code: def test_rebatching_queue_closes_properly(self):
      net = core.Net("net")
      workspace.FeedBlob(
          "tensors", np.array([x for x in range(10)], np.int32)
      )
  
      queue = net.CreateRebatchingQueue([], 1, capacity=10, num_blobs=1)
  
      net.EnqueueRebatchingQueue(
          [queue, "tensors"], 0, enqueue_batch=True
      )
  
      net.CloseRebatchingQueue([queue], 0)
  
      results = [
          net.DequeueRebatchingQueue([queue], 1, num_elements=5),
          net.DequeueRebatchingQueue([queue], 1, num_elements=5),
      ]
  
      workspace.RunNetOnce(net)
  
      npt.assert_array_equal(
          workspace.FetchBlob(results[0]),
          workspace.FetchBlob("tensors")[:5],
      )
      npt.assert_array_equal(
          workspace.FetchBlob(results[1]),
          workspace.FetchBlob("tensors")[5:],
      )
  
      # Enqueuing more should fail now since the queue is closed
      net.EnqueueRebatchingQueue(
          [queue, "tensors"], [], enqueue_batch=True
      )
  
      with self.assertRaises(RuntimeError):
          workspace.RunNetOnce(net)
  
      # Dequeuing more should fail now since the queue is closed
      results = [
          net.DequeueRebatchingQueue([queue], 1, num_elements=5),
      ]
  
      with self.assertRaises(RuntimeError):
          workspace.RunNetOnce(net)
  
file_path: pytorch/caffe2/python/operator_test/rebatching_queue_test.py
imports: from caffe2.python import core, workspace
  from caffe2.python.test_util import TestCase
  import numpy
  import numpy.testing
  from hypothesis import given, settings
  import hypothesis.strategies
  import functools
label_desc: Use image node_id 3 for calling the TestReBatchingQueue obj's underlying member method code with example usage: obj.test_rebatching_queue_closes_properly() without return types