    def batch_to_cache(self, cache: List[torch.Tensor]) -> List[List[torch.Tensor]]:
        """
        Args:
            cache : [history]
                history: [bs, ...]
        Returns:
            new_ache : [[history_1], [history_2], [history_3]...]
        """
        assert len(cache) == 1
        cache_0 = cache[0]
        history: List[List[torch.Tensor]] = []
        for h in torch.split(cache_0, 1, dim=0):
            history.append([h])
        return history
Args:
cache : [history]
history: [bs, ...]
Returns:
new_ache : [[history_1], [history_2], [history_3]...]
modules/wenet_extractor/transducer/predictor.py