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<h1>Source code for torch.nn.parameter</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="n">_disabled_torch_function_impl</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
<span class="c1"># Metaclass to combine _TensorMeta and the instance check override for Parameter.</span>
<span class="k">class</span> <span class="nc">_ParameterMeta</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_TensorMeta</span><span class="p">):</span>
<span class="c1"># Make `isinstance(t, Parameter)` return True for custom tensor instances that have the _is_param flag.</span>
<span class="k">def</span> <span class="fm">__instancecheck__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instance</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__instancecheck__</span><span class="p">(</span><span class="n">instance</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span>
<span class="nb">isinstance</span><span class="p">(</span><span class="n">instance</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">instance</span><span class="p">,</span> <span class="s1">'_is_param'</span><span class="p">,</span> <span class="kc">False</span><span class="p">))</span>
<div class="viewcode-block" id="Parameter"><a class="viewcode-back" href="../../../generated/torch.nn.parameter.Parameter.html#torch.nn.parameter.Parameter">[docs]</a><span class="k">class</span> <span class="nc">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">_ParameterMeta</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""A kind of Tensor that is to be considered a module parameter.</span>
<span class="sd"> Parameters are :class:`~torch.Tensor` 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 Tensor 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"> Args:</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:`locally-disable-grad-doc` for more details. Default: `True`</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">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">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="ow">is</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="ow">or</span> <span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="ow">is</span> <span class="n">Parameter</span><span class="p">:</span>
<span class="c1"># For ease of BC maintenance, keep this path for standard Tensor.</span>
<span class="c1"># Eventually (tm), we should change the behavior for standard Tensor to match.</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">_make_subclass</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">requires_grad</span><span class="p">)</span>
<span class="c1"># Path for custom tensors: set a flag on the instance to indicate parameter-ness.</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="n">requires_grad</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Creating a Parameter from an instance of type </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> "</span>
<span class="s2">"requires that detach() returns an instance of the same type, but return "</span>
<span class="sa">f</span><span class="s2">"type </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> was found instead. To use the type as a "</span>
<span class="s2">"Parameter, please correct the detach() semantics defined by "</span>
<span class="s2">"its __torch_dispatch__() implementation."</span><span class="p">)</span>
<span class="n">t</span><span class="o">.</span><span class="n">_is_param</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">return</span> <span class="n">t</span>
<span class="c1"># Note: the 3 methods below only apply to standard Tensor. Parameters of custom tensor types</span>
<span class="c1"># are still considered that custom tensor type and these methods will not be called for them.</span>
<span class="k">def</span> <span class="nf">__deepcopy__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">memo</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="ow">in</span> <span class="n">memo</span><span class="p">:</span>
<span class="k">return</span> <span class="n">memo</span><span class="p">[</span><span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="p">)]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">result</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">clone</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">preserve_format</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">)</span>
<span class="n">memo</span><span class="p">[</span><span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="p">)]</span> <span class="o">=</span> <span class="n">result</span>
<span class="k">return</span> <span class="n">result</span>
<span class="k">def</span> <span class="fm">__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="se">\n</span><span class="s1">'</span> <span class="o">+</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__repr__</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">__reduce_ex__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">proto</span><span class="p">):</span>
<span class="n">state</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_utils</span><span class="o">.</span><span class="n">_get_obj_state</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="c1"># See Note [Don't serialize hooks]</span>
<span class="n">hooks</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">state</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_utils</span><span class="o">.</span><span class="n">_rebuild_parameter</span><span class="p">,</span>
<span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">,</span> <span class="n">hooks</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">return</span> <span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_utils</span><span class="o">.</span><span class="n">_rebuild_parameter_with_state</span><span class="p">,</span>
<span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">,</span> <span class="n">hooks</span><span class="p">,</span> <span class="n">state</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">__torch_function__</span> <span class="o">=</span> <span class="n">_disabled_torch_function_impl</span></div>
<span class="k">class</span> <span class="nc">UninitializedTensorMixin</span><span class="p">:</span>
<span class="n">_allowed_methods</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="fm">__hash__</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">size</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">copy_</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">is_floating_point</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">half</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">float</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">double</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">char</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">short</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">int</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">long</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">cuda</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">cpu</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">to</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">get_device</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_has_compatible_shallow_copy_type</span><span class="p">,</span>
<span class="p">]</span>
<span class="k">def</span> <span class="nf">materialize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Create a Parameter or Tensor with the same properties of the uninitialized one.</span>
<span class="sd"> Given a shape, it materializes a parameter in the same device</span>
<span class="sd"> and with the same `dtype` as the current one or the specified ones in the</span>
<span class="sd"> arguments.</span>
<span class="sd"> Args:</span>
<span class="sd"> shape : (tuple): the shape for the materialized tensor.</span>
<span class="sd"> device (:class:`torch.device`): the desired device of the parameters</span>
<span class="sd"> and buffers in this module. Optional.</span>
<span class="sd"> dtype (:class:`torch.dtype`): the desired floating point type of</span>
