class_docstr: **Deprecated**. Augment batches in the background processes.
  
  Deprecated. Use ``imgaug.multicore.Pool`` instead.
  
  This is a wrapper around the multiprocessing module.
  
  Parameters
  ----------
  batch_loader : BatchLoader or multiprocessing.Queue
      BatchLoader object that loads the data fed into the
      BackgroundAugmenter, or alternatively a Queue. If a Queue, then it
      must be made sure that a final ``None`` in the Queue signals that the
      loading is finished and no more batches will follow. Otherwise the
      BackgroundAugmenter will wait forever for the next batch.
  
  augseq : Augmenter
      An augmenter to apply to all loaded images.
      This may be e.g. a Sequential to apply multiple augmenters.
  
  queue_size : int
      Size of the queue that is used to temporarily save the augmentation
      results. Larger values offer the background processes more room
      to save results when the main process doesn't load much, i.e. they
      can lead to smoother and faster training. For large images, high
      values can block a lot of RAM though.
  
  nb_workers : 'auto' or int
      Number of background workers to spawn.
      If ``auto``, it will be set to ``C-1``, where ``C`` is the number of
      CPU cores.
docstr: Terminates all background processes immediately.
  
  This will also free their RAM.
docstr_tok: ["Terminates","all","background","processes","immediately",".","This","will","also","free","their","RAM","."]
code: def terminate(self):
      """
      Terminates all background processes immediately.
  
      This will also free their RAM.
  
      """
      # pylint: disable=protected-access
      for worker in self.workers:
          if worker.is_alive():
              worker.terminate()
      self.nb_workers_finished = len(self.workers)
  
      if not self.queue_result._closed:
          self.queue_result.close()
      time.sleep(0.01)
  
imports: from __future__ import print_function, division, absolute_import
  import sys
  import multiprocessing
  import threading
  import traceback
  import time
  import random
  import platform
  import numpy
  import cv2
  import imgaug.imgaug
  import imgaug.random
  from imgaug.augmentables.batches import Batch, UnnormalizedBatch
label_desc: Use image node_id 5 for calling the BackgroundAugmenter obj's underlying member method code with example usage: obj.terminate() without return types