code: def generate_predictor(model_config, *args):
      if model_config["backend"] == "detectron2":
          cfg = get_cfg()
          cfg.MODEL.DEVICE = torch.device(
              "cuda" if torch.cuda.is_available() else "cpu"
          )
          cfg.merge_from_file(
              model_zoo.get_config_file(model_config["model"])
          )
          cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = model_config[
              "threshold"
          ]
          cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(
              model_config["model"]
          )
  
          predictor = DefaultPredictor(
              cfg,
          )
      else:
          raise ValueError("Incorrect backend.")
  
      return predictor
  
imports: from collections import OrderedDict
  from typing import Callable
  import cv2
  import torch
  from hook import HookBase
label_desc: Use image node_id 1 for calling a global function with example usage: generate_predictor(model_config) and returns: predictor