code: def deploy_replicas(min_replicas, max_replicas, max_batch_size):
      @serve.deployment(
          name="echo",
          autoscaling_config={
              "metrics_interval_s": 0.1,
              "min_replicas": min_replicas,
              "max_replicas": max_replicas,
              "look_back_period_s": 0.2,
              "downscale_delay_s": 0.2,
              "upscale_delay_s": 0.2,
          },
      )
      class Echo:
          @serve.batch(max_batch_size=max_batch_size)
          async def handle_batch(self, requests):
              return ["hi" for _ in range(len(requests))]
  
          async def __call__(self, request):
              return await self.handle_batch(request)
  
      serve.run(Echo.bind(), name="echo", route_prefix="/echo")
  
file_path: ray/release/serve_tests/workloads/autoscaling_single_deployment.py
imports: import click
  import json
  import logging
  import math
  import os
  import ray
  from ray import serve
  from serve_test_utils import aggregate_all_metrics, run_wrk_on_all_nodes, save_test_results, is_smoke_test
  from serve_test_cluster_utils import setup_local_single_node_cluster, setup_anyscale_cluster, warm_up_one_cluster, NUM_CPU_PER_NODE, NUM_CONNECTIONS
  from typing import Optional
label_desc: Use image node_id 1 for calling a global function with example usage: deploy_replicas(min_replicas, max_replicas, max_batch_size) and returns: unknown, unknown