code: def from_pretrained(
      model_type: str,
      model_path: Optional[str] = None,
      model: Optional[Any] = None,
      config_path: Optional[str] = None,
      device: Optional[str] = None,
      mask_threshold: float = 0.5,
      confidence_threshold: float = 0.3,
      category_mapping: Optional[Dict] = None,
      category_remapping: Optional[Dict] = None,
      load_at_init: bool = True,
      image_size: int = None,
      **kwargs,
  ):
      """
      Loads a DetectionModel from given path.
  
      Args:
          model_type: str
              Name of the detection framework (example: "yolov5", "mmdet", "detectron2")
          model_path: str
              Path of the detection model (ex. 'model.pt')
          config_path: str
              Path of the config file (ex. 'mmdet/configs/cascade_rcnn_r50_fpn_1x.py')
          device: str
              Device, "cpu" or "cuda:0"
          mask_threshold: float
              Value to threshold mask pixels, should be between 0 and 1
          confidence_threshold: float
              All predictions with score < confidence_threshold will be discarded
          category_mapping: dict: str to str
              Mapping from category id (str) to category name (str) e.g. {"1": "pedestrian"}
          category_remapping: dict: str to int
              Remap category ids based on category names, after performing inference e.g. {"car": 3}
          load_at_init: bool
              If True, automatically loads the model at initalization
          image_size: int
              Inference input size.
      Returns:
          Returns an instance of a DetectionModel
      Raises:
          ImportError: If given {model_type} framework is not installed
      """
  
      model_class_name = MODEL_TYPE_TO_MODEL_CLASS_NAME[model_type]
      DetectionModel = import_model_class(model_type, model_class_name)
  
      return DetectionModel(
          model_path=model_path,
          model=model,
          config_path=config_path,
          device=device,
          mask_threshold=mask_threshold,
          confidence_threshold=confidence_threshold,
          category_mapping=category_mapping,
          category_remapping=category_remapping,
          load_at_init=load_at_init,
          image_size=image_size,
          **kwargs,
      )
  
label_desc: Use image node_id 1 for calling the AutoDetectionModel obj's underlying member method code with example usage: obj.from_pretrained(model_type, model_path, model, config_path, device, mask_threshold, confidence_threshold, category_mapping, category_remapping, load_at_init, image_size) and returns: DetectionModel