Create iterator objects for splits of the WikiText-2 dataset.

        This is the simplest way to use the dataset, and assumes common
        defaults for field, vocabulary, and iterator parameters.

        Arguments:
            batch_size: Batch size.
            bptt_len: Length of sequences for backpropagation through time.
            device: Device to create batches on. Use -1 for CPU and None for
                the currently active GPU device.
            root: The root directory that the dataset's zip archive will be
                expanded into; therefore the directory in whose wikitext-2
                subdirectory the data files will be stored.
            wv_dir, wv_type, wv_dim: Passed to the Vocab constructor for the
                text field. The word vectors are accessible as
                train.dataset.fields['text'].vocab.vectors.
            Remaining keyword arguments: Passed to the splits method.

def iters(cls, batch_size=32, bptt_len=35, device=0, root='.data',
              vectors=None, **kwargs):
        """Create iterator objects for splits of the WikiText-2 dataset.

        This is the simplest way to use the dataset, and assumes common
        defaults for field, vocabulary, and iterator parameters.

        Arguments:
            batch_size: Batch size.
            bptt_len: Length of sequences for backpropagation through time.
            device: Device to create batches on. Use -1 for CPU and None for
                the currently active GPU device.
            root: The root directory that the dataset's zip archive will be
                expanded into; therefore the directory in whose wikitext-2
                subdirectory the data files will be stored.
            wv_dir, wv_type, wv_dim: Passed to the Vocab constructor for the
                text field. The word vectors are accessible as
                train.dataset.fields['text'].vocab.vectors.
            Remaining keyword arguments: Passed to the splits method.
        """
        TEXT = data.Field()

        train, val, test = cls.splits(TEXT, root=root, **kwargs)

        TEXT.build_vocab(train, vectors=vectors)

        return data.BPTTIterator.splits(
            (train, val, test), batch_size=batch_size, bptt_len=bptt_len,
            device=device)