code: def test_make_async_gen_multiple_threads():
      """Tests that using multiple threads can overlap compute even more."""
  
      num_items = 5
  
      def gen(base_iterator):
          for i in base_iterator:
              time.sleep(4)
              yield i
  
      def sleep_udf(item):
          time.sleep(5)
          return item
  
      # All 5 items should be fetched concurrently.
      iterator = make_async_gen(
          base_iterator=iter(range(num_items)), fn=gen, num_workers=5
      )
  
      start_time = time.time()
  
      # Only sleep for first item.
      sleep_udf(next(iterator))
  
      # All subsequent items should already be prefetched and should be ready.
      for _ in iterator:
          pass
      end_time = time.time()
  
      # 4 second for first item, 5 seconds for udf, 0.5 seconds buffer
      assert end_time - start_time < 9.5
  
imports: import threading
  import time
  import numpy
  import pandas
  import pyarrow
  import pytest
  import ray
  from ray.data._internal.block_batching.interfaces import Batch
  from ray.data._internal.block_batching.util import _calculate_ref_hits, blocks_to_batches, collate, finalize_batches, format_batches, resolve_block_refs
  from ray.data._internal.util import Queue, make_async_gen
label_desc: Use image node_id 9 for calling a global function with example usage: test_make_async_gen_multiple_threads() and returns: item