code: def test_evaluate(self):
      reporter = chainer.Reporter()
      reporter.add_observer("target", self.target)
      with reporter:
          mean = self.evaluator.evaluate()
  
      # 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][0], self.batches[i]["x"]
          )
          numpy.testing.assert_array_equal(
              self.target.args[i][1], self.batches[i]["y"]
          )
  
      expect_mean = numpy.mean(
          [numpy.sum(x["x"]) + numpy.sum(x["y"]) for x in self.batches]
      )
      self.assertAlmostEqual(mean["target/loss"], expect_mean, places=4)
  
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 TestEvaluatorDictData obj's underlying member method code with example usage: obj.test_evaluate() without return types