code: def test_imbalanced_replicas(self, ongoing_requests):
      config = AutoscalingConfig(
          min_replicas=1,
          max_replicas=10,
          target_num_ongoing_requests_per_replica=5,
          upscale_delay_s=0.0,
          downscale_delay_s=0.0,
      )
  
      policy_manager = AutoscalingPolicyManager(config)
  
      # Check that as long as the average number of ongoing requests equals
      # the target_num_ongoing_requests_per_replica, the number of replicas
      # stays the same
      if (
          np.mean(ongoing_requests)
          == config.target_num_ongoing_requests_per_replica
      ):
          new_num_replicas = policy_manager.get_decision_num_replicas(
              current_num_ongoing_requests=ongoing_requests,
              curr_target_num_replicas=4,
              current_handle_queued_queries=0,
          )
          assert new_num_replicas == 4
  
      # Check downscaling behavior when average number of requests
      # is lower than target_num_ongoing_requests_per_replica
      elif (
          np.mean(ongoing_requests)
          < config.target_num_ongoing_requests_per_replica
      ):
          new_num_replicas = policy_manager.get_decision_num_replicas(
              current_num_ongoing_requests=ongoing_requests,
              curr_target_num_replicas=4,
              current_handle_queued_queries=0,
          )
  
          if (
              config.target_num_ongoing_requests_per_replica
              - np.mean(ongoing_requests)
              <= 1
          ):
              # Autoscaling uses a ceiling operator, which means a slightly low
              # current_num_ongoing_requests value is insufficient to downscale
              assert new_num_replicas == 4
          else:
              assert new_num_replicas == 3
  
      # Check upscaling behavior when average number of requests
      # is higher than target_num_ongoing_requests_per_replica
      else:
          new_num_replicas = policy_manager.get_decision_num_replicas(
              current_num_ongoing_requests=ongoing_requests,
              curr_target_num_replicas=4,
              current_handle_queued_queries=0,
          )
          assert new_num_replicas == 5
  
imports: import sys
  import numpy
  import pytest
  from ray.serve._private.autoscaling_policy import AutoscalingPolicyManager
  from ray.serve._private.constants import CONTROL_LOOP_PERIOD_S
  from ray.serve.autoscaling_policy import _calculate_desired_num_replicas
  from ray.serve.config import AutoscalingConfig
label_desc: Use image node_id 6 for calling the TestGetDecisionNumReplicas obj's underlying member method code with example usage: obj.test_imbalanced_replicas(ongoing_requests) without return types