code: def test_evaluate(self):
      reporter = chainer.Reporter()
      reporter.add_observer("target", self.link)
      with reporter:
          mean = self.evaluator.evaluate()
  
      # No observation is reported to the current reporter. Instead the
      # evaluator collect results in order to calculate their mean.
      self.assertEqual(len(reporter.observation), 0)
  
      key = "ap/iou=0.50:0.95/area=all/max_dets=100"
      np.testing.assert_equal(
          mean["target/m{}".format(key)], self.expected_ap
      )
      np.testing.assert_equal(
          mean["target/{}/cls0".format(key)], np.nan
      )
      np.testing.assert_equal(
          mean["target/{}/cls1".format(key)], self.expected_ap
      )
      np.testing.assert_equal(
          mean["target/{}/cls2".format(key)], np.nan
      )
  
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 InstanceSegmentationCOCOEvaluator
  from chainercv.utils.testing import attr
  from chainermn import create_communicator
label_desc: Use image node_id 2 for calling the TestInstanceSegmentationCOCOEvaluator obj's underlying member method code with example usage: obj.test_evaluate() without return types