# Source code for torch.nn.parameter

from torch.autograd import Variable

[docs]class Parameter(Variable):
r"""A kind of Variable that is to be considered a module parameter.

Parameters are :class:~torch.autograd.Variable subclasses, that have a
very special property when used with :class:Module s - when they're
assigned as Module attributes they are automatically added to the list of
its parameters, and will appear e.g. in :meth:~Module.parameters iterator.
Assigning a Variable doesn't have such effect. This is because one might
want to cache some temporary state, like last hidden state of the RNN, in
the model. If there was no such class as :class:Parameter, these
temporaries would get registered too.

Arguments:
data (Tensor): parameter tensor.
requires_grad (bool, optional): if the parameter requires gradient. See
:ref:excluding-subgraphs for more details.
"""
def __new__(cls, data=None, requires_grad=True):

def __repr__(self):
return 'Parameter containing:' + self.data.__repr__()