code: def main(args):
      model, tokenizer = load(args.repo_id_or_model_path)
      test_filepath = os.path.join(args.data_root, "mt_bench.jsonl")
      print(f"Loading data from {test_filepath} ...")
  
      if not os.path.exists(test_filepath):
          download_url(
              "https://raw.githubusercontent.com/lm-sys/FastChat/main/fastchat/llm_judge/data/mt_bench/question.jsonl",
              args.data_root,
          )
          os.rename(
              os.path.join(args.data_root, "question.jsonl"),
              test_filepath,
          )
  
      list_data = load_jsonl(test_filepath)
      prompts = []
      for sample in list_data[1:5]:
          prompts += sample["turns"]
  
      if args.enable_streaming:
          kv_cache = enable_streaming_llm(
              model,
              start_size=args.start_size,
              recent_size=args.recent_size,
          )
      else:
          kv_cache = None
  
      streaming_inference(
          model,
          tokenizer,
          prompts,
          kv_cache,
      )
  
file_path: BigDL/python/llm/example/CPU/Applications/streaming-llm/run_streaming_llama.py
imports: import warnings
  import torch
  import argparse
  import os
  from streaming_llm.utils import load, download_url, load_jsonl
  from streaming_llm.enable_streaming_llm import enable_streaming_llm
label_desc: Use image node_id 3 for calling a global function with example usage: main(args) without return types