def METHOD_NAME(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