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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 shutdown(cluster_info, queues=['input']):
 """Stops all TensorFlow nodes by feeding ``None`` into the multiprocessing.Queues. Args: :cluster_info: node reservation information for the cluster (e.g. host, executor_id, pid, ports, etc). :queues: *INTERNAL_USE* Returns: A nodeRDD.mapPartitions() function """

  def _shutdown(iter):
    host = util.get_ip_address()
    executor_id = util.read_executor_id()

    # reconnect to shared queue
    mgr = _get_manager(cluster_info, host, executor_id)

    # send SIGTERM to Tensorboard proc (if running)
    for node in cluster_info:
      if node['host'] == host and node['executor_id'] == executor_id:
        tb_pid = node['tb_pid']
        if tb_pid != 0:
          logging.info("Stopping tensorboard (pid={0})".format(tb_pid))
          subprocess.Popen(["kill", str(tb_pid)])

    # terminate any listening queues
    logging.info("Stopping all queues")
    for q in queues:
      try:
        queue = mgr.get_queue(q)
        logging.info("Feeding None into {0} queue".format(q))
        queue.put(None, block=True)
      except (AttributeError, KeyError):
        msg = "Queue '{}' not found on this node, check for exceptions on other nodes.".format(q)
        raise Exception(msg)

    logging.info("Setting mgr.state to 'stopped'")
    mgr.set('state', 'stopped')
    return [True]

  return _shutdown