code: def hyperopt_tune(args):
      # plz set algo='exhaustive' to use exhaustive search, in this case, max_evals is auto set
      # in other case, max_evals needs to be set manually
      config_file_list = (
          args.config_files.strip().split(" ")
          if args.config_files
          else None
      )
      hp = HyperTuning(
          objective_function,
          algo="exhaustive",
          early_stop=10,
          max_evals=100,
          params_file=args.params_file,
          fixed_config_file_list=config_file_list,
          display_file=args.display_file,
      )
      hp.run()
      hp.export_result(output_file=args.output_file)
      print("best params: ", hp.best_params)
      print("best result: ")
      print(hp.params2result[hp.params2str(hp.best_params)])
  
file_path: RecBole/run_hyper.py
imports: import argparse
  import os
  import numpy
  from recbole.trainer import HyperTuning
  from recbole.quick_start import objective_function
  import ray
  from ray import tune
  from ray.tune.schedulers import ASHAScheduler
  import math
label_desc: Use image node_id 1 for calling a global function with example usage: hyperopt_tune(args) without return types