def _inference_one_image(self, x):
        """
        Args:
            x (dict): one dataset dict with "image" field being a CHW tensor

        Returns:
            dict: one output dict
        """
        orig_shape = (x["height"], x["width"])
        augmented_inputs, tfms = self._get_augmented_inputs(x)
        merged_instances = self._get_augmented_instances(augmented_inputs, tfms, orig_shape)
        if len(merged_instances) > 0:
            if self.model.do_nms:
                # Multiclass NMS.
                keep = batched_nms(
                    merged_instances.pred_boxes.tensor, merged_instances.scores_3d, merged_instances.pred_classes,
                    self.nms_thresh
                )
                merged_instances = merged_instances[keep]

            if not self.model.only_box2d and self.model.do_bev_nms:
                # Bird-eye-view NMS.
                keep = bev_nms(
                    merged_instances.pred_boxes3d,
                    merged_instances.scores_3d,
                    self.model.bev_nms_iou_thresh,
                    class_idxs=merged_instances.pred_classes,
                    class_agnostic=False
                )
                merged_instances = merged_instances[keep]

        return {"instances": merged_instances}

        Args:
            x (dict): one dataset dict with "image" field being a CHW tensor

        Returns:
            dict: one output dict
        
tridet/modeling/dd3d/test_time_augmentation.py