I generally define my networks in the __init__ function using torch.nn layers. However, if I want me convolution to be dependent on the size of the input, it might make more sense to use the torch.nn.Functional version. Are those parameters learnable too? If so, how does that effect the state graph, particularly when loading and saving models?