How to use the tfrecord.iterator_utils function in tfrecord

def __iter__(self):
        worker_info = torch.utils.data.get_worker_info()
        if worker_info is not None:
            shard = worker_info.id, worker_info.num_workers
            np.random.seed(worker_info.seed % np.iinfo(np.uint32).max)
        else:
            shard = None
        it = reader.tfrecord_loader(
            self.data_path, self.index_path, self.description, shard)
        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