How to use the ctgan.models.Generator function in ctgan

"""

        self.transformer = DataTransformer()
        self.transformer.fit(train_data, discrete_columns)
        train_data = self.transformer.transform(train_data)

        data_sampler = Sampler(train_data, self.transformer.output_info)

        data_dim = self.transformer.output_dimensions
        self.cond_generator = ConditionalGenerator(
            train_data,
            self.transformer.output_info,
            log_frequency
        )

        self.generator = Generator(
            self.embedding_dim + self.cond_generator.n_opt,
            self.gen_dim,
            data_dim
        ).to(self.device)

        discriminator = Discriminator(
            data_dim + self.cond_generator.n_opt,
            self.dis_dim
        ).to(self.device)

        optimizerG = optim.Adam(
            self.generator.parameters(), lr=2e-4, betas=(0.5, 0.9),
            weight_decay=self.l2scale
        )
        optimizerD = optim.Adam(discriminator.parameters(), lr=2e-4, betas=(0.5, 0.9))

def __init__(self, embedding_dim, gen_dims, data_dim):
        super(Generator, self).__init__()
        dim = embedding_dim
        seq = []
        for item in list(gen_dims):
            seq += [Residual(dim, item)]
            dim += item
        seq.append(Linear(dim, data_dim))
        self.seq = Sequential(*seq)