for epoch in range(num_epochs):
    for n, (real_samples, mnist_labels) in enumerate(train_loader):
        # Данные для тренировки дискриминатора
        real_samples = real_samples.to(device=device)
        real_samples_labels = torch.ones((batch_size, 1)).to(
            device=device)
        latent_space_samples = torch.randn((batch_size, 100)).to(
            device=device)
        generated_samples = generator(latent_space_samples)
        generated_samples_labels = torch.zeros((batch_size, 1)).to(
            device=device)
        all_samples = torch.cat((real_samples, generated_samples))
        all_samples_labels = torch.cat(
            (real_samples_labels, generated_samples_labels))

        # Обучение дискриминатора
        discriminator.zero_grad()
        output_discriminator = discriminator(all_samples)
        loss_discriminator = loss_function(
            output_discriminator, all_samples_labels)
        loss_discriminator.backward()
        optimizer_discriminator.step()

        # Данные для обучения генератора
        latent_space_samples = torch.randn((batch_size, 100)).to(
            device=device)

        # Обучение генератора
        generator.zero_grad()
        generated_samples = generator(latent_space_samples)
        output_discriminator_generated = discriminator(generated_samples)
        loss_generator = loss_function(
            output_discriminator_generated, real_samples_labels)
        loss_generator.backward()
        optimizer_generator.step()

        # Показываем loss
        if n == batch_size - 1:
            print(f"Epoch: {epoch} Loss D.: {loss_discriminator} | Loss G.:{loss_generator}")