def loss(self, feats: dict, data_samples=None):
        """Calculate losses from the extracted features.

        Args:
            feats (dict): The features extracted from the backbone.
            data_samples (List[BaseDataElement]): The annotation data of
                every samples.

        Returns:
            dict[str, Tensor]: a dictionary of loss components
        """
        shifted_prediction_scores = self(feats)
        labels = feats['answer_targets']
        lm_loss = None

        # we are doing next-token prediction;
        # shift prediction scores and input ids by one
        labels = labels[:, 1:].contiguous()
        lm_loss = self.loss_module(
            shifted_prediction_scores.view(-1,
                                           self.decoder.med_config.vocab_size),
            labels.view(-1))
        lm_loss = lm_loss.view(shifted_prediction_scores.size(0), -1).sum(1)
        # compute weighted loss
        losses = dict()
        loss = feats['answer_weight'] * lm_loss
        loss = loss.sum() / feats['batch_size']
        losses['vqa_loss'] = loss

        return losses
Calculate losses from the extracted features.

        Args:
            feats (dict): The features extracted from the backbone.
            data_samples (List[BaseDataElement]): The annotation data of
                every samples.

        Returns:
            dict[str, Tensor]: a dictionary of loss components
        
mmpretrain/models/heads/vqa_head.py