Create iterator objects for splits of the SNLI 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.
            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.
            vectors: one of the available pretrained vectors or a list with each
                element one of the available pretrained vectors (see Vocab.load_vectors)
            trees: Whether to include shift-reduce parser transitions.
                Default: False.
            Remaining keyword arguments: Passed to the splits method.

def iters(cls, batch_size=32, device=0, root='.data',
              vectors=None, trees=False, **kwargs):
        """Create iterator objects for splits of the SNLI 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.
            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.
            vectors: one of the available pretrained vectors or a list with each
                element one of the available pretrained vectors (see Vocab.load_vectors)
            trees: Whether to include shift-reduce parser transitions.
                Default: False.
            Remaining keyword arguments: Passed to the splits method.
        """
        if trees:
            TEXT = ParsedTextField()
            TRANSITIONS = ShiftReduceField()
        else:
            TEXT = data.Field(tokenize='spacy')
            TRANSITIONS = None
        LABEL = data.Field(sequential=False)

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

        TEXT.build_vocab(train, vectors=vectors)
        LABEL.build_vocab(train)

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