Create iterator objects for splits of the SST dataset.

        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 trees
                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)
            Remaining keyword arguments: Passed to the splits method.

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

        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 trees
                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)
            Remaining keyword arguments: Passed to the splits method.
        """
        TEXT = data.Field()
        LABEL = data.Field(sequential=False)

        train, val, test = cls.splits(TEXT, LABEL, 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)