def forward(
        self,
        batch_real: torch.Tensor,
        batch_anomaly: torch.Tensor,
        batch_segmentation_map: torch.Tensor,
    ) -> torch.Tensor:
        """Computes upsampled segmentation maps.

        Args:
            batch_real (torch.Tensor): Batch of real, non defective images.
            batch_anomaly (torch.Tensor): Batch of potentially anomalous images.
            batch_segmentation_map (torch.Tensor): Batch of anomaly segmentation maps.

        Returns:
            Upsampled anomaly segmentation maps.
        """
        img_x = torch.cat((batch_real, batch_anomaly, batch_segmentation_map), dim=1)
        return self.unet(img_x)
Computes upsampled segmentation maps.

        Args:
            batch_real (torch.Tensor): Batch of real, non defective images.
            batch_anomaly (torch.Tensor): Batch of potentially anomalous images.
            batch_segmentation_map (torch.Tensor): Batch of anomaly segmentation maps.

        Returns:
            Upsampled anomaly segmentation maps.
        
src/anomalib/models/image/dsr/torch_model.py