code: def convert_to_xywh(boxes):
      xmin, ymin, xmax, ymax = boxes.unbind(1)
      return torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), dim=1)
  
imports: import copy
  import itertools
  import logging
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import pycocotools.mask
  import torch
  import torch.distributed
  from lvis import LVISEval, LVISResults, LVIS
  from avalanche.evaluation.metrics.detection import DetectionEvaluator, TCommonDetectionOutput
label_desc: Use image node_id 1 for calling a global function with example usage: convert_to_xywh(boxes) and returns: torch