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
        """
        assert (
            hasattr(model, "classification_heads")
            and self.classification_head_name in model.classification_heads
        ), "model must provide sentence classification head for --criterion=sentence_prediction"

        token_embeddings = model.encoder.sentence_encoder.embed_tokens(
            sample["net_input"]["src_tokens"]
        )
        input_logits, _ = model(
            **sample["net_input"],
            features_only=True,
            classification_head_name=self.classification_head_name,
            token_embeddings=token_embeddings,
        )
        if model.training and self.noise_sampler:
            noise = self.noise_sampler.sample(sample_shape=token_embeddings.shape).to(
                token_embeddings
            )
            noised_embeddings = token_embeddings.detach().clone() + noise

            noised_logits, _ = model(
                **sample["net_input"],
                features_only=True,
                classification_head_name=self.classification_head_name,
                token_embeddings=noised_embeddings,
            )
            symm_kl = self._get_symm_kl(noised_logits, input_logits)
        else:
            symm_kl = 0

        targets = model.get_targets(sample, [input_logits]).view(-1)
        sample_size = targets.numel()

        if not self.regression_target:
            loss = F.nll_loss(
                F.log_softmax(input_logits, dim=-1, dtype=torch.float32),
                targets,
                reduction="sum",
            )
            if model.training:
                symm_kl = symm_kl * sample_size
                loss = loss + self.r3f_lambda * symm_kl
        else:
            logits = input_logits.squeeze().float()
            targets = targets.float()
            loss = F.mse_loss(logits, targets, reduction="sum")

        logging_output = {
            "loss": utils.item(loss.data) if reduce else loss.data,
            "ntokens": sample["ntokens"],
            "nsentences": sample_size,
            "sample_size": sample_size,
        }

        if not self.regression_target:
            preds = input_logits.max(dim=1)[1]
            logging_output.update(ncorrect=(preds == targets).sum().item())

            if model.training and self.noise_sampler:
                logging_output.update(
                    symm_kl=utils.item(symm_kl.data) if reduce else symm_kl.data
                )
        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
        
understand_icl/fairseq/examples/rxf/rxf_src/sentence_prediction_r3f.py