old_contents: from chainercv.chainer_experimental.datasets.sliceable import GetterDataset
  
  
  class TransformDataset(GetterDataset):
      """A sliceable version of :class:`chainer.datasets.TransformDataset`.
  
      Note that it reuqires :obj:`keys` to determine the names of returned
      values.
  
      Here is an example.
  
      >>> def transfrom(in_data):
      >>>     img, bbox, label = in_data
      >>>     ...
      >>>     return new_img, new_label
      >>>
      >>> dataset = TramsformDataset(dataset, ('img', 'label'), transform)
      >>> dataset.keys  # ('img', 'label')
  
      Args:
          dataset: The underlying dataset.
              This dataset should have :meth:`__len__` and :meth:`__getitem__`.
          keys (int or string or tuple of strings): The number or name(s) of
              data that the transform function returns.
          transform (callable): A function that is called to transform values
              returned by the underlying dataset's :meth:`__getitem__`.
      """
  
      def __init__(self, dataset, keys, transform):
          super(TransformDataset, self).__init__()
          self._dataset = dataset
          if isinstance(keys, int):
              if keys == 1:
                  keys = None
              else:
                  keys = (None,) * keys
          self.add_getter(keys, lambda index: transform(dataset[index]))
  
      def __len__(self):
          return len(self._dataset)
  
new_contents: from chainercv.chainer_experimental.datasets.sliceable import GetterDataset
  
  
  class TransformDataset(GetterDataset):
      """A sliceable version of :class:`chainer.datasets.TransformDataset`.
  
      Note that it reuqires :obj:`keys` to determine the names of returned
      values.
  
      Here is an example.
  
      >>> def transfrom(in_data):
      >>>     img, bbox, label = in_data
      >>>     ...
      >>>     return new_img, new_label
      >>>
      >>> dataset = TramsformDataset(dataset, ('img', 'label'), transform)
      >>> dataset.keys  # ('img', 'label')
  
      Args:
          dataset: The underlying dataset.
              This dataset should have :meth:`__len__` and :meth:`__getitem__`.
          keys (int or string or tuple of strings): The number or name(s) of
              data that the transform function returns.
          transform (callable): A function that is called to transform values
              returned by the underlying dataset's :meth:`__getitem__`.
      """
  
      def __init__(self, dataset, keys, transform):
          super(TransformDataset, self).__init__()
          self._dataset = dataset
          self._transfrom = transform
          if isinstance(keys, int):
              if keys == 1:
                  keys = None
              else:
                  keys = (None,) * keys
          self.add_getter(keys, self._get)
  
      def __len__(self):
          return len(self._dataset)
  
      def _get(self, index):
          return self._transform(self._dataset[index])
  
subject: remove lambda