from torch.autograd import 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.
Another difference is that parameters can't be volatile and that they
require gradient by default.
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):
return super(Parameter, cls).__new__(cls, data, requires_grad=requires_grad)
return 'Parameter containing:' + self.data.__repr__()