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<!DOCTYPE html>
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<h1>Source code for torch.nn.parameter</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="k">import</span> <span class="n">Variable</span>
<div class="viewcode-block" id="Parameter"><a class="viewcode-back" href="../../../nn.html#torch.nn.Parameter">[docs]</a><span class="k">class</span> <span class="nc">Parameter</span><span class="p">(</span><span class="n">Variable</span><span class="p">):</span>
<span class="sd">"""A kind of Variable that is to be considered a module parameter.</span>
<span class="sd"> Parameters are :class:`~torch.autograd.Variable` subclasses, that have a</span>
<span class="sd"> very special property when used with :class:`Module` s - when they're</span>
<span class="sd"> assigned as Module attributes they are automatically added to the list of</span>
<span class="sd"> its parameters, and will appear e.g. in :meth:`~Module.parameters` iterator.</span>
<span class="sd"> Assigning a Variable doesn't have such effect. This is because one might</span>
<span class="sd"> want to cache some temporary state, like last hidden state of the RNN, in</span>
<span class="sd"> the model. If there was no such class as :class:`Parameter`, these</span>
<span class="sd"> temporaries would get registered too.</span>
<span class="sd"> Another difference is that parameters can't be volatile and that they</span>
<span class="sd"> require gradient by default.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> data (Tensor): parameter tensor.</span>
<span class="sd"> requires_grad (bool, optional): if the parameter requires gradient. See</span>
<span class="sd"> :ref:`excluding-subgraphs` for more details.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">__new__</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">Parameter</span><span class="p">,</span> <span class="n">cls</span><span class="p">)</span><span class="o">.</span><span class="n">__new__</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">requires_grad</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s1">'Parameter containing:'</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">__repr__</span><span class="p">()</span></div>
</pre></div>
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