How to use the sdgym.synthesizers.ctgan.Discriminator.to function in sdgym

self.transformer = BGMTransformer()
        self.transformer.fit(train_data, categoricals, ordinals)
        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))

        assert self.batch_size % 2 == 0
        mean = torch.zeros(self.batch_size, self.embedding_dim, device=self.device)
        std = mean + 1

        steps_per_epoch = len(train_data) // self.batch_size
        for i in range(self.epochs):
            for id_ in range(steps_per_epoch):
                fakez = torch.normal(mean=mean, std=std)