code: def deserialize(fn, datasets=None, inference=False):
      """
      Helper function to load all objects from a serialized file,
      this includes callbacks and datasets as well as the model, layers,
      etc.
  
      Arguments:
          datasets (DataSet, optional): If the dataset is not serialized
                                        in the file it can be passed in
                                        as an argument.  This will also
                                        override any dataset in the serialized
                                        file
          inference (bool, optional): if true only the weights will be loaded, not
                                      the states
      Returns:
          Model: the model object
          Dataset: the data set object
          Callback: the callbacks
      """
      config_dict = load_obj(fn)
  
      if datasets is not None:
          logger.warn(
              "Ignoring datasets serialized in archive file %s" % fn
          )
      elif "datasets" in config_dict:
          ds_cls = load_class(config_dict["datasets"]["type"])
          dataset = ds_cls.gen_class(config_dict["datasets"]["config"])
          datasets = dataset.gen_iterators()
  
      if "train" in datasets:
          data_iter = datasets["train"]
      else:
          key = list(datasets.keys())[0]
          data_iter = datasets[key]
          logger.warn(
              "Could not find training set iterator"
              "using %s instead" % key
          )
  
      model = Model(config_dict, data_iter)
  
      callbacks = None
      if "callbacks" in config_dict:
          # run through the callbacks looking for dataset objects
          # replace them with the corresponding data set above
          cbs = config_dict["callbacks"]["callbacks"]
          for cb in cbs:
              if "config" not in cb:
                  cb["config"] = {}
              for arg in cb["config"]:
                  if (
                      type(cb["config"][arg]) is dict
                      and "type" in cb["config"][arg]
                  ):
                      if cb["config"][arg]["type"] == "Data":
                          key = cb["config"][arg]["name"]
                          if key in datasets:
                              cb["config"][arg] = datasets[key]
                          else:
                              cb["config"][arg] = None
          # now we can generate the callbacks
          callbacks = Callbacks.load_callbacks(
              config_dict["callbacks"], model
          )
      return (model, dataset, callbacks)
  
imports: import logging
  from neon.models.model import Model
  from neon.callbacks.callbacks import Callbacks
  from neon.util.persist import load_class, load_obj
label_desc: Use image node_id 1 for calling a global function with example usage: deserialize(fn, datasets, inference) and returns: model,  dataset,  callbacks