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"])
        logits = model.get_logits(net_output).float()
        target = model.get_targets(sample, net_output)
        self.xla = is_xla_tensor(logits)

        # XXX: handle weights on xla.
        weights = None
        if hasattr(model, "get_target_weights") and not self.infonce:
            weights = model.get_target_weights(target, net_output)
            if torch.is_tensor(weights):
                weights = weights.float()

        losses = []

        reduction = "none" if ((not reduce) or self.xla) else "sum"
        if self.infonce:
            loss = F.cross_entropy(logits, target, reduction=reduction)
        else:
            loss = F.binary_cross_entropy_with_logits(
                logits, target.float(), weights, reduction=reduction
            )

        if self.xla:
            # tpu-comment: since dynamic shapes lead to recompilations on xla,
            # we don't shrink tensors using mask_indices.
            # Instead, we use mask indices to adjust loss.
            mi = (
                sample["net_input"]["mask_indices"]
                .transpose(0, 1)  # logits are transposed in `model.get_logits`
                .reshape(logits.size(0))
            )
            loss = (loss * mi).sum() if reduce else (loss * mi)

        if "sample_size" in sample:
            sample_size = sample["sample_size"]
        elif "mask_indices" in sample["net_input"]:
            sample_size = sample["net_input"]["mask_indices"].sum()
        else:
            sample_size = target.numel() if self.infonce else target.long().sum().item()
        losses.append(loss.detach().clone())

        if self.loss_weights is not None:
            assert hasattr(model, "get_extra_losses")
            extra_losses = model.get_extra_losses(net_output)
            if torch.is_tensor(extra_losses):
                extra_losses = [extra_losses]
            if len(self.loss_weights) == 1 and len(extra_losses) != 1:
                self.loss_weights = [self.loss_weights[0]] * len(extra_losses)
            assert len(extra_losses) == len(
                self.loss_weights
            ), f"{len(extra_losses)}, {len(self.loss_weights)}"
            for p, coef in zip(extra_losses, self.loss_weights):
                if coef != 0 and p is not None:
                    p = coef * p.float() * sample_size
                    loss += p
                    losses.append(p)

        logging_output = {
            "loss": loss.item() if (reduce and not self.xla) else loss.detach(),
            "ntokens": sample_size,
            "nsentences": sample["id"].numel(),
            "sample_size": sample_size,
        }

        for lk in self.log_keys:
            # Only store "logits" and "target" for computing MAP and MAUC
            # during validation
            if lk == "logits":
                if not self.training:
                    logging_output["logits"] = logits.cpu().numpy()
            elif lk == "target":
                if not self.training:
                    # If the targets have been mixed with the predictions of
                    # teacher models, find the original targets
                    if hasattr(model, "get_original_targets"):
                        original_target = model.get_original_targets(sample, net_output)
                    else:
                        original_target = target
                    logging_output["target"] = original_target.cpu().numpy()
            elif lk in net_output:
                value = net_output[lk]
                if not is_xla_tensor(value):
                    value = float(value)
                logging_output[lk] = value

        if len(losses) > 1:
            for i, l in enumerate(losses):
                logging_output[f"loss_{i}"] = l.item() if not self.xla else l.detach()

        if self.infonce:
            with torch.no_grad():
                if logits.numel() == 0:
                    corr = 0
                    count = 0
                else:
                    assert logits.dim() > 1, logits.shape
                    max = logits.argmax(-1) == 0
                    min = logits.argmin(-1) == 0
                    if is_xla_tensor(logits):
                        max, min = max * mi, min * mi
                        both = max & min
                        corr = max.long().sum() - both.long().sum()
                        count = mi.sum()
                    else:
                        both = max & min
                        corr = max.long().sum().item() - both.long().sum().item()
                        count = float(max.numel())

                logging_output["correct"] = corr
                logging_output["count"] = count

        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/fairseq/criterions/wav2vec_criterion.py