old_contents: import unittest
  
  import numpy
  
  import chainer
  from chainer import link
  from chainer import links
  from chainer import testing
  from chainer.testing import attr
  
  
  class MockPredictor(link.Chain):
  
      def __init__(self, return_shape):
          self.return_shape = return_shape
          super(MockPredictor, self).__init__()
  
      def __call__(self, *xs):
          batchsize = len(xs[0].data)
          y_shape = (batchsize,) + self.return_shape
          y = self.xp.empty(y_shape, dtype=numpy.float32)
          return chainer.Variable(y)
  
  
  class TestClassifier(unittest.TestCase):
  
      def setUp(self):
          self.classifier = links.Classifier(MockPredictor((3,)))
          self.x = numpy.random.uniform(-1, 1, (10, 3)).astype(numpy.float32)
          self.t = numpy.random.randint(3, size=(10,)).astype(numpy.int32)
  
      def check_call(self):
          xp = self.classifier.xp
          x = chainer.Variable(xp.asarray(self.x))
          t = chainer.Variable(xp.asarray(self.t))
          self.classifier(x, t)
  
      def test_call(self):
          self.check_call()
  
      @attr.gpu
      def test_gpu(self):
          self.classifier.to_gpu()
          self.check_call()
  
  
  class TestClassifier2(unittest.TestCase):
  
      def setUp(self):
          self.classifier = links.Classifier(MockPredictor((3,)))
          self.xs = [numpy.random.uniform(-1, 1, (10, 3)).astype(numpy.float32),
                     numpy.random.uniform(-1, 1, (10, 2)).astype(numpy.float32)]
          self.t = numpy.random.randint(3, size=(10,)).astype(numpy.int32)
  
      def check_call(self):
          xp = self.classifier.xp
          xs = [chainer.Variable(xp.asarray(x)) for x in self.xs]
          t = chainer.Variable(xp.asarray(self.t))
          self.classifier(*(xs + [t]))
  
      def test_call(self):
          self.check_call()
  
      @attr.gpu
      def test_gpu(self):
          self.classifier.to_gpu()
          self.check_call()
  
  
  testing.run_module(__name__, __file__)
  
new_contents: import unittest
  
  import numpy
  
  import chainer
  from chainer.functions.evaluation import accuracy
  from chainer import links
  from chainer import testing
  from chainer.testing import attr
  
  
  @testing.parameterize(
      {'compute_accuracy': True},
      {'compute_accuracy': False}
  )
  class TestClassifier(unittest.TestCase):
  
      def setUp(self):
          predictor = links.Linear(10, 3)
          self.link = links.Classifier(predictor)
          self.link.compute_accuracy = self.compute_accuracy
  
          self.x = numpy.random.uniform(-1, 1, (5, 10)).astype(numpy.float32)
          self.t = numpy.random.randint(3, size=(5)).astype(numpy.int32)
  
      def check_call(self):
          xp = self.link.xp
          x = chainer.Variable(xp.asarray(self.x))
          t = chainer.Variable(xp.asarray(self.t))
          loss = self.link(x, t)
  
          self.assertTrue(hasattr(self.link, 'y'))
          self.assertIsNotNone(self.link.y)
  
          self.assertTrue(hasattr(self.link, 'loss'))
          xp.testing.assert_allclose(self.link.loss.data, loss.data)
  
          self.assertTrue(hasattr(self.link, 'accuracy'))
          if self.compute_accuracy:
              self.assertIsNotNone(self.link.accuracy)
          else:
              self.assertIsNone(self.link.accuracy)
  
      def test_call_cpu(self):
          self.check_call()
  
      @attr.gpu
      def test_call_gpu(self):
          self.link.to_gpu()
          self.check_call()
  
  
  testing.run_module(__name__, __file__)
  
subject: Fix test code