code: def create_monitored_session(
      target: tf.train.Server,
      task_index: int,
      checkpoint_dir: str,
      checkpoint_save_secs: int,
      config: tf.ConfigProto = None,
  ) -> tf.Session:
      """
      Create a monitored session for the worker
      :param target: the target string for the tf.Session
      :param task_index: the task index of the worker
      :param checkpoint_dir: a directory path where the checkpoints will be stored
      :param checkpoint_save_secs: number of seconds between checkpoints storing
      :param config: the tensorflow configuration (optional)
      :return: the session to use for the run
      """
      # we chose the first task to be the chief
      is_chief = task_index == 0
  
      # Create the monitored session
      sess = tf.train.MonitoredTrainingSession(
          master=target,
          is_chief=is_chief,
          hooks=[],
          checkpoint_dir=checkpoint_dir,
          save_checkpoint_secs=checkpoint_save_secs,
          config=config,
          log_step_count_steps=0,  # disable logging of steps to avoid TF warning during inference
      )
  
      return sess
  
imports: from typing import Tuple
  import tensorflow
label_desc: Use image node_id 4 for calling a global function with example usage: create_monitored_session(target, task_index, checkpoint_dir, checkpoint_save_secs, config) and returns: sess