import tensorflow as tf

    parser.add_argument('--bpe_path', type=str, default='model/vocab_40000.bpe')
    parser.add_argument('--n_transfer', type=int, default=12)
    parser.add_argument('--lm_coef', type=float, default=0.5)
    parser.add_argument('--b1', type=float, default=0.9)
    parser.add_argument('--b2', type=float, default=0.999)
    parser.add_argument('--e', type=float, default=1e-8)

    args = parser.parse_args()
    print(args)
    globals().update(args.__dict__)
    random.seed(seed)
    np.random.seed(seed)
    tf.set_random_seed(seed)

    logger = ResultLogger(path=os.path.join(log_dir, '{}.jsonl'.format(desc)), **args.__dict__)
    text_encoder = TextEncoder(encoder_path, bpe_path)
    encoder = text_encoder.encoder
    n_vocab = len(text_encoder.encoder)

    (trX1, trX2, trX3, trY), (vaX1, vaX2, vaX3, vaY), (teX1, teX2, teX3) = encode_dataset(rocstories(data_dir), encoder=text_encoder)
    n_y = 2
    encoder['_start_'] = len(encoder)
    encoder['_delimiter_'] = len(encoder)
    encoder['_classify_'] = len(encoder)
