code: def visualize_training(
      images_val,
      reconstructed_images_val,
      random_images_val,
      log_dir,
      prefix,
      viz_n=10,
  ):
      """Helper method to save images visualizing model reconstructions.
  
      Args:
        images_val: Numpy array containing a batch of input images.
        reconstructed_images_val: Numpy array giving the expected output
          (mean) of the decoder.
        random_images_val: Optionally, a Numpy array giving the expected output
          (mean) of decoding samples from the prior, or `None`.
        log_dir: The directory to write images (Python `str`).
        prefix: A specific label for the saved visualizations, which
          determines their filenames (Python `str`).
        viz_n: The number of images from each batch to visualize (Python `int`).
      """
      save_imgs(
          images_val[:viz_n],
          os.path.join(log_dir, "{}_inputs.png".format(prefix)),
      )
      save_imgs(
          reconstructed_images_val[:viz_n],
          os.path.join(
              log_dir, "{}_reconstructions.png".format(prefix)
          ),
      )
  
      if random_images_val is not None:
          save_imgs(
              random_images_val[:viz_n],
              os.path.join(
                  log_dir, "{}_prior_samples.png".format(prefix)
              ),
          )
  
imports: import functools
  import os
  import time
  from absl import flags
  from matplotlib import cm
  from matplotlib import figure
  from matplotlib.backends import backend_agg
  import numpy
  from six.moves import urllib
  import tensorflow.compat.v1
  from tensorflow_probability import distributions
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow.contrib.learn.python.learn.datasets import mnist
  from tensorflow.python.training import moving_averages
label_desc: Use image node_id 5 for calling a global function with example usage: visualize_training(images_val, reconstructed_images_val, random_images_val, log_dir, prefix, viz_n) without return types