code: def train(
      cluster_info, cluster_meta, feed_timeout=600, qname="input"
  ):
      """Feeds Spark partitions into the shared multiprocessing.Queue.
  
      Args:
        :cluster_info: node reservation information for the cluster (e.g. host, executor_id, pid, ports, etc)
        :cluster_meta: dictionary of cluster metadata (e.g. cluster_id, reservation.Server address, etc)
        :feed_timeout: number of seconds after which data feeding times out (600 sec default)
        :qname: *INTERNAL_USE*
  
      Returns:
        A dataRDD.mapPartitions() function
      """
  
      def _train(iter):
          # get shared queue, reconnecting if necessary
          mgr = _get_manager(
              cluster_info,
              util.get_ip_address(),
              util.read_executor_id(),
          )
          try:
              queue = mgr.get_queue(qname)
              equeue = mgr.get_queue("error")
          except (AttributeError, KeyError):
              msg = "Queue '{}' not found on this node, check for exceptions on other nodes.".format(
                  qname
              )
              raise Exception(msg)
  
          state = str(mgr.get("state"))
          logger.info("mgr.state={0}".format(state))
          terminating = state == "'terminating'"
          if terminating:
              logger.info("mgr is terminating, skipping partition")
              count = sum(1 for item in iter)
              logger.info(
                  "Skipped {0} items from partition".format(count)
              )
          else:
              logger.info(
                  "Feeding partition {0} into {1} queue {2}".format(
                      iter, qname, queue
                  )
              )
              count = 0
              for item in iter:
                  count += 1
                  queue.put(item, block=True)
  
              # wait for consumers to finish processing all items in queue before "finishing" this iterator
              joinThr = Thread(target=queue.join)
              joinThr.start()
              timeout = feed_timeout
              while joinThr.is_alive():
                  if not equeue.empty():
                      e_str = equeue.get()
                      raise Exception("Exception in worker:\n" + e_str)
                  time.sleep(1)
                  timeout -= 1
                  if timeout <= 0:
                      raise Exception("Timeout while feeding partition")
  
              logger.info(
                  "Processed {0} items in partition".format(count)
              )
  
          # check if TF is terminating feed after this partition
          if not terminating:
              state = str(mgr.get("state"))
              terminating = state == "'terminating'"
              if terminating:
                  try:
                      logger.info("TFSparkNode: requesting stop")
                      client = reservation.Client(
                          cluster_meta["server_addr"]
                      )
                      client.request_stop()
                      client.close()
                  except Exception as e:
                      # ignore any errors while requesting stop
                      logger.debug(
                          "Error while requesting stop: {0}".format(e)
                      )
  
          return [terminating]
  
      return _train
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import nested_scopes
  from __future__ import print_function
  import json
  import logging
  import multiprocessing
  import os
  import pkg_resources
  import platform
  import socket
  import subprocess
  import sys
  import uuid
  import time
  import traceback
  from packaging import version
  from threading import Thread
  from .None import TFManager
  from .None import TFNode
  from .None import gpu_info
  from .None import marker
  from .None import reservation
  from .None import util
label_desc: Use image node_id 5 for calling a global function with example usage: train(cluster_info, cluster_meta, feed_timeout, qname) and returns: _train, list