code: def evaluate(opt):
      ArgumentParser.validate_translate_opts(opt)
      ArgumentParser._get_all_transform_translate(opt)
      ArgumentParser._validate_transforms_opts(opt)
      ArgumentParser.validate_translate_opts_dynamic(opt)
      logger = init_logger(opt.log_file)
      set_random_seed(opt.seed, use_gpu(opt))
  
      run_results = {}
      dir_name = os.path.dirname(opt.models[0])
      base_name = os.path.basename(opt.models[0])
  
      output_filename = os.path.join(
          dir_name, "mmlu_results_%s.fr.json" % base_name[:-3]
      )
  
      # Build the translator (along with the model)
      engine = InferenceEnginePY(opt)
  
      data_dir = "eval_llm/MMLU-FR/data/"
      ntrain = 5  # nshots from dev
  
      start_time = time.time()
      for task in TASKS:
          print("Testing %s ..." % task)
          records = []
          src = []
          dev_df = pd.read_csv(
              os.path.join(data_dir, "dev", task + "_dev.fr.csv"),
              header=None,
          )[:ntrain]
          test_df = pd.read_csv(
              os.path.join(data_dir, "test", task + "_test.fr.csv"),
              header=None,
          )
          for i in range(test_df.shape[0]):
              # get prompt and make sure it fits
              k = ntrain
              prompt_end = format_example(
                  test_df, i, include_answer=False
              )
              train_prompt = gen_prompt(dev_df, task, k)
              prompt = train_prompt + prompt_end
              while len(prompt.split()) > 768:
                  prompt_split = prompt.split("\n\n")
                  prompt_split.pop(1)
                  prompt = "\n\n".join(prompt_split)
              label = test_df.iloc[i, test_df.shape[1] - 1]
              records.append({"prompt": prompt, "answer": label})
              src.append(prompt.replace("\n", "｟newline｠"))
  
          scores, preds = engine.infer_list(src)
  
          pred_answers = [
              x.lstrip() for sublist in preds for x in sublist
          ]  # flatten the list of list
  
          gold_answers = [record["answer"] for record in records]
          run_results[task] = {
              "pred_answers": pred_answers,
              "gold_answers": gold_answers,
          }
  
      engine.terminate()
  
      with open(output_filename, "w") as f:
          json.dump(run_results, f, ensure_ascii=False, indent=2)
  
      compute_metric(output_filename)
      end_time = time.time()
      logger.info("total run time %.2f" % (end_time - start_time))
  
file_path: OpenNMT-py/eval_llm/MMLU-FR/run_mmlu_opennmt_fr.py
imports: import json
  import os
  import time
  import pandas
  from onmt.utils.logging import init_logger
  from onmt.inference_engine import InferenceEnginePY
  import onmt.opts
  from onmt.utils.parse import ArgumentParser
  from onmt.utils.misc import use_gpu, set_random_seed
label_desc: Use image node_id 5 for calling a global function with example usage: evaluate(opt) without return types