old_contents: import unittest
  
  import numpy as np
  
  from chainer import testing
  from chainer.testing import attr
  from chainer import Variable
  
  from chainercv.links import ResNet101
  from chainercv.links import ResNet152
  from chainercv.links import ResNet50
  
  
  @testing.parameterize(*(
      testing.product_dict(
          [
              {'pick': 'prob', 'shapes': (1, 200), 'n_class': 200},
              {'pick': 'res5',
               'shapes': (1, 2048, 7, 7), 'n_class': None},
              {'pick': ['res2', 'conv1'],
               'shapes': ((1, 256, 56, 56), (1, 64, 112, 112)), 'n_class': None},
          ],
          [
              {'model_class': ResNet50},
              {'model_class': ResNet101},
              {'model_class': ResNet152},
          ],
          [
              {'mode': 'fb'},
              {'mode': 'he'}
          ]
      )
  ))
  class TestResNetCall(unittest.TestCase):
  
      def setUp(self):
          self.link = self.model_class(
              n_class=self.n_class, pretrained_model=None, mode=self.mode)
          self.link.pick = self.pick
  
      def check_call(self):
          xp = self.link.xp
  
          x1 = Variable(xp.asarray(np.random.uniform(
              -1, 1, (1, 3, 224, 224)).astype(np.float32)))
          features = self.link(x1)
          if isinstance(features, tuple):
              for activation, shape in zip(features, self.shapes):
                  self.assertEqual(activation.shape, shape)
          else:
              self.assertEqual(features.shape, self.shapes)
              self.assertEqual(features.dtype, np.float32)
  
      @attr.slow
      def test_call_cpu(self):
          self.check_call()
  
      @attr.gpu
      @attr.slow
      def test_call_gpu(self):
          self.link.to_gpu()
          self.check_call()
  
  
  testing.run_module(__name__, __file__)
  
new_contents: import unittest
  
  import numpy as np
  
  from chainer import testing
  from chainer.testing import attr
  from chainer import Variable
  
  from chainercv.links import ResNet101
  from chainercv.links import ResNet152
  from chainercv.links import ResNet50
  
  
  @testing.parameterize(*(
      testing.product_dict(
          [
              {'pick': 'prob', 'shapes': (1, 200), 'n_class': 200},
              {'pick': 'res5',
               'shapes': (1, 2048, 7, 7), 'n_class': None},
              {'pick': ['res2', 'conv1'],
               'shapes': ((1, 256, 56, 56), (1, 64, 112, 112)), 'n_class': None},
          ],
          [
              {'model_class': ResNet50},
              {'model_class': ResNet101},
              {'model_class': ResNet152},
          ],
          [
              {'mode': 'fb'},
              {'mode': 'he'}
          ]
      )
  ))
  class TestResNetCall(unittest.TestCase):
  
      def setUp(self):
          self.link = self.model_class(
              n_class=self.n_class, pretrained_model=None, mode=self.mode)
          self.link.pick = self.pick
  
      def check_call(self):
          xp = self.link.xp
  
          x = Variable(xp.asarray(np.random.uniform(
              -1, 1, (1, 3, 224, 224)).astype(np.float32)))
          features = self.link(x)
          if isinstance(features, tuple):
              for activation, shape in zip(features, self.shapes):
                  self.assertEqual(activation.shape, shape)
          else:
              self.assertEqual(features.shape, self.shapes)
              self.assertEqual(features.dtype, np.float32)
  
      @attr.slow
      def test_call_cpu(self):
          self.check_call()
  
      @attr.gpu
      @attr.slow
      def test_call_gpu(self):
          self.link.to_gpu()
          self.check_call()
  
  
  testing.run_module(__name__, __file__)
  
subject: fix variable name