that has parameters that can be learned by gradient descent similar to the discriminator function and we usually represent the generator as being a deep neural network though once again it could be any other kind of model that satisfies those differentiability properties after we have applied G to Z we obtain a sample from the model and ideally this will resemble actual samples from the data set though early in learning it will not after we've obtained that sample we apply the discriminator function D again and this