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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def convert_predictions_to_image_summaries(hook_args):
 """Optionally converts images from hooks_args to image summaries. Args: hook_args: DecodeHookArgs namedtuple Returns: summaries: list of tf.Summary values if hook_args.decode_hpara """

  decode_hparams = hook_args.decode_hparams
  if not decode_hparams.display_decoded_images:
    return []
  predictions = hook_args.predictions[0]

  # Display ten random inputs and outputs so that tensorboard does not hang.
  all_summaries = []
  rand_predictions = np.random.choice(predictions, size=10)
  for ind, prediction in enumerate(rand_predictions):
    output_summary = image_to_tf_summary_value(
        prediction["outputs"], tag="%d_output" % ind)
    input_summary = image_to_tf_summary_value(
        prediction["inputs"], tag="%d_input" % ind)
    all_summaries.append(input_summary)
    all_summaries.append(output_summary)
  return all_summaries