code: def IOU(self, ax1, ay1, ax2, ay2, sa, bx1, by1, bx2, by2, sb):
      # sa = abs((ax2 - ax1) * (ay2 - ay1))
      # sb = abs((bx2 - bx1) * (by2 - by1))
      x1, y1 = max(ax1, bx1), max(ay1, by1)
      x2, y2 = min(ax2, bx2), min(ay2, by2)
      w = x2 - x1
      h = y2 - y1
      if w < 0 or h < 0:
          return 0.0
      else:
          return 1.0 * w * h / (sa + sb - w * h)
  
imports: import os
  import sys
  import math
  import pickle
  import yaml
  import imageio
  import numpy
  from tqdm import tqdm
  from scipy.spatial import ConvexHull
  import cv2
  import paddle
  from ppgan.utils.download import get_path_from_url
  from ppgan.utils.animate import normalize_kp
  from ppgan.modules.keypoint_detector import KPDetector
  from ppgan.models.generators.occlusion_aware import OcclusionAwareGenerator
  from ppgan.faceutils import face_detection
label_desc: Use image node_id 8 for calling the FirstOrderPredictor obj's underlying member method code with example usage: obj.IOU(ax1, ay1, ax2, ay2, sa, bx1, by1, bx2, by2, sb) and returns: int, unknown