code: def test_evaluate(self, backend_config):
      data = backend_config.get_array(self.data)
      batches = [backend_config.get_array(b) for b in self.batches]
      device = backend_config.device
  
      iterator, converter, target, evaluator = self.prepare(
          data, batches, device
      )
  
      reporter = chainer.Reporter()
      reporter.add_observer("target", target)
      with reporter:
          mean = evaluator.evaluate()
  
      # The converter gets results of the iterator and the device number.
      self.assertEqual(len(converter.args), len(data))
      if backend_config.use_cuda:
          expected_device_arg = backend_config.cuda_device
      else:
          expected_device_arg = -1
  
      for i in range(len(data)):
          numpy.testing.assert_array_equal(
              _cpu._to_cpu(converter.args[i]["batch"]), self.data[i]
          )
          self.assertEqual(
              converter.args[i]["device"], expected_device_arg
          )
  
      # The model gets results of converter.
      self.assertEqual(len(target.args), len(batches))
      for i in range(len(batches)):
          numpy.testing.assert_array_equal(
              _cpu._to_cpu(target.args[i]), self.batches[i]
          )
  
      expect_mean = numpy.mean([numpy.sum(x) for x in self.batches])
      self.assertAlmostEqual(
          _cpu._to_cpu(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 3 for calling the TestEvaluatorTupleData obj's underlying member method code with example usage: obj.test_evaluate(backend_config) without return types