code: def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
      # Rescale coords (xyxy) from img1_shape to img0_shape
      if ratio_pad is None:  # calculate from img0_shape
          gain = min(
              img1_shape[0] / img0_shape[0],
              img1_shape[1] / img0_shape[1],
          )  # gain  = old / new
          pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (
              img1_shape[0] - img0_shape[0] * gain
          ) / 2  # wh padding
      else:
          gain = ratio_pad[0][0]
          pad = ratio_pad[1]
  
      coords[:, [0, 2]] -= pad[0]  # x padding
      coords[:, [1, 3]] -= pad[1]  # y padding
      coords[:, :4] /= gain
      clip_coords(coords, img0_shape)
      return coords
  
imports: import glob
  import logging
  import os
  import re
  import time
  from pathlib import Path
  from typing import List
  import cv2
  import numpy
  import torch
  import torchvision
  from norfair import Detection
label_desc: Use image node_id 7 for calling a global function with example usage: scale_coords(img1_shape, coords, img0_shape, ratio_pad) and returns: coords