How to use the babi.read_data.read_data function in babi

def _main(config, num_trials):
    load_metadata(config)

    # Load data
    if config.train:
        comb_train_ds = read_data(config, 'train', config.task)
        comb_dev_ds = read_data(config, 'dev', config.task)
    test_task = config.task if not config.task == 'joint' else 'all'
    comb_test_ds = read_data(config, 'test', test_task)

    # For quick draft initialize (deubgging).
    if config.draft:
        config.train_num_batches = 1
        config.val_num_batches = 1
        config.test_num_batches = 1
        config.num_epochs = 2
        config.val_period = 1
        config.save_period = 1
        # TODO : Add any other parameter that induces a lot of computations

    pprint(config.__dict__)

def _main(config, num_trials):
    load_metadata(config)

    # Load data
    if config.train:
        comb_train_ds = read_data(config, 'train', config.task)
        comb_dev_ds = read_data(config, 'dev', config.task)
    test_task = config.task if not config.task == 'joint' else 'all'
    comb_test_ds = read_data(config, 'test', test_task)

    # For quick draft initialize (deubgging).
    if config.draft:
        config.train_num_batches = 1
        config.val_num_batches = 1
        config.test_num_batches = 1
        config.num_epochs = 2
        config.val_period = 1
        config.save_period = 1
        # TODO : Add any other parameter that induces a lot of computations

    pprint(config.__dict__)

    # TODO : specify eval tensor names to save in evals folder

def _main(config, num_trials):
    load_metadata(config)

    # Load data
    if config.train:
        comb_train_ds = read_data(config, 'train', config.task)
        comb_dev_ds = read_data(config, 'dev', config.task)
    test_task = config.task if not config.task == 'joint' else 'all'
    comb_test_ds = read_data(config, 'test', test_task)

    # For quick draft initialize (deubgging).
    if config.draft:
        config.train_num_batches = 1
        config.val_num_batches = 1
        config.test_num_batches = 1
        config.num_epochs = 2
        config.val_period = 1
        config.save_period = 1
        # TODO : Add any other parameter that induces a lot of computations

    pprint(config.__dict__)

    # TODO : specify eval tensor names to save in evals folder
    eval_tensor_names = ['a', 'rf', 'rb', 'correct', 'yp']
    eval_ph_names = ['q', 'q_mask', 'x', 'x_mask', 'y']