code: def test_scaledown_shared_objects(shutdown_only):
      cluster = AutoscalingCluster(
          head_resources={"CPU": 0},
          worker_node_types={
              "cpu_node": {
                  "resources": {
                      "CPU": 1,
                      "object_store_memory": 100 * 1024 * 1024,
                  },
                  "node_config": {},
                  "min_workers": 0,
                  "max_workers": 5,
              },
          },
          idle_timeout_minutes=0.05,
      )
  
      try:
          cluster.start(
              _system_config={
                  "scheduler_report_pinned_bytes_only": True
              }
          )
          ray.init("auto")
  
          actors = [Actor.remote() for _ in range(5)]
          ray.get([a.f.remote() for a in actors])
          print("All five nodes launched")
  
          # Verify scale-up.
          wait_for_condition(
              lambda: ray.cluster_resources().get("CPU", 0) == 5
          )
  
          data = actors[0].create.remote(1024 * 1024 * 5)
          ray.get([a.recv.remote(data) for a in actors])
          print("Data broadcast successfully, deleting actors.")
          del actors
  
          # Verify scale-down.
          wait_for_condition(
              lambda: ray.cluster_resources().get("CPU", 0) == 1,
              timeout=30,
          )
      finally:
          cluster.shutdown()
  
file_path: ray/python/ray/tests/test_autoscaler_fake_scaledown.py
imports: import platform
  import re
  import numpy
  import pytest
  import ray
  from ray._private.test_utils import wait_for_condition
  from ray.cluster_utils import AutoscalingCluster
label_desc: Use image node_id 1 for calling a global function with example usage: test_scaledown_shared_objects(shutdown_only) without return types