def _get_cait_features(self, input_tensor: torch.Tensor) -> list[torch.Tensor]:
        """Get Class-Attention-Image-Transformers (CaiT) features.

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

        Returns:
            list[torch.Tensor]: List of features.
        """
        feature = self.feature_extractor.patch_embed(input_tensor)
        feature = feature + self.feature_extractor.pos_embed
        feature = self.feature_extractor.pos_drop(feature)
        for i in range(41):  # paper Table 6. Block Index = 40
            feature = self.feature_extractor.blocks[i](feature)
        batch_size, _, num_channels = feature.shape
        feature = self.feature_extractor.norm(feature)
        feature = feature.permute(0, 2, 1)
        feature = feature.reshape(batch_size, num_channels, self.input_size[0] // 16, self.input_size[1] // 16)
        return [feature]
Get Class-Attention-Image-Transformers (CaiT) features.

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

        Returns:
            list[torch.Tensor]: List of features.
        
src/anomalib/models/image/fastflow/torch_model.py