class_docstr: Builds a Enqueuer from a PyDataset.
  
  Args:
      py_dataset: A `keras.utils.PyDataset` object.
      use_multiprocessing: use multiprocessing if True, otherwise threading
      shuffle: whether to shuffle the data at the beginning of each epoch
docstr: Gets the Pool initializer for multiprocessing.
  
  Args:
      workers: Number of workers.
  
  Returns:
      Function, a Function to initialize the pool
docstr_tok: ["Gets","the","Pool","initializer","for","multiprocessing",".","Args",":","workers",":","Number","of","workers",".","Returns",":","Function",",","a","Function","to","initialize","the","pool"]
code: def _get_executor_init(self, workers):
      """Gets the Pool initializer for multiprocessing.
  
      Args:
          workers: Number of workers.
  
      Returns:
          Function, a Function to initialize the pool
      """
  
      def pool_fn(seqs):
          pool = get_pool_class(True)(
              workers,
              initializer=init_pool_generator,
              initargs=(seqs, None, get_worker_id_queue()),
          )
          _DATA_POOLS.add(pool)
          return pool
  
      return pool_fn
  
imports: import multiprocessing.dummy
  import queue
  import random
  import threading
  import time
  import warnings
  import weakref
  from contextlib import closing
  import numpy
  import tree
  from keras.api_export import keras_export
  from keras.trainers.data_adapters import data_adapter_utils
  from keras.trainers.data_adapters.data_adapter import DataAdapter
label_desc: Use image node_id 2 for calling the OrderedEnqueuer obj's underlying member method code with example usage: obj._get_executor_init(workers) and returns: pool_fn, pool