How to use the d2l.tokenize function in d2l

def load_data_imdb(batch_size, num_steps=500):
    d2l.download_imdb()
    train_data, test_data = d2l.read_imdb('train'), d2l.read_imdb('test')
    train_tokens = d2l.tokenize(train_data[0], token='word')
    test_tokens = d2l.tokenize(test_data[0], token='word')
    vocab = nlp.Vocab(nlp.data.count_tokens(itertools.chain.from_iterable(train_tokens)), min_freq=5)
    train_features = mx.nd.array([d2l.trim_pad(vocab[line], num_steps, vocab[vocab.unknown_token])
                               for line in train_tokens])
    test_features = mx.nd.array([d2l.trim_pad(vocab[line], num_steps, vocab[vocab.unknown_token])
                               for line in test_tokens])
    train_iter = d2l.load_array((train_features, train_data[1]), batch_size)
    test_iter = d2l.load_array((test_features, test_data[1]), batch_size,
                               is_train=False)
    return train_iter, test_iter, vocab

def load_data_imdb(batch_size, num_steps=500):
    d2l.download_imdb()
    train_data, test_data = d2l.read_imdb('train'), d2l.read_imdb('test')
    train_tokens = d2l.tokenize(train_data[0], token='word')
    test_tokens = d2l.tokenize(test_data[0], token='word')
    vocab = nlp.Vocab(nlp.data.count_tokens(itertools.chain.from_iterable(train_tokens)), min_freq=5)
    train_features = mx.nd.array([d2l.trim_pad(vocab[line], num_steps, vocab[vocab.unknown_token])
                               for line in train_tokens])
    test_features = mx.nd.array([d2l.trim_pad(vocab[line], num_steps, vocab[vocab.unknown_token])
                               for line in test_tokens])
    train_iter = d2l.load_array((train_features, train_data[1]), batch_size)
    test_iter = d2l.load_array((test_features, test_data[1]), batch_size,
                               is_train=False)
    return train_iter, test_iter, vocab