code: def test_queue():
      queue = Queue(5)
      num_producers = 10
      num_producers_finished = 0
      num_items = 20
  
      def execute_computation():
          for item in range(num_items):
              if queue.put(item):
                  # Return early when it's instructed to do so.
                  break
          # Put -1 as indicator of thread being finished.
          queue.put(-1)
  
      # Use separate threads as producers.
      threads = [
          threading.Thread(target=execute_computation, daemon=True)
          for _ in range(num_producers)
      ]
  
      for thread in threads:
          thread.start()
  
      for i in range(num_producers * num_items):
          item = queue.get()
          if item == -1:
              num_producers_finished += 1
          if i > num_producers * num_items / 2:
              num_producers_alive = (
                  num_producers - num_producers_finished
              )
              # Check there are some alive producers.
              assert num_producers_alive > 0, num_producers_alive
              # Release the alive producers.
              queue.release(num_producers_alive)
              # Consume the remaining items in queue.
              while queue.qsize() > 0:
                  queue.get()
              break
  
      # Sleep 5 seconds to allow producer threads to exit.
      time.sleep(5)
      # Then check the queue is still empty.
      assert queue.qsize() == 0
  
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 11 for calling a global function with example usage: test_queue() without return types