code: def main(args, evaluator):
      if args.eval_type == "validation":
          result = {}
          for subject_name in tqdm(TASK_NAME_MAPPING.keys()):
              val_file_path = os.path.join(
                  args.eval_data_path, "val", f"{subject_name}_val.csv"
              )
              val_df = pd.read_csv(val_file_path)
              score, _ = evaluator.eval_subject(
                  subject_name, val_df, args.eval_type
              )
              result[subject_name] = score
          cal_ceval(result)
      elif args.eval_type == "test":
          all_answers = {}
          for subject_name in tqdm(TASK_NAME_MAPPING.keys()):
              test_file_path = os.path.join(
                  args.eval_data_path,
                  "test",
                  f"{subject_name}_test.csv",
              )
              test_df = pd.read_csv(test_file_path)
              _, answers = evaluator.eval_subject(
                  subject_name, test_df, args.eval_type
              )
              all_answers[subject_name] = answers
          json.dump(
              all_answers,
              open("submission.json", "w"),
              ensure_ascii=False,
              indent=4,
          )
      else:
          invalidInputError(
              False, "Invalid eval_type, please use validation or test."
          )
  
file_path: BigDL/python/llm/dev/benchmark/ceval/eval.py
imports: import os
  import argparse
  import pandas
  import torch
  import json
  from tqdm import tqdm
  from bigdl.llm.utils.common.log4Error import invalidInputError
  from evaluators.qwen import QwenEvaluator
  from evaluators.llama import LlamaEvaluator
label_desc: Use image node_id 2 for calling a global function with example usage: main(args, evaluator) without return types