code: def decode_netout(self, netout, anchors, obj_thresh, net_h, net_w):
      grid_h, grid_w = netout.shape[:2]
      nb_box = 3
      netout = netout.reshape((grid_h, grid_w, nb_box, -1))
      nb_class = netout.shape[-1] - 5
      boxes = []
      netout[..., :2] = self._sigmoid(netout[..., :2])
      netout[..., 4:] = self._sigmoid(netout[..., 4:])
      netout[..., 5:] = (
          netout[..., 4][..., np.newaxis] * netout[..., 5:]
      )
      netout[..., 5:] *= netout[..., 5:] > obj_thresh
  
      for row in range(grid_h):
          for col in range(grid_w):
              for b in range(nb_box):
                  # 4th element is objectness score
                  objectness = netout[row, col, b, 4]
  
                  if objectness <= obj_thresh:
                      continue
  
                  # first 4 elements are x, y, w, and h
                  x, y, w, h = netout[row, col, b, :4]
                  x = (
                      col + x
                  ) / grid_w  # center position, unit: image width
                  y = (
                      row + y
                  ) / grid_h  # center position, unit: image height
                  w = (
                      anchors[2 * b + 0] * np.exp(w) / net_w
                  )  # unit: image width
                  h = (
                      anchors[2 * b + 1] * np.exp(h) / net_h
                  )  # unit: image height
                  # last elements are class probabilities
                  classes = netout[row, col, b, 5:]
                  box = BoundBox(
                      x - w / 2,
                      y - h / 2,
                      x + w / 2,
                      y + h / 2,
                      objectness,
                      classes,
                  )
                  boxes.append(box)
  
      return boxes
  
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 3 for calling the CustomDetectionUtils obj's underlying member method code with example usage: obj.decode_netout(netout, anchors, obj_thresh, net_h, net_w) and returns: boxes