code: def train_one_epoch(
      generator,
      discriminator,
      generator_optimizer,
      discriminator_optimizer,
      dataset,
      step_counter,
      log_interval,
      noise_dim,
  ):
      """
      Train `generator` and `discriminator` models on `dataset`.
      Args:
      generator: Generator model.
      discriminator: Discriminator model.
      generator_optimizer: Optimizer to use for generator.
      discriminator_optimizer: Optimizer to use for discriminator.
      dataset: Dataset of images to train on.
      step_counter: An integer variable, used to write summaries regularly.
      log_interval: How many steps to wait between logging and collecting
        summaries.
      noise_dim: Dimension of noise vector to use.
      """
  
      total_generator_loss = 0.0
      total_discriminator_loss = 0.0
      for batch_index, images in enumerate(dataset):
          with tf.device("/cpu:0"):
              tf.compat.v1.assign_add(step_counter, 1)
          with tf.contrib.summary.record_summaries_every_n_global_steps(
              log_interval, global_step=step_counter
          ):
              current_batch_size = images.shape[0]
              noise = tf.random.uniform(
                  shape=[current_batch_size, noise_dim],
                  minval=-1.0,
                  maxval=1.0,
                  seed=batch_index,
              )
  
              # we can use 2 tapes or a single persistent tape.
              # Using two tapes is memory efficient since intermediate tensors can be
              # released between the two .gradient() calls below
              with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
                  generated_images = generator(noise)
                  tf.contrib.summary.image(
                      "generated_images",
                      tf.reshape(generated_images, [-1, 28, 28, 1]),
                      max_images=10,
                  )
  
                  discriminator_gen_outputs = discriminator(
                      generated_images
                  )
                  discriminator_real_outputs = discriminator(images)
                  discriminator_loss_val = discriminator_loss(
                      discriminator_real_outputs,
                      discriminator_gen_outputs,
                  )
                  total_discriminator_loss += discriminator_loss_val
  
                  generator_loss_val = generator_loss(
                      discriminator_gen_outputs
                  )
                  total_generator_loss += generator_loss_val
  
              generator_grad = gen_tape.gradient(
                  generator_loss_val, generator.variables
              )
              discriminator_grad = disc_tape.gradient(
                  discriminator_loss_val, discriminator.variables
              )
  
              generator_optimizer.apply_gradients(
                  zip(generator_grad, generator.variables)
              )
              discriminator_optimizer.apply_gradients(
                  zip(discriminator_grad, discriminator.variables)
              )
  
              if (
                  log_interval
                  and batch_index > 0
                  and batch_index % log_interval == 0
              ):
                  print(
                      "Batch #%d\tAverage Generator Loss: %.6f\tAverage Discriminator Loss: %.6f"
                      % (
                          batch_index,
                          total_generator_loss / batch_index,
                          total_discriminator_loss / batch_index,
                      )
                  )
  
imports: from __future__ import absolute_import, division, print_function
  import argparse
  import os
  import sys
  import time
  from tempfile import gettempdir
  import tensorflow
  from tensorflow.examples.tutorials.mnist import input_data
  from clearml import Task
label_desc: Use image node_id 3 for calling a global function with example usage: train_one_epoch(generator, discriminator, generator_optimizer, discriminator_optimizer, dataset, step_counter, log_interval, noise_dim) without return types