[Python] What is the difference between an Embedding Layer with a bias immediately afterwards...

I am reading the "Deep Learning for Coders with fastai & PyTorch" book. I'm still a bit confused as to what the Embedding module does. It seems like a short and simple network, except I can't seem to wrap my head around what Embedding does differently than Linear without a bias. I know it does some faster computational version of a dot product where one of the matrices is a one-hot encoded matrix and the other is the embedding matrix. It does this to in effect select a piece of data? Please point out where I am wrong. Here is one of the simple networks shown in the book.

class DotProduct(Module):
def __init__(self, n_users, n_movies, n_factors):
self.user_factors = Embedding(n_users, n_factors)
self.movie_factors = Embedding(n_movies, n_factors)

def forward(self, x):
users = self.user_factors(x[:,0])
movies = self.movie_factors(x[:,1])
return (users * movies).sum(dim=1)

Continue reading...