code: def test_connect_to_ray(monkeypatch, ray_start_cluster):
      def getpid(args):
          return os.getpid()
  
      def check_pool_size(pool, size):
          args = [tuple() for _ in range(size)]
          assert len(set(pool.map(getpid, args))) == size
  
      address = ray_start_cluster.address
      # Use different numbers of CPUs to distinguish between starting a local
      # ray cluster and connecting to an existing one.
      start_cpus = 1  # Set in fixture.
      init_cpus = 2
  
      # Set up the cluster id so that gcs is talking with a different
      # storage prefix
      monkeypatch.setenv(
          "RAY_external_storage_namespace", "new_cluster"
      )
      ray._raylet.Config.initialize("")
  
      # Check that starting a pool still starts ray if RAY_ADDRESS not set.
      pool = Pool(processes=init_cpus, ray_address="local")
      assert ray.is_initialized()
      assert int(ray.cluster_resources()["CPU"]) == init_cpus
      check_pool_size(pool, init_cpus)
      pool.terminate()
      pool.join()
      ray.shutdown()
  
      # Check that starting a pool connects to a running ray cluster if
      # ray_address is passed in.
      pool = Pool(ray_address=address)
      assert ray.is_initialized()
      assert int(ray.cluster_resources()["CPU"]) == start_cpus
      check_pool_size(pool, start_cpus)
      pool.terminate()
      pool.join()
      ray.shutdown()
  
      monkeypatch.setenv(
          "RAY_external_storage_namespace", "new_cluster2"
      )
      ray._raylet.Config.initialize("")
  
      # Set RAY_ADDRESS, so pools should connect to the running ray cluster.
      os.environ["RAY_ADDRESS"] = address
  
      # Check that starting a pool connects to a running ray cluster if
      # RAY_ADDRESS is set.
      pool = Pool()
      assert ray.is_initialized()
      assert int(ray.cluster_resources()["CPU"]) == start_cpus
      check_pool_size(pool, start_cpus)
      pool.terminate()
      pool.join()
      ray.shutdown()
  
      # Check that trying to start a pool on an existing ray cluster throws an
      # error if there aren't enough CPUs for the number of processes.
      with pytest.raises(Exception):
          Pool(processes=start_cpus + 1)
      assert int(ray.cluster_resources()["CPU"]) == start_cpus
      ray.shutdown()
  
imports: import os
  import sys
  import platform
  import pytest
  import tempfile
  import time
  import random
  from collections import defaultdict
  import queue
  import math
  import ray
  from ray._private.test_utils import SignalActor
  from ray.util.multiprocessing import Pool, TimeoutError, JoinableQueue
  from ray.util.joblib import register_ray
  from joblib import parallel_backend, Parallel, delayed
label_desc: Use image node_id 8 for calling a global function with example usage: test_connect_to_ray(monkeypatch, ray_start_cluster) and returns: os