code: def __init__(
      self,
      model_type: ModuleType,
      model: torch.nn.Module,
      class_map: ClassMap,
      tfms: albumentations_adapter.Adapter,
      confidence_threshold: float = 0.5,
  ):
      super().__init__(
          model_path="",
          confidence_threshold=confidence_threshold,
          load_at_init=False,
      )
  
      self.model_type = model_type
      self.class_map = class_map
      self.confidence_threshold = confidence_threshold
      self.model = model
      self.tfms = tfms
      self.category_mapping = self.class_map._class2id
  
imports: from types import ModuleType
  from icevision.imports import *
  from icevision.imports import *
  from icevision.core import *
  from icevision.data import *
  from icevision.utils.imageio import *
  from icevision.visualize.draw_data import *
  from icevision.visualize.utils import *
  from icevision.tfms.albumentations import albumentations_adapter
  from sahi.models.base import DetectionModel
  from sahi.prediction import ObjectPrediction
  from sahi.predict import get_sliced_prediction
label_desc: Use image node_id 1 to create a new IceSahiModel object from inherited base classes: DetectionModel with example: obj = IceSahiModel(model_type, model, class_map, tfms, confidence_threshold)