def METHOD_NAME(self, emissions: torch.Tensor) -> List[List[dict]]:
    """Decode sequence.
    Args:
        emissions (torch.Tensor): Emission probabilities (in log scale).
    Returns:
        List[List[dict]]: Decoded hypotheses.
    """
    emissions = emissions.float().contiguous().cpu()
    B, T, N = emissions.size()
    hyps = []
    for b in range(B):
        emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0)
        results = self.decoder.METHOD_NAME(emissions_ptr, T, N)
        nbest_results = results[: self.nbest]
        hyps.append(
            [
                dict(
                    tokens=self.get_tokens(result.tokens),
                    score=result.score,
                    timesteps=self.get_timesteps(result.tokens),
                    words=[
                        self.word_dict.get_entry(x) for x in result.words if x >= 0
                    ],
                )
                for result in nbest_results
            ]
        )
    return hyps