def forward(self, b1: torch.Tensor, b2: torch.Tensor, b3: torch.Tensor, b4: torch.Tensor) -> torch.Tensor:
        """Decodes latent represnetations into an image.

        Args:
            b1 (torch.Tensor): First (top level) quantized feature map.
            b2 (torch.Tensor): Second quantized feature map.
            b3 (torch.Tensor): Third quantized feature map.
            b4 (torch.Tensor): Fourth (bottom level) quantized feature map.

        Returns:
            Reconstructed image.
        """
        up1 = self.up1(b4)
        cat1 = torch.cat((up1, b3), dim=1)
        db1 = self.db1(cat1)

        up2 = self.up2(db1)
        cat2 = torch.cat((up2, b2), dim=1)
        db2 = self.db2(cat2)

        up3 = self.up3(db2)
        cat3 = torch.cat((up3, b1), dim=1)
        db3 = self.db3(cat3)

        return self.fin_out(db3)
Decodes latent represnetations into an image.

        Args:
            b1 (torch.Tensor): First (top level) quantized feature map.
            b2 (torch.Tensor): Second quantized feature map.
            b3 (torch.Tensor): Third quantized feature map.
            b4 (torch.Tensor): Fourth (bottom level) quantized feature map.

        Returns:
            Reconstructed image.
        
src/anomalib/models/image/dsr/torch_model.py