code: def main(
      tokens_per_request: int,
      batch_size: int,
      num_replicas: int,
      num_trials: int,
      trial_runtime: float,
      use_intermediate_deployment: bool,
  ):
      app = Downstream.options(num_replicas=num_replicas).bind(
          tokens_per_request
      )
      if use_intermediate_deployment:
          app = Intermediate.bind(app)
  
      serve.run(app)
  
      mean, stddev = asyncio.new_event_loop().run_until_complete(
          run_benchmark(
              tokens_per_request,
              batch_size,
              num_trials,
              trial_runtime,
          )
      )
      print(
          "HTTP streaming throughput {}: {} +- {} tokens/s".format(
              f"(num_replicas={num_replicas}, "
              f"tokens_per_request={tokens_per_request}, "
              f"batch_size={batch_size}, "
              f"use_intermediate_deployment={use_intermediate_deployment})",
              mean,
              stddev,
          )
      )
  
file_path: ray/python/ray/serve/_private/benchmarks/streaming/streaming_http_throughput.py
imports: import asyncio
  import logging
  from typing import Tuple
  import aiohttp
  import click
  from starlette.responses import StreamingResponse
  from ray import serve
  from ray.serve._private.benchmarks.common import run_throughput_benchmark
  from ray.serve.handle import DeploymentHandle, RayServeHandle
label_desc: Use image node_id 1 for calling a global function with example usage: main(tokens_per_request, batch_size, num_replicas, num_trials, trial_runtime, use_intermediate_deployment) without return types