Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

The most fundamental methods a model class needs to implement are:
• __init__(self): It defines the parts that make up the model—in our case, two
parameters, b and w.
You are not limited to defining parameters, though. Models can
contain other models as their attributes as well, so you can easily
nest them. We’ll see an example of this shortly as well.
Besides, do not forget to include super().__init__() to execute
the __init__() method of the parent class (nn.Module) before
your own.
• forward(self, x): It performs the actual computation; that is, it outputs a
prediction, given the input x.
It may seem weird but, whenever using your model to make
predictions, you should NOT call the forward(x) method!
You should call the whole model instead, as in model(x), to
perform a forward pass and output predictions.
The reason is, the call to the whole model involves extra steps,
namely, handling forward and backward hooks. If you don’t use
hooks (and we don’t use any right now), both calls are equivalent.
Hooks are a very useful mechanism that allows retrieving
intermediate values in deeper models. We’ll get to them
eventually.
104 | Chapter 1: A Simple Regression Problem