class_docstr: A decorator to realize nano_multiprocessing training on customized training step.
class_docstr_tok: ["A","decorator","to","realize","nano_multiprocessing","training","on","customized","training","step","."]
docstr: Initialize the training step function.
docstr_tok: ["Initialize","the","training","step","function","."]
code: def __init__(self, func):
      """Initialize the training step function."""
      self.func = func
  
imports: import cloudpickle
  import tensorflow
  import numpy
  from functools import wraps, partial
  from tempfile import TemporaryDirectory
  import os
  import json
  from bigdl.nano.utils.common import schedule_processors
  from bigdl.nano.tf.keras.distributed_utils import _find_free_port
  from tensorflow.keras.losses import Loss
  import warnings
  from bigdl.nano.utils.common import invalidInputError
label_desc: Use image node_id 1 to create a new nano_multiprocessing object from inherited base classes: object with example: obj = nano_multiprocessing(func)