it might be for x, for example,
class A(nn.Module):
  def __init__(self):
    super().__init__()
    self.a = 10
  def forward(self, x):
    print('x type:', type(x))
    # Explicitly forward every layer.
    # Branch2a.
    in_channel = x.size(1)
    print('in_channel type:', type(in_channel))
    l_size = list(x.size())
    print('l_size type:', type(l_size))
    import torch
    fake = torch.ones(3,4,5).cuda()
    print('fake type:', type(fake.size(2)))
    # import pdb; pdb.set_trace()
    return x

net = A()
net(torch.randn(1, 2, 3))

would return
x type: <class 'torch.Tensor'>
in_channel type: <class 'int'>
l_size type: <class 'list'>
fake type: <class 'int'>
tensor([[[-1.2244, -1.2652, -0.6877],
         [ 1.5777, -1.5478,  1.1681]]])