def score(
        self,
        state: LMState,
        token_index: int,
        no_cache: bool = False,
    ) -> Tuple[LMState, int]:
        """
        Evaluate language model based on the current lm state and new word
        Parameters:
        -----------
        state: current lm state
        token_index: index of the word
                     (can be lexicon index then you should store inside LM the
                      mapping between indices of lexicon and lm, or lm index of a word)
        Returns:
        --------
        (LMState, float): pair of (new state, score for the current word)
        """
        curr_state = self.states[state]

        def trim_cache(targ_size: int) -> None:
            while len(self.stateq) > targ_size:
                rem_k = self.stateq.popleft()
                rem_st = self.states[rem_k]
                rem_st = FairseqLMState(rem_st.prefix, None, None)
                self.states[rem_k] = rem_st

        if curr_state.probs is None:
            new_incremental_state = (
                curr_state.incremental_state.copy()
                if curr_state.incremental_state is not None
                else None
            )
            with torch.no_grad():
                if new_incremental_state is not None:
                    new_incremental_state = apply_to_sample(
                        lambda x: x.cuda(), new_incremental_state
                    )
                elif self.save_incremental:
                    new_incremental_state = {}

                res = self.model(
                    torch.from_numpy(curr_state.prefix).cuda(),
                    incremental_state=new_incremental_state,
                )
                probs = self.model.get_normalized_probs(
                    res, log_probs=True, sample=None
                )

                if new_incremental_state is not None:
                    new_incremental_state = apply_to_sample(
                        lambda x: x.cpu(), new_incremental_state
                    )

                curr_state = FairseqLMState(
                    curr_state.prefix, new_incremental_state, probs[0, -1].cpu().numpy()
                )

            if not no_cache:
                self.states[state] = curr_state
                self.stateq.append(state)

        score = curr_state.probs[token_index].item()

        trim_cache(self.max_cache)

        outstate = state.child(token_index)
        if outstate not in self.states and not no_cache:
            prefix = np.concatenate(
                [curr_state.prefix, torch.LongTensor([[token_index]])], -1
            )
            incr_state = curr_state.incremental_state

            self.states[outstate] = FairseqLMState(prefix, incr_state, None)

        if token_index == self.unk:
            score = float("-inf")

        return outstate, score

        Evaluate language model based on the current lm state and new word
        Parameters:
        -----------
        state: current lm state
        token_index: index of the word
                     (can be lexicon index then you should store inside LM the
                      mapping between indices of lexicon and lm, or lm index of a word)
        Returns:
        --------
        (LMState, float): pair of (new state, score for the current word)
        
understand_icl/fairseq/examples/speech_recognition/new/decoders/flashlight_decoder.py