class_docstr: An offline predictor worker that takes input and produces output by queue.
  Each process will exit when they see :class:`DIE`.
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docstr: Args:
      idx, config: same as in :class:`MultiProcessPredictWorker`.
      inqueue (multiprocessing.Queue): input queue to get data point. elements are (task_id, dp)
      outqueue (multiprocessing.Queue): output queue to put result. elements are (task_id, output)
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code: def __init__(self, idx, inqueue, outqueue, config):
      """
      Args:
          idx, config: same as in :class:`MultiProcessPredictWorker`.
          inqueue (multiprocessing.Queue): input queue to get data point. elements are (task_id, dp)
          outqueue (multiprocessing.Queue): output queue to put result. elements are (task_id, output)
      """
      super(MultiProcessQueuePredictWorker, self).__init__(idx, config)
      self.inqueue = inqueue
      self.outqueue = outqueue
      assert isinstance(self.inqueue, multiprocessing.queues.Queue)
      assert isinstance(self.outqueue, multiprocessing.queues.Queue)
  
imports: import multiprocessing
  import numpy
  from concurrent.futures import Future
  import tensorflow
  from six.moves import queue, range
  from ..compat import tfv1
  from ..tfutils.model_utils import describe_trainable_vars
  from ..utils import logger
  from ..utils.concurrency import DIE, ShareSessionThread, StoppableThread
  from .base import AsyncPredictorBase, OfflinePredictor, OnlinePredictor
label_desc: Use image node_id 1 to create a new MultiProcessQueuePredictWorker object from inherited base classes: MultiProcessPredictWorker with example: obj = MultiProcessQueuePredictWorker(idx, inqueue, outqueue, config)