code: def prepared_saved_conf(
      model_conf, runner_class, runner_module, runner_conf, source
  ):
      logger.info("loaded model_conf is: {}".format(model_conf))
      if "source" in model_conf:
          if source is None:
              source = model_conf["source"]
  
      runner_class_, runner_module_ = (
          model_conf["runner_class"],
          model_conf["runner_module"],
      )
      if (
          runner_class_ == runner_class
          and runner_module_ == runner_module
      ):
          if "runner_conf" in model_conf:
              saved_conf = model_conf["runner_conf"]
              saved_conf.update(runner_conf)
              runner_conf = saved_conf
              logger.info(
                  "runner_conf is updated: {}".format(runner_conf)
              )
      else:
          logger.warning(
              "runner_class or runner_module is not equal to the saved model, "
              "use the new runner_conf, runner_class and runner module to train the model,\
                          saved module & class: {} {}, new module & class: {} {}".format(
                  runner_module_,
                  runner_class_,
                  runner_module,
                  runner_class,
              )
          )
  
      return runner_conf, source, runner_class, runner_module
  
file_path: FATE/python/fate/components/components/nn/component_utils.py
imports: from fate.components.components.nn.loader import Loader
  from fate.components.components.nn.nn_runner import NNRunner
  from fate.components.components.utils import consts
  from fate.arch.dataframe import DataFrame
  from fate.components.components.utils.tools import add_dataset_type
  from fate.arch import Context
  from fate.components.core import ARBITER, GUEST, HOST, Role, cpn
  import logging
  from fate.components.core.component_desc.artifacts.data._dataframe import DataframeReader, DataframeWriter
  from fate.components.core.component_desc.artifacts.data._directory import DataDirectoryReader
  from fate.components.core.component_desc.artifacts.model._directory import ModelDirectoryReader, ModelDirectoryWriter
label_desc: Use image node_id 5 for calling a global function with example usage: prepared_saved_conf(model_conf, runner_class, runner_module, runner_conf, source) and returns: runner_conf,  source,  runner_class,  runner_module