How to use the reader.DataReader function in reader

help="The initial value for loss scaling.")
    parser.add_argument(
        "--fetch_steps",
        type=int,
        default=100,
        help="The frequency to fetch and print output.")
    parser.add_argument(
        "--num_epochs",
        type=int,
        default=1,
        help="How many epochs to run.")

    args = parser.parse_args()
    # Append args related to dict
    src_dict = reader.DataReader.load_dict(args.src_vocab_fpath)
    trg_dict = reader.DataReader.load_dict(args.trg_vocab_fpath)
    dict_args = [
        "src_vocab_size", str(len(src_dict)), "trg_vocab_size",
        str(len(trg_dict)), "bos_idx", str(src_dict[args.special_token[0]]),
        "eos_idx", str(src_dict[args.special_token[1]]), "unk_idx",
        str(src_dict[args.special_token[2]])
    ]
    merge_cfg_from_list(args.opts + dict_args,
                        [TrainTaskConfig, ModelHyperParams])
    return args

type=ast.literal_eval,
        default=True,
        help="The flag indicating whether to use py_reader.")
    parser.add_argument(
        "--use_parallel_exe",
        type=ast.literal_eval,
        default=False,
        help="The flag indicating whether to use ParallelExecutor.")
    parser.add_argument(
        'opts',
        help='See config.py for all options',
        default=None,
        nargs=argparse.REMAINDER)
    args = parser.parse_args()
    # Append args related to dict
    src_dict = reader.DataReader.load_dict(args.src_vocab_fpath)
    trg_dict = reader.DataReader.load_dict(args.trg_vocab_fpath)
    phone_dict = reader.DataReader.load_dict(args.phoneme_vocab_fpath)
    dict_args = [
        "src_vocab_size", str(len(src_dict)), "trg_vocab_size",
        str(len(trg_dict)), "phone_vocab_size", str(len(phone_dict)), "bos_idx",
        str(src_dict[args.special_token[0]]), "eos_idx",
        str(src_dict[args.special_token[1]]), "unk_idx",
        str(src_dict[args.special_token[2]])
    ]
    merge_cfg_from_list(args.opts + dict_args,
                        [InferTaskConfig, ModelHyperParams])
    return args

def prepare_data_generator(args, is_test, count, pyreader):
    """
    Data generator wrapper for DataReader. If use py_reader, set the data
    provider for py_reader
    """
    data_reader = reader.DataReader(
        fpattern=args.val_file_pattern if is_test else args.train_file_pattern,
        src_vocab_fpath=args.src_vocab_fpath,
        trg_vocab_fpath=args.trg_vocab_fpath,
        token_delimiter=args.token_delimiter,
        use_token_batch=args.use_token_batch,
        batch_size=args.batch_size * (1 if args.use_token_batch else count),
        pool_size=args.pool_size,
        sort_type=args.sort_type,
        shuffle=args.shuffle,
        shuffle_batch=args.shuffle_batch,
        start_mark=args.special_token[0],
        end_mark=args.special_token[1],
        unk_mark=args.special_token[2],
        # count start and end tokens out
        max_length=ModelHyperParams.max_length - 2,
        clip_last_batch=False).batch_generator

def prepare_data_generator(args,
                           is_test,
                           count,
                           pyreader,
                           py_reader_provider_wrapper,
                           place=None):
    """
    Data generator wrapper for DataReader. If use py_reader, set the data
    provider for py_reader
    """
    data_reader = reader.DataReader(
        fpattern=args.val_file_pattern if is_test else args.train_file_pattern,
        src_vocab_fpath=args.src_vocab_fpath,
        trg_vocab_fpath=args.trg_vocab_fpath,
        token_delimiter=args.token_delimiter,
        use_token_batch=args.use_token_batch,
        batch_size=args.batch_size * (1 if args.use_token_batch else count),
        pool_size=args.pool_size,
        sort_type=args.sort_type,
        shuffle=args.shuffle,
        shuffle_batch=args.shuffle_batch,
        start_mark=args.special_token[0],
        end_mark=args.special_token[1],
        unk_mark=args.special_token[2],
        # count start and end tokens out
        max_length=ModelHyperParams.max_length - 2,
        clip_last_batch=False).batch_generator

