How to use the tfrecord.reader.multi_tfrecord_loader function in tfrecord

def __iter__(self):
        worker_info = torch.utils.data.get_worker_info()
        if worker_info is not None:
            np.random.seed(worker_info.seed % np.iinfo(np.uint32).max)
        it = reader.multi_tfrecord_loader(
            self.data_pattern, self.index_pattern, self.splits, self.description)
        if self.shuffle_queue_size:
            it = iterator_utils.shuffle_iterator(it, self.shuffle_queue_size)
        if self.transform:
            it = map(self.transform, it)
        return it