code: def correct_yolo_boxes(boxes, image_h, image_w, net_h, net_w):
      new_w, new_h = net_w, net_h
      for i in range(len(boxes)):
          x_offset, x_scale = (net_w - new_w) / 2.0 / net_w, float(
              new_w
          ) / net_w
          y_offset, y_scale = (net_h - new_h) / 2.0 / net_h, float(
              new_h
          ) / net_h
          boxes[i].xmin = int(
              (boxes[i].xmin - x_offset) / x_scale * image_w
          )
          boxes[i].xmax = int(
              (boxes[i].xmax - x_offset) / x_scale * image_w
          )
          boxes[i].ymin = int(
              (boxes[i].ymin - y_offset) / y_scale * image_h
          )
          boxes[i].ymax = int(
              (boxes[i].ymax - y_offset) / y_scale * image_h
          )
  
imports: import os
  import re
  import numpy
  import json
  from imageai.Detection.Custom.voc import parse_voc_annotation
  from imageai.Detection.YOLO.yolov3 import yolov3_main, yolov3_train, dummy_loss
  from imageai.Detection.Custom.generator import BatchGenerator
  from imageai.Detection.Custom.utils.utils import normalize, evaluate, makedirs
  from tensorflow.keras.callbacks import ReduceLROnPlateau
  from tensorflow.keras.optimizers import Adam
  from imageai.Detection.Custom.callbacks import CustomModelCheckpoint
  from imageai.Detection.Custom.utils.multi_gpu_model import multi_gpu_model
  from imageai.Detection.Custom.gen_anchors import generateAnchors
  import tensorflow
  from tensorflow.keras.models import load_model
  from tensorflow.keras import Input
  from tensorflow.keras.callbacks import TensorBoard
  import tensorflow.keras.backend
  import cv2
label_desc: Use image node_id 4 for calling the CustomDetectionUtils obj's underlying member method code with example usage: obj.correct_yolo_boxes(boxes, image_h, image_w, net_h, net_w) without return types