code: def xywh2xyxy(x):
      # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
      y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
      y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
      y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
      y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
      y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
      return y
  
imports: import os
  import time
  import json
  import copy
  import cv2
  import numpy
  import torch
  from torchmetrics.detection.mean_ap import MeanAveragePrecision
  import torchvision
  from pycocotools.coco import COCO
  from pycocotools.cocoeval import COCOeval
  from compression.api import DataLoader, Metric
label_desc: Use image node_id 2 for calling a global function with example usage: xywh2xyxy(x) and returns: y