def forward(
        self,
        act1: torch.Tensor,
        act2: torch.Tensor,
        act3: torch.Tensor,
        act4: torch.Tensor,
        act5: torch.Tensor,
        act6: torch.Tensor,
    ) -> torch.Tensor:
        """Compute predicted anomaly scores from encoder activations.

        Args:
            act1 (torch.Tensor): First block encoder activations
            act2 (torch.Tensor): Second block encoder activations
            act3 (torch.Tensor): Third block encoder activations
            act4 (torch.Tensor): Fourth block encoder activations
            act5 (torch.Tensor): Fifth block encoder activations
            act6 (torch.Tensor): Sixth block encoder activations

        Returns:
            torch.Tensor: Predicted anomaly scores per pixel
        """
        up_b = self.up_b(act6)
        cat_b = torch.cat((up_b, act5), dim=1)
        db_b = self.db_b(cat_b)

        up1 = self.up1(db_b)
        cat1 = torch.cat((up1, act4), dim=1)
        db1 = self.db1(cat1)

        up2 = self.up2(db1)
        cat2 = torch.cat((up2, act3), dim=1)
        db2 = self.db2(cat2)

        up3 = self.up3(db2)
        cat3 = torch.cat((up3, act2), dim=1)
        db3 = self.db3(cat3)

        up4 = self.up4(db3)
        cat4 = torch.cat((up4, act1), dim=1)
        db4 = self.db4(cat4)

        return self.fin_out(db4)
Compute predicted anomaly scores from encoder activations.

        Args:
            act1 (torch.Tensor): First block encoder activations
            act2 (torch.Tensor): Second block encoder activations
            act3 (torch.Tensor): Third block encoder activations
            act4 (torch.Tensor): Fourth block encoder activations
            act5 (torch.Tensor): Fifth block encoder activations
            act6 (torch.Tensor): Sixth block encoder activations

        Returns:
            torch.Tensor: Predicted anomaly scores per pixel
        
src/anomalib/models/image/draem/torch_model.py