old_contents: import unittest
  
  import numpy as np
  
  from chainer import testing
  from chainer.testing import attr
  from chainer.testing import condition
  
  from chainercv.datasets import voc_detection_label_names
  from chainercv.datasets import VOCDetectionDataset
  from chainercv.utils import assert_is_detection_dataset
  
  
  def _create_paramters():
      split_years = testing.product({
          'split': ['train', 'trainval', 'val'],
          'year': ['2007', '2012']})
      split_years += [{'split': 'test', 'year': '2007'}]
      params = testing.product_dict(
          split_years,
          [{'use_difficult': True, 'return_difficult': True},
           {'use_difficult': True, 'return_difficult': False},
           {'use_difficult': False, 'return_difficult': True},
           {'use_difficult': False, 'return_difficult': False}])
      return params
  
  
  @testing.parameterize(*_create_paramters())
  class TestVOCDetectionDataset(unittest.TestCase):
  
      def setUp(self):
          self.dataset = VOCDetectionDataset(
              split=self.split,
              year=self.year,
              use_difficult=self.use_difficult,
              return_difficult=self.return_difficult)
          self.n_out = 4 if self.return_difficult else 3
  
      @attr.slow
      def test_as_detection_dataset(self):
          assert_is_detection_dataset(
              self.dataset, len(voc_detection_label_names))
  
      @attr.slow
      @condition.repeat(10)
      def test_difficult(self):
          if not self.return_difficult:
              return
  
          i = np.random.randint(0, len(self.dataset))
          _, bbox, _, difficult = self.dataset[i]
          self.assertIsInstance(difficult, np.ndarray)
          self.assertEqual(difficult.dtype, np.bool)
          self.assertEqual(difficult.shape, (bbox.shape[0],))
  
          if not self.use_difficult:
              np.testing.assert_equal(difficult, 0)
  
  
  testing.run_module(__name__, __file__)
  
new_contents: import unittest
  
  import numpy as np
  
  from chainer import testing
  from chainer.testing import attr
  from chainer.testing import condition
  
  from chainercv.datasets import voc_detection_label_names
  from chainercv.datasets import VOCDetectionDataset
  from chainercv.utils import assert_is_detection_dataset
  
  
  def _create_paramters():
      split_years = testing.product({
          'split': ['train', 'trainval', 'val'],
          'year': ['2007', '2012']})
      split_years += [{'split': 'test', 'year': '2007'}]
      params = testing.product_dict(
          split_years,
          [{'use_difficult': True, 'return_difficult': True},
           {'use_difficult': True, 'return_difficult': False},
           {'use_difficult': False, 'return_difficult': True},
           {'use_difficult': False, 'return_difficult': False}])
      return params
  
  
  @testing.parameterize(*_create_paramters())
  class TestVOCDetectionDataset(unittest.TestCase):
  
      def setUp(self):
          self.dataset = VOCDetectionDataset(
              split=self.split,
              year=self.year,
              use_difficult=self.use_difficult,
              return_difficult=self.return_difficult)
          self.n_out = 4 if self.return_difficult else 3
  
      @attr.slow
      def test_as_detection_dataset(self):
          assert_is_detection_dataset(
              self.dataset, len(voc_detection_label_names), n_example=10)
  
      @attr.slow
      @condition.repeat(10)
      def test_difficult(self):
          if not self.return_difficult:
              return
  
          i = np.random.randint(0, len(self.dataset))
          _, bbox, _, difficult = self.dataset[i]
          self.assertIsInstance(difficult, np.ndarray)
          self.assertEqual(difficult.dtype, np.bool)
          self.assertEqual(difficult.shape, (bbox.shape[0],))
  
          if not self.use_difficult:
              np.testing.assert_equal(difficult, 0)
  
  
  testing.run_module(__name__, __file__)
  
subject: use n_example