code: def _worker(
      sequence: tf.keras.utils.Sequence, queries: Queue, answers: Queue
  ) -> None:
      """
      _worker defines a data loader worker's primary loop.  This loop runs in either a
      threading.Thread or a multiprocessing.Process; the caller (a _ParallelEnqueuer) is responsible
      for passing in the appropriate type of Queue.
  
      Parameters:
          sequence: the user-provided Keras Sequence.
          queries: a queue of tuples of (indices, order) that need to be read from the sequence.
          answers: a queue of tuples of (data, order) that workers fill with data from the sequence.
      """
  
      try:
          while True:
              query = queries.get()
              if query is None:
                  return
              i, order = query
              data = sequence[i]
              answers.put((data, order))
      finally:
          answers.put(None)
  
imports: import abc
  import collections
  import multiprocessing
  import multiprocessing.queues
  import queue
  import threading
  from typing import Any, Deque, Dict, Iterator, Optional, Type, Union
  import numpy
  import tensorflow
  from determined.common import check
label_desc: Use image node_id 1 for calling a global function with example usage: _worker(sequence, queries, answers) without return types