def forward(self, input_tensor: torch.Tensor) -> list[torch.Tensor]:
        """Extract features at multiple scales.

        Args:
            input_tensor (torch.Tensor): Input images.

        Returns:
            list[torch.Tensor]: List of feature tensors at different scales.
        """
        output = []
        for scale in range(self.n_scales):
            feat_s = (
                F.interpolate(
                    input_tensor,
                    size=(self.input_size[0] // (2**scale), self.input_size[1] // (2**scale)),
                )
                if scale > 0
                else input_tensor
            )
            feat_s = self.feature_extractor(feat_s)["features.6.8"]

            output.append(feat_s)
        return output
Extract features at multiple scales.

        Args:
            input_tensor (torch.Tensor): Input images.

        Returns:
            list[torch.Tensor]: List of feature tensors at different scales.
        
src/anomalib/models/image/csflow/torch_model.py