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.
docstr: 'setModelTypeAsYOLOv3()' is used to set the model type to the YOLOv3 model
  for the training instance object .
  :return:
docstr_tok: ["'setModelTypeAsYOLOv3","(",")","'","is","used","to","set","the","model","type","to","the","YOLOv3","model","for","the","training","instance","object",".",":","return",":"]
code: def setModelTypeAsYOLOv3(self):
      """
      'setModelTypeAsYOLOv3()' is used to set the model type to the YOLOv3 model
      for the training instance object .
      :return:
      """
      self.__model_type = "yolov3"
  
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 2 for calling the DetectionModelTrainer obj's underlying member method code with example usage: obj.setModelTypeAsYOLOv3() without return types