I noticed deepcopying a module causes its parameters() to be tensors rather than nn.Parameters.
import torch.nn
import copy
l = torch.nn.Linear(3,1)
c = copy.deepcopy(l)
print([type(p) for p in l.parameters()])
print([type(p) for p in c.parameters()])
[<class 'torch.nn.parameter.Parameter'>, <class 'torch.nn.parameter.Parameter'>]
[<class 'torch.Tensor'>, <class 'torch.Tensor'>]
Why does this happen and can this cause any problems when working with the copy of the module later on?