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

        Returns:
            dict: one output dict
        """
        orig_shape = (input["height"], input["width"])
        augmented_inputs, tfms = self._get_augmented_inputs(input)
        # Detect boxes from all augmented versions
        with self._turn_off_roi_heads(["mask_on", "keypoint_on"]):
            # temporarily disable roi heads
            all_boxes, all_scores, all_classes = self._get_augmented_boxes(augmented_inputs, tfms)
        # merge all detected boxes to obtain final predictions for boxes
        merged_instances = self._merge_detections(all_boxes, all_scores, all_classes, orig_shape)

        if self.cfg.MODEL.MASK_ON:
            # Use the detected boxes to obtain masks
            augmented_instances = self._rescale_detected_boxes(
                augmented_inputs, merged_instances, tfms
            )
            # run forward on the detected boxes
            outputs = self._batch_inference(augmented_inputs, augmented_instances)
            # Delete now useless variables to avoid being out of memory
            del augmented_inputs, augmented_instances
            # average the predictions
            merged_instances.pred_masks = self._reduce_pred_masks(outputs, tfms)
            merged_instances = detector_postprocess(merged_instances, *orig_shape)
            return {"instances": merged_instances}
        else:
            return {"instances": merged_instances}

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

        Returns:
            dict: one output dict
        
video_chat_text/video_chat_with_MOSS/models/grit_src/third_party/CenterNet2/detectron2/modeling/test_time_augmentation.py