def prepare_data_generator(args,
                           is_test,
                           count,
                           pyreader,
                           py_reader_provider_wrapper,
                           place=None):
    """
    Data generator wrapper for DataReader. If use py_reader, set the data
    provider for py_reader
    """
    data_reader = reader.DataReader(
        fpattern=args.val_file_pattern if is_test else args.train_file_pattern,
        src_vocab_fpath=args.src_vocab_fpath,
        trg_vocab_fpath=args.trg_vocab_fpath,
        token_delimiter=args.token_delimiter,
        use_token_batch=args.use_token_batch,
        batch_size=args.batch_size * (1 if args.use_token_batch else count),
        pool_size=args.pool_size,
        sort_type=args.sort_type,
        shuffle=args.shuffle,
        shuffle_batch=args.shuffle_batch,
        start_mark=args.special_token[0],
        end_mark=args.special_token[1],
        unk_mark=args.special_token[2],
        # count start and end tokens out
        max_length=ModelHyperParams.max_length - 2,
        clip_last_batch=False).batch_generator

default=True,
        help="The flag indicating whether to use memory optimization.")
    parser.add_argument(
        "--use_py_reader",
        type=ast.literal_eval,
        default=True,
        help="The flag indicating whether to use py_reader.")
    parser.add_argument(
        "--fetch_steps",
        type=int,
        default=100,
        help="The frequency to fetch and print output.")

    args = parser.parse_args()
    # Append args related to dict
    src_dict = reader.DataReader.load_dict(args.src_vocab_fpath)
    trg_dict = reader.DataReader.load_dict(args.trg_vocab_fpath)
    dict_args = [
        "src_vocab_size", str(len(src_dict)), "trg_vocab_size",
        str(len(trg_dict)), "bos_idx", str(src_dict[args.special_token[0]]),
        "eos_idx", str(src_dict[args.special_token[1]]), "unk_idx",
        str(src_dict[args.special_token[2]])
    ]
    merge_cfg_from_list(args.opts + dict_args,
                        [TrainTaskConfig, ModelHyperParams])
    return args

type=ast.literal_eval,
        default=True,
        help="The flag indicating whether to use py_reader.")
    parser.add_argument(
        "--use_parallel_exe",
        type=ast.literal_eval,
        default=False,
        help="The flag indicating whether to use ParallelExecutor.")
    parser.add_argument(
        'opts',
        help='See config.py for all options',
        default=None,
        nargs=argparse.REMAINDER)
    args = parser.parse_args()
    # Append args related to dict
    src_dict = reader.DataReader.load_dict(args.src_vocab_fpath)
    trg_dict = reader.DataReader.load_dict(args.trg_vocab_fpath)
    dict_args = [
        "src_vocab_size", str(len(src_dict)), "trg_vocab_size",
        str(len(trg_dict)), "bos_idx", str(src_dict[args.special_token[0]]),
        "eos_idx", str(src_dict[args.special_token[1]]), "unk_idx",
        str(src_dict[args.special_token[2]])
    ]
    merge_cfg_from_list(args.opts + dict_args,
                        [InferTaskConfig, ModelHyperParams])
    return args

help="The iteration number to run in profiling.")
    parser.add_argument(
        "--use_parallel_exe",
        type=bool,
        default=False,
        help="The flag indicating whether to use ParallelExecutor.")
    parser.add_argument(
        'opts',
        help='See config.py for all options',
        default=None,
        nargs=argparse.REMAINDER)

    args = parser.parse_args()
    # Append args related to dict
    src_dict = reader.DataReader.load_dict(args.src_vocab_fpath)
    trg_dict = reader.DataReader.load_dict(args.trg_vocab_fpath)
    dict_args = [
        "src_vocab_size", str(len(src_dict)), "trg_vocab_size",
        str(len(trg_dict)), "bos_idx", str(src_dict[args.special_token[0]]),
        "eos_idx", str(src_dict[args.special_token[1]]), "unk_idx",
        str(src_dict[args.special_token[2]])
    ]
    merge_cfg_from_list(args.opts + dict_args,
                        [TrainTaskConfig, ModelHyperParams])
    return args