def get_channels_dim(self) -> int:
        """Get feature channel dimension.

        Returns:
            (int): feature channel dimension.
        """
        # dryrun
        self.feature_extractor.eval()
        with torch.no_grad():
            features = self.feature_extractor(torch.rand(1, 3, 256, 256))
        # sum channels
        return sum(feature.shape[1] for feature in features.values())
Get feature channel dimension.

        Returns:
            (int): feature channel dimension.
        
src/anomalib/models/image/supersimplenet/torch_model.py