<span class="sd"> the floating point parameters and buffers in this module. Optional.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">device</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">device</span>
<span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">dtype</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">dtype</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cls_to_become</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">shape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s1">'Can</span><span class="se">\'</span><span class="s1">t access the shape of an uninitialized parameter or buffer. '</span>
<span class="s1">'This error usually happens in `load_state_dict` when trying to load '</span>
<span class="s1">'an uninitialized parameter into an initialized one. '</span>
<span class="s1">'Call `forward` to initialize the parameters before accessing their attributes.'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">share_memory_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s1">'Can</span><span class="se">\'</span><span class="s1">t share memory on an uninitialized parameter or buffer. '</span>
<span class="s1">'Call `forward` to initialize the parameters before calling '</span>
<span class="s1">'`module.share_memory()`.'</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="sa">f</span><span class="s1">'<</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">>'</span>
<span class="k">def</span> <span class="nf">__reduce_ex__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">proto</span><span class="p">):</span>
<span class="c1"># See Note [Don't serialize hooks]</span>
<span class="k">return</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">,</span>
<span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">,)</span>
<span class="p">)</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">__torch_function__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">types</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(),</span> <span class="n">kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="c1"># method-wrapper is to detect access to Tensor properties that are</span>
<span class="c1"># wrapped in descriptors</span>
<span class="k">if</span> <span class="n">func</span> <span class="ow">in</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_allowed_methods</span> <span class="ow">or</span> <span class="n">func</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'method-wrapper'</span><span class="p">:</span>
<span class="k">if</span> <span class="n">kwargs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">__torch_function__</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">types</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s1">'Attempted to use an uninitialized parameter in </span><span class="si">{}</span><span class="s1">. '</span>
<span class="s1">'This error happens when you are using a `LazyModule` or '</span>
<span class="s1">'explicitly manipulating `torch.nn.parameter.</span><span class="si">{}</span><span class="s1">` '</span>
<span class="s1">'objects. When using LazyModules Call `forward` with a dummy batch '</span>
<span class="s1">'to initialize the parameters before calling torch functions'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">is_lazy</span><span class="p">(</span><span class="n">param</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">UninitializedTensorMixin</span><span class="p">)</span>
<div class="viewcode-block" id="UninitializedParameter"><a class="viewcode-back" href="../../../generated/torch.nn.parameter.UninitializedParameter.html#torch.nn.parameter.UninitializedParameter">[docs]</a><span class="k">class</span> <span class="nc">UninitializedParameter</span><span class="p">(</span><span class="n">UninitializedTensorMixin</span><span class="p">,</span> <span class="n">Parameter</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""A parameter that is not initialized.</span>
<span class="sd"> Uninitialized Parameters are a a special case of :class:`torch.nn.Parameter`</span>
<span class="sd"> where the shape of the data is still unknown.</span>
<span class="sd"> Unlike a :class:`torch.nn.Parameter`, uninitialized parameters</span>
<span class="sd"> hold no data and attempting to access some properties, like their shape,</span>
<span class="sd"> will throw a runtime error. The only operations that can be performed on a uninitialized</span>
<span class="sd"> parameter are changing its datatype, moving it to a different device and</span>
<span class="sd"> converting it to a regular :class:`torch.nn.Parameter`.</span>
<span class="sd"> The default device or dtype to use when the parameter is materialized can be set</span>
<span class="sd"> during construction using e.g. ``device='cuda'``.</span>
<span class="sd"> """</span>
<span class="n">cls_to_become</span> <span class="o">=</span> <span class="n">Parameter</span>
<span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</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="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="n">factory_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'device'</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span> <span class="s1">'dtype'</span><span class="p">:</span> <span class="n">dtype</span><span class="p">}</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">factory_kwargs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">_make_subclass</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">requires_grad</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__deepcopy__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">memo</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="ow">in</span> <span class="n">memo</span><span class="p">:</span>
<span class="k">return</span> <span class="n">memo</span><span class="p">[</span><span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="p">)]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">result</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)(</span><span class="bp">self</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">memo</span><span class="p">[</span><span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="p">)]</span> <span class="o">=</span> <span class="n">result</span>
<span class="k">return</span> <span class="n">result</span></div>
<div class="viewcode-block" id="UninitializedBuffer"><a class="viewcode-back" href="../../../generated/torch.nn.parameter.UninitializedBuffer.html#torch.nn.parameter.UninitializedBuffer">[docs]</a><span class="k">class</span> <span class="nc">UninitializedBuffer</span><span class="p">(</span><span class="n">UninitializedTensorMixin</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""A buffer that is not initialized.</span>
<span class="sd"> Uninitialized Buffer is a a special case of :class:`torch.Tensor`</span>
<span class="sd"> where the shape of the data is still unknown.</span>
<span class="sd"> Unlike a :class:`torch.Tensor`, uninitialized parameters</span>
<span class="sd"> hold no data and attempting to access some properties, like their shape,</span>
<span class="sd"> will throw a runtime error. The only operations that can be performed on a uninitialized</span>
<span class="sd"> parameter are changing its datatype, moving it to a different device and</span>
<span class="sd"> converting it to a regular :class:`torch.Tensor`.</span>
<span class="sd"> The default device or dtype to use when the buffer is materialized can be set</span>
<span class="sd"> during construction using e.g. ``device='cuda'``.</span>
<span class="sd"> """</span>
<span class="n">cls_to_become</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span>
<span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="n">factory_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'device'</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span> <span class="s1">'dtype'</span><span class="p">:</span> <span class="n">dtype</span><span class="p">}</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">factory_kwargs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="o">.</span><span class="n">_make_subclass</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">requires_grad</span><span class="p">)</span></div>
</pre></div>
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