code: def __call__(self, *args, **kwargs):
      new_args = []
      from bigdl.nano.utils.tf import MultiprocessingBackend
  
      backend = MultiprocessingBackend()
  
      main_model = None
  
      with TemporaryDirectory() as temp_dir:
          for i, arg in enumerate(args):
              # save the model
              if isinstance(arg, tf.Module):
                  arg.save(os.path.join(temp_dir, f"args_{i}"))
                  new_args.append(
                      ("model", os.path.join(temp_dir, f"args_{i}"))
                  )
                  main_model = arg
                  continue
  
              # save the optimizer
              if isinstance(arg, tf.keras.optimizers.Optimizer):
                  with open(
                      os.path.join(temp_dir, f"args_{i}.pkl"), "wb"
                  ) as f:
                      cloudpickle.dump(arg, f)
                  new_args.append(
                      (
                          "optimizer",
                          os.path.join(temp_dir, f"args_{i}.pkl"),
                      )
                  )
                  continue
  
              # save the loss
              if isinstance(arg, tf.keras.losses.Loss):
                  with open(
                      os.path.join(temp_dir, f"args_{i}.pkl"), "wb"
                  ) as f:
                      cloudpickle.dump(arg, f)
                  new_args.append(
                      ("loss", os.path.join(temp_dir, f"args_{i}.pkl"))
                  )
                  continue
  
              # serialize the dataset
              if isinstance(arg, tf.data.Dataset):
                  warnings.warn(
                      "If dataset is created by `from_generator`, please initiate "
                      "`_GeneratorState` of the dataset first, that is "
                      "dataset._GeneratorState = dataset._GeneratorState(generator). "
                      "Otherwise, there exists errors because of a known limitation: "
                      "https://www.tensorflow.org/api_docs/python/tf/data/Dataset"
                      "#from_generator"
                  )
                  if hasattr(arg, "_GeneratorState") and hasattr(
                      arg._GeneratorState, "_generator"
                  ):
                      # support dataset created by `from_generator`
                      train_ds_gen = arg._GeneratorState._generator
                      train_ds_signature = arg.element_spec
                      new_args.append(
                          ("dataset", train_ds_gen, train_ds_signature)
                      )
                      continue
                  else:
                      from tensorflow.python.distribute.coordinator.values import (
                          serialize_dataset_to_graph,
                      )
  
                      train_ds_def = serialize_dataset_to_graph(
                          arg
                      ).numpy()
                      train_elem_spec = arg.element_spec
                      new_args.append(
                          ("dataset", train_ds_def, train_elem_spec)
                      )
                      continue
  
              with open(
                  os.path.join(temp_dir, f"args_{i}.pkl"), "wb"
              ) as f:
                  cloudpickle.dump(arg, f)
                  new_args.append(
                      (
                          "others",
                          os.path.join(temp_dir, f"args_{i}.pkl"),
                      )
                  )
  
          # check necessary objects
          arg_instance = list(map(lambda x: x[0], new_args))
          invalidInputError(
              "model" in arg_instance,
              "Please check if a model inherited from "
              "tensorflow.keras.Model or "
              "bigdl.nano.tf.keras.Model is inputted as "
              "parameter in train function.",
          )
          invalidInputError(
              "optimizer" in arg_instance,
              "Please check if optimizer is inputted"
              " as parameter in train function.",
          )
          invalidInputError(
              "loss" in arg_instance,
              "Please check if loss is inputted as "
              "parameter in train function. Moreover, if "
              "you use a customized loss, please add "
              "'nano_multiprocessing_loss' decorator.",
          )
          invalidInputError(
              "dataset" in arg_instance,
              "Please check if tensorflow.data.Dataset "
              "is inputted as parameter in train "
              "function. Moreover, if dataset is "
              "created by 'from_generator', please init"
              " '_GeneratorState' of dataset first.",
          )
  
          target_path = os.path.join(temp_dir, "target.pkl")
          with open(target_path, "wb") as f:
              cloudpickle.dump(self.func, f)
  
          ports = set()
          while len(ports) < self.nproc:
              ports.add(_find_free_port())
          ports = list(ports)
          worker_list = [f"localhost:{p}" for p in ports]
  
          # TODO: this env mainly for core affinity and allocation limit
          # while does not work for stock TF
          envs = schedule_processors(self.nproc)
  
          for i, env in enumerate(envs):
              env.update(
                  {
                      "TF_CONFIG": json.dumps(
                          {
                              "cluster": {"worker": worker_list},
                              "task": {"type": "worker", "index": i},
                          }
                      ),
                      "no_proxy": "localhost",
                  }
              )
  
          histrories = backend.run(
              target=_train_func,
              args=(target_path, *new_args),
              nprocs=self.nproc,
              envs=envs,
          )
  
          main_model.load_weights("trained_model_weights")
  
      return histrories[0]
  
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 2 for calling the _Nano_Customized_Training obj's underlying member method code with example usage: obj.__call__() and returns: histrories