code: def test_perform_inference(self):
      from sahi.models.yolov8onnx import Yolov8OnnxDetectionModel
  
      # Init model
      download_yolov8n_onnx_model()
  
      yolov8_onnx_detection_model = Yolov8OnnxDetectionModel(
          model_path=Yolov8ONNXTestConstants.YOLOV8N_ONNX_MODEL_PATH,
          confidence_threshold=CONFIDENCE_THRESHOLD,
          iou_threshold=IOU_THRESHOLD,
          device=MODEL_DEVICE,
          category_mapping={"0": "something"},
          load_at_init=True,
          image_size=IMAGE_SIZE,
      )
  
      # Prepare image
      image_path = "tests/data/small-vehicles1.jpeg"
      image = cv2.imread(image_path)
  
      # Perform inference
      yolov8_onnx_detection_model.perform_inference(image)
      original_predictions = (
          yolov8_onnx_detection_model.original_predictions
      )
  
      boxes = original_predictions[0]
  
      # Find most confident bbox for car
      best_box_index = np.argmax(boxes[boxes[:, 5] == 2][:, 4])
      best_bbox = boxes[best_box_index]
  
      # Compare
      desired_bbox = [603, 239, 629, 259]
      predicted_bbox = best_bbox.tolist()
      margin = 2
  
      for ind, point in enumerate(predicted_bbox[:4]):
          assert (
              point < desired_bbox[ind] + margin
              and point > desired_bbox[ind] - margin
          )
  
      for box in boxes[0]:
          self.assertGreaterEqual(
              predicted_bbox[4], CONFIDENCE_THRESHOLD
          )
  
file_path: sahi/tests/test_yolov8onnx.py
imports: import unittest
  import cv2
  import numpy
  from sahi.utils.yolov8onnx import Yolov8ONNXTestConstants, download_yolov8n_onnx_model
label_desc: Use image node_id 3 for calling the TestYolov8OnnxDetectionModel obj's underlying member method code with example usage: obj.test_perform_inference() without return types