old_contents: import unittest
  
  import numpy as np
  
  from chainer import testing
  from chainercv.transforms import resize_contain
  
  
  @testing.parameterize(*testing.product_dict(
      [
          {'size': (48, 96), 'scaled_size': (32, 64),
           'y_offset': 8, 'x_offset': 16},
          {'size': (16, 68), 'scaled_size': (16, 32),
           'y_offset': 0, 'x_offset': 18},
          {'size': (24, 16), 'scaled_size': (8, 16),
           'y_offset': 8, 'x_offset': 0},
          {'size': (47, 97), 'scaled_size': (32, 64),
           'y_offset': 7, 'x_offset': 16},
      ],
      [
          {'fill': 128},
          {'fill': (104, 117, 123)},
          {'fill': np.random.uniform(255, size=(3, 1, 1))},
      ]
  ))
  class TestResizeContain(unittest.TestCase):
  
      def test_resize_contain(self):
          H, W = 32, 64
          img = np.random.uniform(255, size=(3, H, W))
  
          out, param = resize_contain(
              img, self.size, fill=self.fill, return_param=True)
  
          self.assertEqual(param['scaled_size'], self.scaled_size)
          self.assertEqual(param['y_offset'], self.y_offset)
          self.assertEqual(param['x_offset'], self.x_offset)
  
          if self.scaled_size == (H, W):
              np.testing.assert_array_equal(
                  out[:,
                      self.y_offset:self.y_offset + H,
                      self.x_offset:self.x_offset + W],
                  img)
  
          if self.y_offset > 0 or self.x_offset > 0:
              if isinstance(self.fill, int):
                  fill = (self.fill,) * 3
              else:
                  fill = self.fill
                  np.testing.assert_array_equal(
                      out[:, 0, 0], np.array(fill).flatten())
  
  
  testing.run_module(__name__, __file__)
  
new_contents: import unittest
  
  import numpy as np
  
  from chainer import testing
  from chainercv.transforms import resize_contain
  
  
  @testing.parameterize(*testing.product_dict(
      [
          {'size': (48, 96), 'scaled_size': (32, 64),
           'y_offset': 8, 'x_offset': 16},
          {'size': (16, 68), 'scaled_size': (16, 32),
           'y_offset': 0, 'x_offset': 18},
          {'size': (24, 16), 'scaled_size': (8, 16),
           'y_offset': 8, 'x_offset': 0},
          {'size': (47, 97), 'scaled_size': (32, 64),
           'y_offset': 7, 'x_offset': 16},
      ],
      [
          {'fill': 128},
          {'fill': (104, 117, 123)},
          {'fill': np.random.uniform(255, size=(3, 1, 1))},
      ]
  ))
  class TestResizeContain(unittest.TestCase):
  
      def test_resize_contain(self):
          H, W = 32, 64
          img = np.random.uniform(255, size=(3, H, W))
  
          out, param = resize_contain(
              img, self.size, fill=self.fill, return_param=True)
  
          self.assertEqual(param['scaled_size'], self.scaled_size)
          self.assertEqual(param['y_offset'], self.y_offset)
          self.assertEqual(param['x_offset'], self.x_offset)
  
          if self.scaled_size == (H, W):
              np.testing.assert_array_equal(
                  out[:,
                      self.y_offset:self.y_offset + H,
                      self.x_offset:self.x_offset + W],
                  img)
  
          if self.y_offset > 0 or self.x_offset > 0:
              if isinstance(self.fill, int):
                  fill = (self.fill,) * 3
              else:
                  fill = self.fill
              np.testing.assert_array_equal(
                  out[:, 0, 0], np.array(fill).flatten())
  
  
  testing.run_module(__name__, __file__)
  
subject: fix indent