docstr: Prediction is performed using self.model and the prediction result is set to self._original_predictions.
  Args:
      image: np.ndarray
          A numpy array that contains the image to be predicted.
      image_size: int
          Inference input size.
docstr_tok: ["Prediction","is","performed","using","self.model","and","the","prediction","result","is","set","to","self._original_predictions",".","Args",":","image",":","np.ndarray","A","numpy","array","that","contains","the","image","to","be","predicted",".","image_size",":","int","Inference","input","size","."]
code: def perform_inference(
      self, image: np.ndarray, image_size: int = None
  ):
      """
      Prediction is performed using self.model and the prediction result is set to self._original_predictions.
      Args:
          image: np.ndarray
              A numpy array that contains the image to be predicted.
          image_size: int
              Inference input size.
      """
      self._original_predictions = self.model_type.end2end_detect(
          img=PIL.Image.fromarray(image),
          transforms=self.tfms,
          model=self.model,
          class_map=self.class_map,
          detection_threshold=self.confidence_threshold,
      )
  
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 2 for calling the IceSahiModel obj's underlying member method code with example usage: obj.perform_inference(image, image_size) without return types