old_contents: import numpy as np
  
  from chainer.dataset import dataset_mixin
  
  
  class MixUpSoftLabelDataset(dataset_mixin.DatasetMixin):
  
      """Dataset which returns mixed images and labels for mixup learning [#]_.
  
      :class:`MixUpSoftLabelDataset` mixes two pairs of labeled images fetched
      from the base dataset.
  
      Unlike `LabeledImageDatasets`, label is a one-dimensional float array with
      at most two nonnegative weights (i.e. soft label). The sum of the two
      weights is one.
  
      Example:
  
          We construct a mixup dataset from MNIST.
  
          .. code::
  
              >>> from chainer.datasets import get_mnist
              >>> from chainercv.datasets import SiameseDataset
              >>> from chainercv.datasets import MixUpSoftLabelDataset
              >>> mnist, _ = get_mnist()
              >>> base_dataset = SiameseDataset(mnist, mnist)
              >>> dataset = MixUpSoftLabelDataset(base_dataset, 10)
              >>> mixed_image, mixed_label = dataset[0]
              >>> mixed_label.shape
              (10,)
              >>> mixed_label.dtype
              dtype('float32')
  
      Args:
          dataset: The underlying dataset. The dataset returns :obj:`img_0,
              label_0, img_1, label_1`, which is a tuple containing two pairs
              of an image and a label. Typically, dataset is `SiameseDataset`.
  
              The shapes of images and labels should be constant.
          n_class (int): The number of classes in the base dataset.
  
      .. [#] Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz.
          `mixup: Beyond Empirical Risk Minimization\
          <https://arxiv.org/abs/1710.09412>`_. arXiv 2017.
  
      """
  
      def __init__(self, dataset, n_class):
          self._dataset = dataset
          self._n_class = n_class
  
      def __len__(self):
          return len(self._dataset)
  
      def get_example(self, i):
          image1, label1, image2, label2 = self._dataset[i]
          mix_ratio = np.random.random()
  
          image = mix_ratio * image1 + (1-mix_ratio) * image2
          label = np.zeros(self._n_class, dtype=np.float32)
          label[label1] += mix_ratio
          label[label2] += 1 - mix_ratio
          return image, label
  
new_contents: import numpy as np
  
  from chainer.dataset import dataset_mixin
  
  
  class MixUpSoftLabelDataset(dataset_mixin.DatasetMixin):
  
      """Dataset which returns mixed images and labels for mixup learning [#]_.
  
      :class:`MixUpSoftLabelDataset` mixes two pairs of labeled images fetched
      from the base dataset.
  
      Unlike `LabeledImageDatasets`, label is a one-dimensional float array with
      at most two nonnegative weights (i.e. soft label). The sum of the two
      weights is one.
  
      Example:
  
          We construct a mixup dataset from MNIST.
  
          .. code::
  
              >>> from chainer.datasets import get_mnist
              >>> from chainercv.datasets import SiameseDataset
              >>> from chainercv.datasets import MixUpSoftLabelDataset
              >>> mnist, _ = get_mnist()
              >>> base_dataset = SiameseDataset(mnist, mnist)
              >>> dataset = MixUpSoftLabelDataset(base_dataset, 10)
              >>> mixed_image, mixed_label = dataset[0]
              >>> mixed_label.shape
              (10,)
              >>> mixed_label.dtype
              dtype('float32')
  
      Args:
          dataset: The underlying dataset. The dataset returns :obj:`img_0,
              label_0, img_1, label_1`, which is a tuple containing two pairs
              of an image and a label. Typically, dataset is `SiameseDataset`.
  
              The shapes of images and labels should be constant.
          n_class (int): The number of classes in the base dataset.
  
      .. [#] Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz.
          `mixup: Beyond Empirical Risk Minimization\
          <https://arxiv.org/abs/1710.09412>`_. arXiv 2017.
  
      """
  
      def __init__(self, dataset, n_class):
          self._dataset = dataset
          self._n_class = n_class
  
      def __len__(self):
          return len(self._dataset)
  
      def get_example(self, i):
          img_0, label_0, img_1, label_1 = self._dataset[i]
          mix_ratio = np.random.random()
  
          image = mix_ratio * img_0 + (1-mix_ratio) * img_1
          label = np.zeros(self._n_class, dtype=np.float32)
          label[label_0] += mix_ratio
          label[label_1] += 1 - mix_ratio
          return image, label
  
subject: change argument name as doc