code: def test_evaluate(self):
      reporter = chainer.Reporter()
      reporter.add_observer("target", self.target)
      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)
  
      # The converter gets results of the iterator.
      self.assertEqual(len(self.converter.args), len(self.data))
      for i in range(len(self.data)):
          numpy.testing.assert_array_equal(
              self.converter.args[i]["batch"], self.data[i]
          )
          self.assertIsNone(self.converter.args[i]["device"])
  
      # The model gets results of converter.
      self.assertEqual(len(self.target.args), len(self.batches))
      for i in range(len(self.batches)):
          numpy.testing.assert_array_equal(
              self.target.args[i], self.batches[i]
          )
  
      self.assertAlmostEqual(
          mean["target/loss"], self.expect_mean, places=4
      )
  
      self.evaluator.finalize()
      self.assertTrue(self.iterator.finalized)
  
imports: import unittest
  import numpy
  import chainer
  from chainer import backend
  from chainer.backends import _cpu
  from chainer import dataset
  from chainer import iterators
  from chainer import testing
  from chainer.training import extensions
label_desc: Use image node_id 2 for calling the TestEvaluator obj's underlying member method code with example usage: obj.test_evaluate() without return types