code: def main(args):
      model_name_or_path = args.model_name_or_path
      model, tokenizer = load(
          model_name_or_path, args.enable_streaming_with_H2O, args
      )
      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:
          prompts += sample["turns"]
  
      if args.enable_streaming_with_H2O:
          kv_cache = None
          streaming_inference_heavy_hitter(
              model,
              tokenizer,
              prompts,
              kv_cache,
          )
  
      else:
          kv_cache = None
          streaming_inference(
              model,
              tokenizer,
              prompts,
              kv_cache,
          )
  
file_path: H2O/h2o_hf/run_streaming.py
imports: import warnings
  import torch
  import argparse
  import json
  import os
  import time
  import re
  import sys
  from tqdm import tqdm
  from streaming_llm.utils import load, download_url, load_jsonl
  from transformers.models.llama.modeling_llama import LlamaAttention
  from utils_real_drop.modify_llama import H2OLlamaAttention_streaming, H2OLlamaForCausalLM_streaming
label_desc: Use image node_id 4 for calling a global function with example usage: main(args) without return types