I am proficient enough to understand how to read Pytorch code and reimplement them to fit my own needs but being self-taught there are still a lot of things I do not understand. Truth to be told I didn’t do a lot of OOP at all before learning Pytorch, I mainly just made many functions and chain them together to make my network work. Since I started to look at other people’s code to learn Pytorch I have noticed that there are 2 major ways of coding your networks. One way is to stuff all the layers in nn.sequential() and just assign that to a model OR define a class then assign that to a model. My question is what is the major difference? I have tried both ways and IMO nn.sequential is easier, I have also seen nn.sequential defined within the model class as well.