code: def _rand_bboxes(num_boxes, h, w):
      cx, cy, bw, bh = torch.rand(num_boxes, 4).T
  
      tl_x = ((cx * w) - (w * bw / 2)).clamp(0, w)
      tl_y = ((cy * h) - (h * bh / 2)).clamp(0, h)
      br_x = ((cx * w) + (w * bw / 2)).clamp(0, w)
      br_y = ((cy * h) + (h * bh / 2)).clamp(0, h)
  
      bboxes = torch.stack([tl_x, tl_y, br_x, br_y], dim=0).T
      return bboxes
  
imports: import os
  from unittest import TestCase
  import cv2
  import numpy
  import torch
  from mmengine.structures import InstanceData, PixelData
  from mmdet.evaluation import INSTANCE_OFFSET
  from mmdet.structures import DetDataSample
  from mmdet.visualization import DetLocalVisualizer, TrackLocalVisualizer
label_desc: Use image node_id 1 for calling a global function with example usage: _rand_bboxes(num_boxes, h, w) and returns: bboxes