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: 'setGpuUsage' function allows you to set the GPUs to be used while training
  train_gpu can be:
  - an integer, indicating the number of GPUs to use
  - a list of integers, indicating the id of the GPUs to be used
  - a string, indicating the it og the id of the GPUs to be used, separated by commas
  :param train_gpus: gpus where to run
  :return:
docstr_tok: ["'setGpuUsage","'","function","allows","you","to","set","the","GPUs","to","be","used","while","training","train_gpu","can","be",":","-","an","integer",",","indicating","the","number","of","GPUs","to","use","-","a","list","of","integers",",","indicating","the","id","of","the","GPUs","to","be","used","-","a","string",",","indicating","the","it","og","the","id","of","the","GPUs","to","be","used",",","separated","by","commas",":","param","train_gpus",":","gpus","where","to","run",":","return",":"]
code: def setGpuUsage(self, train_gpus):
      """
      'setGpuUsage' function allows you to set the GPUs to be used while training
      train_gpu can be:
      - an integer, indicating the number of GPUs to use
      - a list of integers, indicating the id of the GPUs to be used
      - a string, indicating the it og the id of the GPUs to be used, separated by commas
      :param train_gpus: gpus where to run
      :return:
      """
      # train_gpus, could be a string separated by comma, or a list of int or the number of GPUs to be used
      if type(train_gpus) == str:
          train_gpus = train_gpus.split(",")
      if type(train_gpus) == int:
          train_gpus = range(train_gpus)
      # let it as a string separated by commas
      self.__train_gpus = ",".join([str(gpu) for gpu in train_gpus])
  
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 DetectionModelTrainer obj's underlying member method code with example usage: obj.setGpuUsage(train_gpus) without return types