class_docstr: This is the object detection class for videos and camera live stream inputs in the ImageAI library. It provides support for RetinaNet,
  YOLOv3 and TinyYOLOv3 object detection networks. After instantiating this class, you can set it's properties and
  make object detections using it's pre-defined functions.
  The following functions are required to be called before object detection can be made
  * setModelPath()
  * At least of of the following and it must correspond to the model set in the setModelPath()
  [setModelTypeAsRetinaNet(), setModelTypeAsYOLOv3(), setModelTinyYOLOv3()]
  * loadModel() [This must be called once only before performing object detection]
  Once the above functions have been called, you can call the detectObjectsFromVideo() function
  or the detectCustomObjectsFromVideo() of  the object detection instance object at anytime to
  obtain observable objects in any video or camera live stream.
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code: def setModelPath(self, model_path: str):
      extension_check(model_path)
      self.__detector.setModelPath(model_path)
  
imports: import os, warnings
  from tkinter import Image
  from collections import defaultdict
  from typing import List, Tuple, Dict, Union
  from PIL import Image
  import torchvision
  import numpy
  from enum import Enum
  import torch
  import cv2
  from typing import Union, List
  from ..yolov3.yolov3 import YoloV3
  from ..yolov3.tiny_yolov3 import YoloV3Tiny
  from ..yolov3.utils import draw_bbox_and_label, get_predictions, prepare_image
  from ..retinanet.utils import read_image, draw_bounding_boxes_and_labels, tensor_to_ndarray
  import uuid
  from ..backend_check.model_extension import extension_check
label_desc: Use image node_id 5 for calling the VideoObjectDetection obj's underlying member method code with example usage: obj.setModelPath(model_path) without return types