code: def run_client(
      client_num,
      deployment_name,
      prompt_length,
      max_new_tokens,
      num_queries,
      warmup,
      stream,
      vllm,
      use_thread=False,
  ):
      """
      Run MII client for benchmarking. The scenario is a bit complicated:
      1. The main process puts `num_queries` queries into the input queue
      2. Each client runs `warmup` iterations () taking the queries from the input queue
      3. --- barrier ---
      4. The main process marks the start time
      5a. All clients send `num_queries' query in total and put the results into the result queue
      5b. The main process takes the results from the result queue (in parallel with 5a)
      6. The main process marks the end time after receiving `num_queries' results
      """
  
      if use_thread:
          runnable_cls = threading.Thread
          barrier_cls = threading.Barrier
          queue_cls = queue.Queue
      else:
          runnable_cls = multiprocessing.Process
          barrier_cls = multiprocessing.Barrier
          queue_cls = multiprocessing.Queue
  
      barrier = barrier_cls(client_num + 1)
      query_queue = queue_cls()
      result_queue = queue_cls()
  
      processes = [
          runnable_cls(
              target=_run_parallel,
              args=(
                  deployment_name,
                  warmup,
                  barrier,
                  query_queue,
                  result_queue,
                  client_num,
                  stream,
                  vllm,
              ),
          )
          for i in range(client_num)
      ]
      for p in processes:
          p.start()
  
      tokenizer = AutoTokenizer.from_pretrained(
          "meta-llama/Llama-2-7b-hf"
      )
      query_generator = RandomQueryGenerator(
          all_text, tokenizer, seed=42
      )
      MAX_PROMPT_LENGTH = 4000
      request_text = query_generator.get_random_request_text(
          prompt_length,
          prompt_length * PROMPT_LENGTH_VAR,
          MAX_PROMPT_LENGTH,
          num_queries + warmup * client_num,
      )
  
      for t in request_text:
          req_max_new_tokens = int(
              np.random.normal(
                  max_new_tokens, MAX_NEW_TOKENS_VAR * max_new_tokens
              )
          )
          query_queue.put((t, req_max_new_tokens))
  
      # Tokenizers must be initialized after fork.
      # So we need to fork before putting inputs to the queue.
      # We need this barrier to stop child processse from taking inputs before the main process puts them
      barrier.wait()
      # This barrier is to make sure that all clients have finished warmup
      barrier.wait()
  
      response_details = []
      while len(response_details) < num_queries:
          res = result_queue.get()
          # vLLM returns concatinated tokens
          if vllm:
              all_tokens = tokenizer.tokenize(res.generated_tokens)
              res.generated_tokens = all_tokens[
                  len(tokenizer.tokenize(res.prompt)) :
              ]
          response_details.append(res)
  
      return response_details
  
imports: import os
  import time
  import random
  import argparse
  import queue
  import multiprocessing
  import threading
  from statistics import mean
  from dataclasses import dataclass, asdict
  from typing import List, Iterable
  from pathlib import Path
  from datetime import datetime
  import numpy
  from transformers import AutoTokenizer
  from random_query_generator import RandomQueryGenerator
  from sample_input import all_text
  import time
  import json
  import asyncio
  import requests
  from postprocess_results import get_summary, ResponseDetails
label_desc: Use image node_id 5 for calling a global function with example usage: run_client(client_num, deployment_name, prompt_length, max_new_tokens, num_queries, warmup, stream, vllm, use_thread) and returns: response_details