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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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)