training.py

@tf.function
def train_step(images):
  seed = tf.random.normal([BATCH_SIZE, SEED_SIZE])

  with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
    generated_images = generator(seed, training=True)

    real_output = discriminator(images, training=True)
    fake_output = discriminator(generated_images, training=True)

    gen_loss = generator_loss(fake_output)
    disc_loss = discriminator_loss(real_output, fake_output)
    

    gradients_of_generator = gen_tape.gradient(\
        gen_loss, generator.trainable_variables)
    gradients_of_discriminator = disc_tape.gradient(\
        disc_loss, discriminator.trainable_variables)

    generator_optimizer.apply_gradients(zip(
        gradients_of_generator, generator.trainable_variables))
    discriminator_optimizer.apply_gradients(zip(
        gradients_of_discriminator, 
        discriminator.trainable_variables))
  return gen_loss,disc_loss