


module.state_dict().keys()
         ['bias', 'weight']



to(device: Optional[Union[int, device]] = ..., dtype: Optional[Union[dtype, str]] = ..., non_blocking: bool = ...) -> T
   to(dtype: Union[dtype, str], non_blocking: bool = ...) -> T
   to(tensor: Tensor, non_blocking: bool = ...) -> T
  Moves and/or casts the parameters and buffers.

  This can be called as

  to(device=None, dtype=None, non_blocking=False)

  to(dtype, non_blocking=False)

  to(tensor, non_blocking=False)

  to(memory_format=torch.channels_last)

  Its signature is similar to "torch.Tensor.to()", but only
  accepts floating point or complex "dtype"s. In addition, this
  method will only cast the floating point or complex parameters
  and buffers to "dtype" (if given). The integral parameters and
  buffers will be moved "device", if that is given, but with
  dtypes unchanged. When "non_blocking" is set, it tries to
