In pytorch, we have Variables, which are the building block in autograd, and we have an utility class nn.Parameter, which is used to indicate to nn.Module that that specific variable should be present when .parameters() is called.
For example, you could have something like
class Net(nn.Module):
    def forward(self, input):
        self.x = input * 2
        return self.x

and when you call .parameters(), you don’t want self.x to be returned as a parameter. For more discussion on this matter, have a look at https://github.com/pytorch/pytorch/issues/143 527