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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 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'))
    logging.info("mgr.state={0}".format(state))
    terminating = state == "'terminating'"
    if terminating:
      logging.info("mgr is terminating, skipping partition")
      count = sum(1 for item in iter)
      logging.info("Skipped {0} items from partition".format(count))
    else:
      logging.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.isAlive()):
        if (not equeue.empty()):
          e_str = equeue.get()
          equeue.task_done()
          raise Exception("exception in worker:\n" + e_str)
        time.sleep(1)
        timeout -= 1
        if timeout <= 0:
          raise Exception("Timeout while feeding partition")

      logging.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:
          logging.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
          logging.debug("Error while requesting stop: {0}".format(e))

    return [terminating]

  return _train