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: Creates a generator to extract data from the queue.
  
  Skip the data if it is `None`.
  
  Yields:
      The next element in the queue, i.e. a tuple
      `(inputs, targets)` or
      `(inputs, targets, sample_weights)`.
docstr_tok: ["Creates","a","generator","to","extract","data","from","the","queue",".","Skip","the","data","if","it","is","`","None","`",".","Yields",":","The","next","element","in","the","queue",",","i.e",".","a","tuple","`","(","inputs",",","targets",")","`","or","`","(","inputs",",","targets",",","sample_weights",")","`","."]
code: def get(self):
      """Creates a generator to extract data from the queue.
  
      Skip the data if it is `None`.
  
      Yields:
          The next element in the queue, i.e. a tuple
          `(inputs, targets)` or
          `(inputs, targets, sample_weights)`.
      """
      while self.is_running():
          try:
              inputs = self.queue.get(block=True, timeout=5).get()
              if self.is_running():
                  self.queue.task_done()
              if inputs is not None:
                  yield inputs
          except queue.Empty:
              pass
          except Exception as e:
              self.stop()
              raise e
  
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 5 for calling the OrderedEnqueuer obj's underlying member method code with example usage: obj.get() without return types