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
        """
        assert (
            hasattr(model, "classification_heads")
            and self.ranking_head_name in model.classification_heads
        ), "model must provide sentence ranking head for --criterion=sentence_ranking"

        scores = []
        for idx in range(self.num_classes):
            score, _ = model(
                **sample["net_input{idx}".format(idx=idx + 1)],
                classification_head_name=self.ranking_head_name,
            )
            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)
            lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
            loss = F.nll_loss(lprobs, 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": loss.data,
            "ntokens": sample["ntokens"],
            "nsentences": sample_size,
            "sample_size": sample_size,
        }
        if targets is not None:
            logging_output["ncorrect"] = (logits.argmax(dim=1) == targets).sum()

        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
        
structured_prompting/fairseq-version/fairseq/fairseq/criterions/sentence_ranking.py