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

def fit(self, train_data, categorical_columns=tuple(), ordinal_columns=tuple()):

        self.transformer = DataTransformer()
        self.transformer.fit(train_data, categorical_columns, ordinal_columns)
        train_data = self.transformer.transform(train_data)

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

        data_dim = self.transformer.output_dim
        self.cond_generator = Cond(train_data, self.transformer.output_info)

        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))