import tensorflow as tf

            image_difference = fake_images - real_images
            print_obj(func_name, "image_difference", image_difference)

            # Get random samples from this mixed image distribution.
            mixed_images = random_uniform_num * image_difference
            mixed_images += real_images
            print_obj(func_name, "mixed_images", mixed_images)

            # Send to the discriminator to get logits.
            mixed_logits = self.get_discriminator_logits(
                X=mixed_images, alpha_var=alpha_var, params=params
            )
            print_obj(func_name, "mixed_logits", mixed_logits)

            # Get the mixed loss.
            mixed_loss = tf.reduce_sum(
                input_tensor=mixed_logits,
                name="mixed_loss"
            )
            print_obj(func_name, "mixed_loss", mixed_loss)

            # Get gradient from returned list of length 1.
            mixed_gradients = tf.gradients(
                ys=mixed_loss,
                xs=[mixed_images],
                name="gradients"
            )[0]
            print_obj(func_name, "mixed_gradients", mixed_gradients)

            # Get gradient's L2 norm.
