old_contents: class Iterator(object):
  
      """Base class of all dataset iterators.
  
      Iterator iterates over the dataset, yielding a minibatch at each
      iteration. Minibatch is a list of examples. Each implementation should
      implement an iterator protocol (e.g., the :meth:`__next__` method).
  
      Note that, even if the iterator supports setting the batch size, it does
      not guarantee that each batch always contains the same number of examples.
      For example, if you let the iterator to stop at the end of the sweep, the
      last batch may contain a fewer number of examples.
  
      The interface between the iterator and the underlying dataset is not fixed,
      and up to the implementation.
  
      Each implementation should provide the following attributes (or possibly
      read-only properties).
  
      - ``batch_size``: the number of examples within each minibatch.
      - ``epoch``: the number of completed sweeps over the dataset.
      - ``epoch_detail``: floating point number version of the epoch. For
        example, if the iterator is at the middle of the dataset at the third
        epoch, then this value is 2.5.
      - ``is_new_epoch``: True if the epoch count was incremented at the last
        update.
  
      Each implementation should also support serialization to resume/suspend the
      iteration.
  
      """
      def __iter__(self):
          """Returns self."""
          return self
  
      def __next__(self):
          """Returns the next batch.
  
          This is a part of the iterator protocol of Python. It may raise the
          :class:`StopIteration` exception when it stops the iteration.
  
          """
          raise NotImplementedError
  
      def next(self):
          """Python2 alternative of ``__next__``.
  
          It calls :meth:`__next__` by default.
          """
          return self.__next__()
  
      def finalize(self):
          """Finalizes the iterator and possibly releases the resources.
  
          This method does nothing by default. Implementation may override it to
          better handle the internal resources.
  
          """
          pass
  
      def serialize(self, serializer):
          """Serializes the internal state of the iterator.
  
          This is a method to support serializer protocol of Chainer.
  
          .. note::
             It should only serialize the internal state that changes over the
             iteration. It should not serializes what is set manually by
             users such as the batch size.
  
          """
          pass
  
new_contents: class Iterator(object):
  
      """Base class of all dataset iterators.
  
      Iterator iterates over the dataset, yielding a minibatch at each
      iteration. Minibatch is a list of examples. Each implementation should
      implement an iterator protocol (e.g., the :meth:`__next__` method).
  
      Note that, even if the iterator supports setting the batch size, it does
      not guarantee that each batch always contains the same number of examples.
      For example, if you let the iterator to stop at the end of the sweep, the
      last batch may contain a fewer number of examples.
  
      The interface between the iterator and the underlying dataset is not fixed,
      and up to the implementation.
  
      Each implementation should provide the following attributes (not needed to
      be writable).
  
      - ``batch_size``: the number of examples within each minibatch.
      - ``epoch``: the number of completed sweeps over the dataset.
      - ``epoch_detail``: floating point number version of the epoch. For
        example, if the iterator is at the middle of the dataset at the third
        epoch, then this value is 2.5.
      - ``is_new_epoch``: True if the epoch count was incremented at the last
        update.
  
      Each implementation should also support serialization to resume/suspend the
      iteration.
  
      """
      def __iter__(self):
          """Returns self."""
          return self
  
      def __next__(self):
          """Returns the next batch.
  
          This is a part of the iterator protocol of Python. It may raise the
          :class:`StopIteration` exception when it stops the iteration.
  
          """
          raise NotImplementedError
  
      def next(self):
          """Python2 alternative of ``__next__``.
  
          It calls :meth:`__next__` by default.
          """
          return self.__next__()
  
      def finalize(self):
          """Finalizes the iterator and possibly releases the resources.
  
          This method does nothing by default. Implementation may override it to
          better handle the internal resources.
  
          """
          pass
  
      def serialize(self, serializer):
          """Serializes the internal state of the iterator.
  
          This is a method to support serializer protocol of Chainer.
  
          .. note::
             It should only serialize the internal state that changes over the
             iteration. It should not serializes what is set manually by
             users such as the batch size.
  
          """
          pass
  
subject: Fix expression in docstring