def forward(self, model, sample, reduce=True):
        """Compute the 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
        """
        net_output = model(**sample["net_input"])
        loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce)
        sample_size = (
            sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
        )
        logging_output = {
            "loss": utils.item(loss.data) if reduce else loss.data,
            "nll_loss": utils.item(nll_loss.data) if reduce else nll_loss.data,
            "ntokens": sample["ntokens"],
            "nsentences": sample["target"].size(0),
            "sample_size": sample_size,
        }

        alignment_loss = None

        # Compute alignment loss only for training set and non dummy batches.
        if "alignments" in sample and sample["alignments"] is not None:
            alignment_loss = self.compute_alignment_loss(sample, net_output)

        if alignment_loss is not None:
            logging_output["alignment_loss"] = utils.item(alignment_loss.data)
            loss += self.alignment_lambda * alignment_loss

        return loss, sample_size, logging_output
Compute the 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/label_smoothed_cross_entropy_with_alignment.py