code: def parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument("--model", required=True)
      parser.add_argument("--model_args", default="")
      parser.add_argument("--pretrained", required=True, type=str)
      parser.add_argument("--tasks", required=True, nargs="+", type=str)
      parser.add_argument(
          "--precision", required=True, nargs="+", type=str
      )
      parser.add_argument("--provide_description", action="store_true")
      parser.add_argument("--num_fewshot", type=int, default=0)
      parser.add_argument("--batch_size", type=str, default=None)
      parser.add_argument(
          "--max_batch_size",
          type=int,
          default=None,
          help="Maximal batch size to try with --batch_size auto",
      )
      parser.add_argument("--device", type=str, default=None)
      parser.add_argument("--output_path", default=None)
      parser.add_argument(
          "--limit",
          type=float,
          default=None,
          help="Limit the number of examples per task. "
          "If <1, limit is a percentage of the total number of examples.",
      )
      parser.add_argument("--data_sampling", type=float, default=None)
      parser.add_argument("--no_cache", action="store_true")
      parser.add_argument("--decontamination_ngrams_path", default=None)
      parser.add_argument("--description_dict_path", default=None)
      parser.add_argument("--check_integrity", action="store_true")
      parser.add_argument(
          "--write_out", action="store_true", default=False
      )
      parser.add_argument("--output_base_path", type=str, default=None)
  
      return parser.parse_args()
  
file_path: BigDL/python/llm/dev/benchmark/harness/run_llb.py
imports: import argparse
  import json
  import logging
  import os
  from harness_to_leaderboard import *
  from lm_eval import tasks, evaluator, utils, models
  from bigdl_llm import BigDLLM
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: parser