code: def train_step(real_images):
      # Sample random points in the latent space
      random_latent_vectors = tf.random.normal(
          shape=(batch_size, latent_dim)
      )
      # Decode them to fake images
      generated_images = generator(random_latent_vectors)
      # Combine them with real images
      combined_images = tf.concat(
          [generated_images, real_images], axis=0
      )
  
      # Assemble labels discriminating real from fake images
      labels = tf.concat(
          [
              tf.ones((batch_size, 1)),
              tf.zeros((real_images.shape[0], 1)),
          ],
          axis=0,
      )
      # Add random noise to the labels - important trick!
      labels += 0.05 * tf.random.uniform(labels.shape)
  
      # Train the discriminator
      with tf.GradientTape() as tape:
          predictions = discriminator(combined_images)
          d_loss = loss_fn(labels, predictions)
      grads = tape.gradient(d_loss, discriminator.trainable_weights)
      d_optimizer.apply(grads, discriminator.trainable_weights)
  
      # Sample random points in the latent space
      random_latent_vectors = tf.random.normal(
          shape=(batch_size, latent_dim)
      )
      # Assemble labels that say "all real images"
      misleading_labels = tf.zeros((batch_size, 1))
  
      # Train the generator (note that we should *not* update the weights
      # of the discriminator)!
      with tf.GradientTape() as tape:
          predictions = discriminator(generator(random_latent_vectors))
          g_loss = loss_fn(misleading_labels, predictions)
      grads = tape.gradient(g_loss, generator.trainable_weights)
      g_optimizer.apply(grads, generator.trainable_weights)
      return d_loss, g_loss, generated_images
  
file_path: keras/guides/writing_a_custom_training_loop_in_tensorflow.py
imports: import time
  import os
  import tensorflow
  import keras
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: train_step(real_images) and returns: d_loss,  g_loss,  generated_images