    def batch_score(
        self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor
    ) -> Tuple[torch.Tensor, List[Any]]:
        """Score new token batch (required).

        Args:
            ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
            states (List[Any]): Scorer states for prefix tokens.
            xs (torch.Tensor):
                The encoder feature that generates ys (n_batch, xlen, n_feat).

        Returns:
            tuple[torch.Tensor, List[Any]]: Tuple of
                batchfied scores for next token with shape of `(n_batch,
                    n_vocab)`
                and next state list for ys.

        """
        scores = list()
        outstates = list()
        for i, (y, state, x) in enumerate(zip(ys, states, xs)):
            score, outstate = self.score(y, state, x)
            outstates.append(outstate)
            scores.append(score)
        scores = torch.cat(scores, 0).view(ys.shape[0], -1)
        return scores, outstates
Score new token batch (required).

Args:
ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
states (List[Any]): Scorer states for prefix tokens.
xs (torch.Tensor):
The encoder feature that generates ys (n_batch, xlen, n_feat).

Returns:
tuple[torch.Tensor, List[Any]]: Tuple of
batchfied scores for next token with shape of `(n_batch,
n_vocab)`
and next state list for ys.
modules/wenet_extractor/paraformer/search/scorer_interface.py