I've been trying to understand this piece of code here for a bit:
class linearRegression(torch.nn.Module):
def __init__(self, inputSize, outputSize):
super(linearRegression, self).__init__()
self.linear = torch.nn.Linear(inputSize, outputSize)
def forward(self, x):
out = self.linear(x)
return out
why is the initialisation of the torch.nn.Module being inherited completely into this class? how does this help when self.linear is calling functions used for the module straight from torch.nn.linear anyways? Would be awesome if somebody could give an explanation on this!