code: def test_consistency(self):
      reporter = chainer.Reporter()
  
      if self.comm.rank == 0:
          multi_iterator = SerialIterator(
              self.dataset, self.batchsize, repeat=False, shuffle=False
          )
      else:
          multi_iterator = None
      multi_link = _InstanceSegmentationStubLink(
          self.masks, self.labels, self.initial_count
      )
      multi_evaluator = InstanceSegmentationVOCEvaluator(
          multi_iterator,
          multi_link,
          label_names=("cls0", "cls1", "cls2"),
          comm=self.comm,
      )
      reporter.add_observer("target", multi_link)
      with reporter:
          multi_mean = multi_evaluator.evaluate()
  
      if self.comm.rank != 0:
          self.assertEqual(multi_mean, {})
          return
  
      single_iterator = SerialIterator(
          self.dataset, self.batchsize, repeat=False, shuffle=False
      )
      single_link = _InstanceSegmentationStubLink(
          self.masks, self.labels
      )
      single_evaluator = InstanceSegmentationVOCEvaluator(
          single_iterator,
          single_link,
          label_names=("cls0", "cls1", "cls2"),
      )
      reporter.add_observer("target", single_link)
      with reporter:
          single_mean = single_evaluator.evaluate()
  
      self.assertEqual(set(multi_mean.keys()), set(single_mean.keys()))
      for key in multi_mean.keys():
          np.testing.assert_equal(single_mean[key], multi_mean[key])
  
imports: import numpy
  import unittest
  import chainer
  from chainer.datasets import TupleDataset
  from chainer.iterators import SerialIterator
  from chainer import testing
  from chainercv.extensions import InstanceSegmentationVOCEvaluator
  from chainercv.utils.testing import attr
  from chainermn import create_communicator
label_desc: Use image node_id 2 for calling the TestInstanceSegmentationVOCEvaluatorMPI obj's underlying member method code with example usage: obj.test_consistency() without return types