def forward(self, model, sample, reduce=True):
        """Compute ranking loss for the given sample.

        Returns a tuple with three elements:
        1) the loss
        2) the sample size, which is used as the denominator for the gradient
        3) logging outputs to display while training
        """
        scores = []
        for idx in range(self.args.num_classes):
            score, _ = model(
                **sample['net_input{idx}'.format(idx=idx+1)],
                classification_head_name='sentence_classification_head',
            )
            scores.append(score)

        logits = torch.cat(scores, dim=1)
        sample_size = logits.size(0)

        if 'target' in sample:
            targets = model.get_targets(sample, [logits]).view(-1)
            loss = F.nll_loss(
                F.log_softmax(logits, dim=-1, dtype=torch.float32),
                targets,
                reduction='sum',
            )
        else:
            targets = None
            loss = torch.tensor(0.0, requires_grad=True)

        if self.prediction_h is not None:
            preds = logits.argmax(dim=1)
            for i, (id, pred) in enumerate(zip(sample['id'].tolist(), preds.tolist())):
                if targets is not None:
                    label = targets[i].item()
                    print('{}\t{}\t{}'.format(id, pred, label), file=self.prediction_h)
                else:
                    print('{}\t{}'.format(id, pred), file=self.prediction_h)

        logging_output = {
            'loss': utils.item(loss.data) if reduce else loss.data,
            'ntokens': sample['ntokens'],
            'nsentences': sample_size,
            'sample_size': sample_size,
        }
        if targets is not None:
            logging_output.update(
                ncorrect=(logits.max(dim=1)[1] == targets).sum().item()
            )
        return loss, sample_size, logging_output
Compute ranking loss for the given sample.

        Returns a tuple with three elements:
        1) the loss
        2) the sample size, which is used as the denominator for the gradient
        3) logging outputs to display while training
        
infoxlm/fairseq/fairseq/criterions/sentence_ranking.py