class_docstr: This is the Detection Model training class, which allows you to train object detection models
  on image datasets that are in Pascal VOC annotation format, using the YOLOv3.
code: def _create_callbacks(self, saved_weights_name, model_to_save):
      checkpoint = CustomModelCheckpoint(
          model_to_save=model_to_save,
          filepath=saved_weights_name
          + "ex-{epoch:03d}--loss-{loss:08.3f}.h5",
          monitor="loss",
          verbose=0,
          save_best_only=True,
          mode="min",
          period=1,
      )
      reduce_on_plateau = ReduceLROnPlateau(
          monitor="loss",
          factor=0.1,
          patience=2,
          verbose=0,
          mode="min",
          epsilon=0.01,
          cooldown=0,
          min_lr=0,
      )
      tensor_board = TensorBoard(log_dir=self.__logs_directory)
      return [checkpoint, reduce_on_plateau, tensor_board]
  
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 9 for calling the DetectionModelTrainer obj's underlying member method code with example usage: obj._create_callbacks(saved_weights_name, model_to_save) and returns: list