def produce_fake_densepose_losses_segm(self, densepose_predictor_outputs: Any) -> LossDict:
        """
        Fake losses for fine / coarse segmentation. These are used when
        no suitable ground truth data was found in a batch. The loss has a value 0
        and is primarily used to construct the computation graph, so that
        `DistributedDataParallel` has similar graphs on all GPUs and can perform
        reduction properly.

        Args:
            densepose_predictor_outputs: DensePose predictor outputs, an object
                of a dataclass that is assumed to have the following attributes:
             * fine_segm - fine segmentation estimates, tensor of shape [N, C, S, S]
             * coarse_segm - coarse segmentation estimates, tensor of shape [N, D, S, S]
        Return:
            dict: str -> tensor: dict of losses with the following entries:
             * `loss_densepose_I`: has value 0
             * `loss_densepose_S`: has value 0, added only if `segm_trained_by_masks` is False
        """
        losses = {
            "loss_densepose_I": densepose_predictor_outputs.fine_segm.sum() * 0,
            "loss_densepose_S": self.segm_loss.fake_value(densepose_predictor_outputs),
        }
        return losses

        Fake losses for fine / coarse segmentation. These are used when
        no suitable ground truth data was found in a batch. The loss has a value 0
        and is primarily used to construct the computation graph, so that
        `DistributedDataParallel` has similar graphs on all GPUs and can perform
        reduction properly.

        Args:
            densepose_predictor_outputs: DensePose predictor outputs, an object
                of a dataclass that is assumed to have the following attributes:
             * fine_segm - fine segmentation estimates, tensor of shape [N, C, S, S]
             * coarse_segm - coarse segmentation estimates, tensor of shape [N, D, S, S]
        Return:
            dict: str -> tensor: dict of losses with the following entries:
             * `loss_densepose_I`: has value 0
             * `loss_densepose_S`: has value 0, added only if `segm_trained_by_masks` is False
        
EVA-02/det/projects/DensePose/densepose/modeling/losses/chart.py