def METHOD_NAME(self,
                inputs: torch.Tensor,
                in_audios: Union[np.ndarray, list],
                save_dir=None):
    if isinstance(in_audios[0], str) and save_dir is not None:
        # save the embeddings
        os.makedirs(save_dir, exist_ok=True)
        for i, p in enumerate(in_audios):
            save_path = os.path.join(
                save_dir, '%s.npy' %
                (os.path.basename(p).rsplit('.', 1)[0]))
            np.save(save_path, inputs[i].numpy())
    if len(inputs) == 2:
        # compute the score
        score = self.compute_cos_similarity(inputs[0], inputs[1])
        score = round(score, 5)
        if score >= self.thr:
            ans = 'yes'
        else:
            ans = 'no'
        output = {OutputKeys.SCORE: score, OutputKeys.TEXT: ans}
    else:
        output = {OutputKeys.TEXT: 'No similarity score output'}
    return output