code: def bbox_iou(box1, box2):
      """
      Returns the IoU of two bounding boxes
      IoU: IoU = Area Of Overlap / Area of Union -> How close the predicted bounding box is
      to the ground truth bounding box. Higher IoU = Better accuracy
      In training, used to track accuracy. with inference, using to remove duplicate bounding boxes
      """
      # Get the coordinates of bounding boxes
      b1_x1, b1_y1, b1_x2, b1_y2 = (
          box1[:, 0],
          box1[:, 1],
          box1[:, 2],
          box1[:, 3],
      )
      b2_x1, b2_y1, b2_x2, b2_y2 = (
          box2[:, 0],
          box2[:, 1],
          box2[:, 2],
          box2[:, 3],
      )
      # get the coordinates of the intersection rectangle
      inter_rect_x1 = np.maximum(b1_x1, b2_x1)
      inter_rect_y1 = np.maximum(b1_y1, b2_y1)
      inter_rect_x2 = np.maximum(b1_x2, b2_x2)
      inter_rect_y2 = np.maximum(b1_y2, b2_y2)
      # Intersection area
      inter_area = np.clip(
          inter_rect_x2 - inter_rect_x1 + 1, 0, 99999
      ) * np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0, 99999)
      # Union Area
      b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)
      b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)
      iou = inter_area / (b1_area + b2_area - inter_area)
      return iou
  
imports: import sys
  import io
  import time
  import math
  import cv2
  import numpy
  from PIL import Image
  from tinygrad.tensor import Tensor
  from tinygrad.nn import BatchNorm2d, Conv2d
  from tinygrad.helpers import fetch
label_desc: Use image node_id 3 for calling a global function with example usage: bbox_iou(box1, box2) and returns: iou