def guide_loss_and_acc(self, model, lprobs, lprobs_teacher, target, reduce=True):
        """ lprobs_teacher is used as guide for lprobs """
        if self.alpha == 0.0 or model.num_updates < self.disable_update_num:
            return self.compute_loss_and_acc(model, lprobs, target, reduction=('sum' if reduce else 'none'))
        if not lprobs.batch_first:
            lprobs = lprobs.transpose(0, 1)
            lprobs_teacher = lprobs_teacher.transpose(0, 1)

        lprobs = lprobs.view(-1, lprobs.size(-1)).float()  # -> (B x T) x C
        lprobs_teacher = lprobs_teacher.view(-1, lprobs_teacher.size(-1)).float()  # -> (B x T) x C
        target = target.view(-1)
        loss = F.nll_loss(lprobs, target, ignore_index=self.padding_idx, reduction='sum' if reduce else 'none')
        nll_loss = loss
        probs_teacher = lprobs_teacher.exp().masked_fill_(target.unsqueeze(-1).eq(self.padding_idx), 0)
        probs_teacher = probs_teacher.detach()
        guide_loss = -(probs_teacher*lprobs).sum() if reduce else -(probs_teacher*lprobs).sum(-1, keepdim=True)
        loss = self.alpha*guide_loss + (1.0 - self.alpha)*loss

        mask = target.ne(self.padding_idx)
        correct = torch.sum(lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask)))
        total = torch.sum(mask)
        return loss, nll_loss, correct, total
 lprobs_teacher is used as guide for lprobs 
understand_icl/fairseq/examples/speech_text_joint_to_text/criterions/text_guide_cross_entropy_acc.py