt id="torch.Generator">
<em class="property"><span class="pre">class</span> </em><code class="sig-prename descclassname"><span class="pre">torch.</span></code><code class="sig-name descname"><span class="pre">Generator</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em><span class="sig-paren">)</span> → <a class="reference internal" href="#torch.Generator" title="torch.Generator"><span class="pre">Generator</span></a><a class="headerlink" href="#torch.Generator" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates and returns a generator object that manages the state of the algorithm which
produces pseudo random numbers. Used as a keyword argument in many <a class="reference internal" href="../torch.html#inplace-random-sampling"><span class="std std-ref">In-place random sampling</span></a>
functions.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>device</strong> (<a class="reference internal" href="../tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device for the generator.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>An torch.Generator object.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.Generator" title="torch.Generator">Generator</a></p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">g_cpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Generator</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">g_cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Generator</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
</pre></div>
</div>
<dl class="py attribute">
<dt id="torch.Generator.device">
<code class="sig-name descname"><span class="pre">device</span></code><a class="headerlink" href="#torch.Generator.device" title="Permalink to this definition">¶</a></dt>
<dd><p>Generator.device -> device</p>
<p>Gets the current device of the generator.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">g_cpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Generator</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">g_cpu</span><span class="o">.</span><span class="n">device</span>
<span class="go">device(type='cpu')</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt id="torch.Generator.get_state">
<code class="sig-name descname"><span class="pre">get_state</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span> → <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><a class="headerlink" href="#torch.Generator.get_state" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the Generator state as a <code class="docutils literal notranslate"><span class="pre">torch.ByteTensor</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A <code class="docutils literal notranslate"><span class="pre">torch.ByteTensor</span></code> which contains all the necessary bits
to restore a Generator to a specific point in time.</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">g_cpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Generator</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">g_cpu</span><span class="o">.</span><span class="n">get_state</span><span class="p">()</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt id="torch.Generator.initial_seed">
<code class="sig-name descname"><span class="pre">initial_seed</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span> → <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><span class="pre">int</span></a><a class="headerlink" href="#torch.Generator.initial_seed" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the initial seed for generating random numbers.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">g_cpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Generator</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">g_cpu</span><span class="o">.</span><span class="n">initial_seed</span><span class="p">()</span>
<span class="go">2147483647</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt id="torch.Generator.manual_seed">
<code class="sig-name descname"><span class="pre">manual_seed</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span> → <a class="reference internal" href="#torch.Generator" title="torch.Generator"><span class="pre">Generator</span></a><a class="headerlink" href="#torch.Generator.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.
It is recommended to set a large seed, i.e. a number that has a good balance of 0
and 1 bits. Avoid having many 0 bits in the seed.</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 v3.10)"><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>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>An torch.Generator object.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.Generator" title="torch.Generator">Generator</a></p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">g_cpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Generator</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">g_cpu</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">2147483647</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt id="torch.Generator.seed">
<code class="sig-name descname"><span class="pre">seed</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span> → <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><span class="pre">int</span></a><a class="headerlink" href="#torch.Generator.seed" title="Permalink to this definition">¶</a></dt>
<dd><p>Gets a non-deterministic random number from std::random_device or the current
time and uses it to seed a Generator.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">g_cpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Generator</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">g_cpu</span><span class="o">.</span><span class="n">seed</span><span class="p">()</span>
<span class="go">1516516984916</span>
</pre></div>
</div>
</dd></dl>
<dl class="py method">
<dt id="torch.Generator.set_state">
<code class="sig-name descname"><span class="pre">set_state</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em><span class="sig-paren">)</span> → <span class="pre">void</span><a class="headerlink" href="#torch.Generator.set_state" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the 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>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">g_cpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Generator</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">g_cpu_other</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Generator</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">g_cpu</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">g_cpu_other</span><span class="o">.</span><span class="n">get_state</span><span class="p">())</span>
</pre></div>
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