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.decoder, 'adaptive_softmax') and model.decoder.adaptive_softmax is not None
        adaptive_softmax = model.decoder.adaptive_softmax

        net_output = model(**sample['net_input'])
        orig_target = model.get_targets(sample, net_output)

        nsentences = orig_target.size(0)
        orig_target = orig_target.view(-1)

        bsz = orig_target.size(0)

        logits, target = adaptive_softmax(net_output[0], orig_target)
        assert len(target) == len(logits)

        loss = net_output[0].new(1 if reduce else bsz).zero_()

        for i in range(len(target)):
            if target[i] is not None:
                assert (target[i].min() >= 0 and target[i].max() <= logits[i].size(1))
                loss += F.cross_entropy(
                    logits[i],
                    target[i],
                    ignore_index=self.padding_idx,
                    reduction='sum' if reduce else 'none',
                )

        orig = utils.strip_pad(orig_target, self.padding_idx)
        ntokens = orig.numel()
        sample_size = sample['target'].size(0) if self.args.sentence_avg else ntokens
        logging_output = {
            'loss': utils.item(loss.data) if reduce else loss.data,
            'ntokens': ntokens,
            'nsentences': nsentences,
            'sample_size': sample_size,
        }
        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
        
infoxlm/fairseq/fairseq/criterions/adaptive_loss.py