def loss(self, inputs: List[torch.Tensor], data_samples: List[DataSample],
             **kwargs) -> Dict[str, torch.Tensor]:
        """The forward function in training.

        Args:
            inputs (List[torch.Tensor]): The input images.
            data_samples (List[DataSample]): All elements required
                during the forward function.

        Returns:
            Dict[str, torch.Tensor]: A dictionary of loss components.
        """
        assert isinstance(inputs, list)
        img_v1 = inputs[0]
        img_v2 = inputs[1]
        # compute online features
        proj_online_v1 = self.neck(self.backbone(img_v1))[0]
        proj_online_v2 = self.neck(self.backbone(img_v2))[0]
        # compute target features
        with torch.no_grad():
            # update the target net
            self.target_net.update_parameters(
                nn.Sequential(self.backbone, self.neck))

            proj_target_v1 = self.target_net(img_v1)[0]
            proj_target_v2 = self.target_net(img_v2)[0]

        loss_1 = self.head.loss(proj_online_v1, proj_target_v2)
        loss_2 = self.head.loss(proj_online_v2, proj_target_v1)

        losses = dict(loss=2. * (loss_1 + loss_2))
        return losses
The forward function in training.

        Args:
            inputs (List[torch.Tensor]): The input images.
            data_samples (List[DataSample]): All elements required
                during the forward function.

        Returns:
            Dict[str, torch.Tensor]: A dictionary of loss components.
        
mmpretrain/models/selfsup/byol.py