def _produce_mask_and_results(
        self, instance: Instances, bbox_xywh: IntTupleBox
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Method to get labels and DensePose results from an instance

        Args:
            instance (Instances): an instance of
                `DensePoseEmbeddingPredictorOutputWithConfidences`
            bbox_xywh (IntTupleBox): the corresponding bounding box

        Return:
            mask (torch.Tensor): shape [H, W], DensePose segmentation mask
            embeddings (Tuple[torch.Tensor]): a tensor of shape [D, H, W]
                DensePose CSE Embeddings
            other_values: a tensor of shape [1, H, W], DensePose CSE confidence
        """
        _, _, w, h = bbox_xywh
        densepose_output = instance.pred_densepose
        mask, embeddings, _ = super()._produce_mask_and_results(instance, bbox_xywh)
        other_values = F.interpolate(
            getattr(densepose_output, self.confidence_channel),
            size=(h, w),
            mode="bilinear",
        )[0].cpu()
        return mask, embeddings, other_values

        Method to get labels and DensePose results from an instance

        Args:
            instance (Instances): an instance of
                `DensePoseEmbeddingPredictorOutputWithConfidences`
            bbox_xywh (IntTupleBox): the corresponding bounding box

        Return:
            mask (torch.Tensor): shape [H, W], DensePose segmentation mask
            embeddings (Tuple[torch.Tensor]): a tensor of shape [D, H, W]
                DensePose CSE Embeddings
            other_values: a tensor of shape [1, H, W], DensePose CSE confidence
        
EVA-02/det/projects/DensePose/densepose/data/samplers/densepose_cse_confidence_based.py