code: def objective_function(
      config_dict=None, config_file_list=None, saved=True
  ):
      r"""The default objective_function used in HyperTuning
  
      Args:
          config_dict (dict, optional): Parameters dictionary used to modify experiment parameters. Defaults to ``None``.
          config_file_list (list, optional): Config files used to modify experiment parameters. Defaults to ``None``.
          saved (bool, optional): Whether to save the model. Defaults to ``True``.
      """
  
      config = Config(
          config_dict=config_dict, config_file_list=config_file_list
      )
      init_seed(config["seed"], config["reproducibility"])
      logger = getLogger()
      for hdlr in logger.handlers[:]:  # remove all old handlers
          logger.removeHandler(hdlr)
      init_logger(config)
      logging.basicConfig(level=logging.ERROR)
      dataset = create_dataset(config)
      train_data, valid_data, test_data = data_preparation(
          config, dataset
      )
      init_seed(config["seed"], config["reproducibility"])
      model_name = config["model"]
      model = get_model(model_name)(config, train_data._dataset).to(
          config["device"]
      )
      trainer = get_trainer(config["MODEL_TYPE"], config["model"])(
          config, model
      )
      best_valid_score, best_valid_result = trainer.fit(
          train_data, valid_data, verbose=False, saved=saved
      )
      test_result = trainer.evaluate(test_data, load_best_model=saved)
  
      tune.report(**test_result)
      return {
          "model": model_name,
          "best_valid_score": best_valid_score,
          "valid_score_bigger": config["valid_metric_bigger"],
          "best_valid_result": best_valid_result,
          "test_result": test_result,
      }
  
file_path: RecBole/recbole/quick_start/quick_start.py
imports: import logging
  import sys
  import torch.distributed
  from collections.abc import MutableMapping
  from logging import getLogger
  from ray import tune
  from recbole.config import Config
  from recbole.data import create_dataset, data_preparation
  from recbole.data.transform import construct_transform
  from recbole.utils import init_logger, get_model, get_trainer, init_seed, set_color, get_flops, get_environment
label_desc: Use image node_id 4 for calling a global function with example usage: objective_function(config_dict, config_file_list, saved) and returns: dict