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
        """
        token_embeddings = model.encoder.embed_tokens(sample["net_input"]["src_tokens"])
        input_logits, extra = model(**sample["net_input"])
        loss, nll_loss = self.compute_loss(
            model, (input_logits, extra), sample, reduce=reduce
        )
        sample_size = (
            sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
        )

        if model.training:
            noise = self.noise_sampler.sample(sample_shape=token_embeddings.shape).to(
                token_embeddings
            )
            noised_embeddings = token_embeddings.clone() + noise

            noised_logits, _ = model(
                **sample["net_input"], token_embeddings=noised_embeddings
            )
            symm_kl = self._get_symm_kl(noised_logits, input_logits)

        if model.training:
            symm_kl = symm_kl * sample_size
            loss = loss + self.r3f_lambda * symm_kl

        logging_output = {
            "loss": loss.data,
            "nll_loss": nll_loss.data,
            "ntokens": sample["ntokens"],
            "nsentences": sample["target"].size(0),
            "sample_size": sample_size,
        }

        if model.training:
            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
        
structured_prompting/fairseq-version/fairseq/examples/rxf/rxf_src/label_smoothed_cross_entropy_r3f.py