import torch
from collections import OrderedDict


class Parameter(torch.Tensor):
    r"""A kind of Tensor that is to be considered a module parameter.

    Parameters are :class:`~torch.Tensor` 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 Tensor 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. Default: `True`
    """

    def __new__(cls, data=None, requires_grad=True):
        """
        ----what 
        Parameter.__new__
            0. create a parameter from a tensor
            1. set `requires_grad` true, for getting grads 

        ----procedure
        1. if `data` is None, then create an empty tensor for `data`
        2. then `torch.Tensor._make_subclass` actually 
            add grads for `data` 
        
        ----internal
        torch.Tensor._make_subclass
            0. Instantiates a subclass of torch.Tensor. Used by nn.Parameter()
            1. built_in func from .cpp file
            2. the function name is THPVariable_make_subclass
            4. the source can be understood a little.
        """
        if data is None:
            data = torch.Tensor()
        return torch.Tensor._make_subclass(cls, data, requires_grad)

    def __deepcopy__(self, memo):
        if id(self) in memo:
            return memo[id(self)]
        else:
            result = type(self)(self.data.clone(), self.requires_grad)
            memo[id(self)] = result
            return result

    def __repr__(self):
        return 'Parameter containing:\n' + super(Parameter, self).__repr__()

    def __reduce_ex__(self, proto):
        # See Note [Don't serialize hooks]
        return (
            torch._utils._rebuild_parameter,
            (self.data, self.requires_grad, OrderedDict())
        )
