code: def test_map_async(pool_4_processes):
      def f(args):
          index, signal = args
          ray.get(signal.wait.remote())
          return index, os.getpid()
  
      signal = SignalActor.remote()
      async_result = pool_4_processes.map_async(
          f, [(i, signal) for i in range(1000)]
      )
      assert not async_result.ready()
      with pytest.raises(TimeoutError):
          async_result.get(timeout=0.01)
      async_result.wait(timeout=0.01)
  
      # Send the signal to finish the tasks.
      ray.get(signal.send.remote())
      async_result.wait(timeout=10)
      assert async_result.ready()
      assert async_result.successful()
  
      results = async_result.get()
      assert len(results) == 1000
  
      pid_counts = defaultdict(int)
      for i, (index, pid) in enumerate(results):
          assert i == index
          pid_counts[pid] += 1
  
      # Check that the functions are spread somewhat evenly.
      for count in pid_counts.values():
          assert count > 100
  
      def bad_func(index):
          if index == 50:
              raise Exception("test_map_async failure")
  
      async_result = pool_4_processes.map_async(bad_func, range(100))
      async_result.wait(10)
      assert async_result.ready()
      assert not async_result.successful()
  
      with pytest.raises(Exception, match="test_map_async failure"):
          async_result.get()
  
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 15 for calling a global function with example usage: test_map_async(pool_4_processes) and returns: index,  os