def loss(
        self,
        images: torch.Tensor,
        data_samples: Optional[List[DataSample]] = None,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor]]:
        """generate train_loss from the input tensor and data_samples.

        Args:
            images (Tensor): A batch of images. The shape of it should be
                (B, C, H, W) for images and (B, T, C, H, W) for videos.
            data_samples (List[DataSample], optional): The annotation
                data of every samples.

        Returns:
            Dict[torch.Tensor]: The losses features.
        """
        visual_embeds = self.extract_feat(images)
        image_atts = torch.ones(
            visual_embeds.size()[:-1], dtype=torch.long).to(self.device)

        questions = []
        for sample in data_samples:
            questions.append(sample.get('question'))
        questions = self.tokenizer(
            questions, padding='longest', return_tensors='pt').to(self.device)

        questions.input_ids[:, 0] = \
            self.tokenizer.additional_special_tokens_ids[0]

        # multimodal fusion
        multimodal_embeds = self.multimodal_backbone(
            questions.input_ids,
            attention_mask=questions.attention_mask,
            encoder_hidden_states=visual_embeds,
            encoder_attention_mask=image_atts,
            return_dict=True,
        )

        # put answer from data_samples into tensor form
        answer_raw_text = []
        for sample in data_samples:
            answer_raw_text.extend(sample.gt_answer)
        answer = self.tokenizer(
            answer_raw_text, padding='longest',
            return_tensors='pt').to(self.device)
        answer_targets = answer.input_ids.masked_fill(
            answer.input_ids == self.tokenizer.pad_token_id, -100)
        for sample in data_samples:
            # follow BLIP setting, set answer_weight to 0.2 for VG dataset.
            if not hasattr(sample, 'gt_answer_weight'):
                sample.gt_answer_weight = torch.tensor([0.2])
            else:
                sample.gt_answer_weight = torch.tensor(sample.gt_answer_weight)
        answer_weight = torch.cat(
            [sample.gt_answer_weight for sample in data_samples],
            dim=0).to(self.device)
        answer_count = torch.tensor(
            [len(sample.gt_answer) for sample in data_samples]).to(self.device)

        question_states, question_atts = [], []
        for b, n in enumerate(answer_count):
            question_states += [multimodal_embeds.last_hidden_state[b]] * n
            question_atts += [questions.attention_mask[b]] * n

        question_states = torch.stack(question_states, dim=0).to(self.device)
        question_atts = torch.stack(question_atts, dim=0).to(self.device)

        head_feats = dict(
            answer_input_ids=answer.input_ids,
            answer_attention_mask=answer.attention_mask,
            answer_weight=answer_weight,
            answer_targets=answer_targets,
            question_states=question_states,
            question_atts=question_atts,
            batch_size=len(data_samples),
        )

        losses = self.vqa_head.loss(head_feats)

        return losses
generate train_loss from the input tensor and data_samples.

        Args:
            images (Tensor): A batch of images. The shape of it should be
                (B, C, H, W) for images and (B, T, C, H, W) for videos.
            data_samples (List[DataSample], optional): The annotation
                data of every samples.

        Returns:
            Dict[torch.Tensor]: The losses features.
        
mmpretrain/models/multimodal/blip/blip_vqa.py