code: def test_predictions(self):
      test_image_urls = [
          "https://raw.githubusercontent.com/ultralytics/yolov5/master/data/images/bus.jpg",
          "https://www.aljazeera.com/wp-content/uploads/2022/10/2022-04-28T192650Z_1186456067_UP1EI4S1I0P14_RTRMADP_3_SOCCER-ENGLAND-MUN-CHE-REPORT.jpg",
      ]
      variant = "n"
      depth, width, ratio = get_variant_multiples(variant)
      TinyYolov8 = YOLOv8(w=width, r=ratio, d=depth, num_classes=80)
      state_dict = safe_load(
          fetch(
              f"https://gitlab.com/r3sist/yolov8_weights/-/raw/master/yolov8{variant}.safetensors"
          )
      )
      load_state_dict(TinyYolov8, state_dict)
  
      for i in range(len(test_image_urls)):
          img = cv2.imdecode(
              np.frombuffer(
                  fetch(test_image_urls[i]).read_bytes(), np.uint8
              ),
              1,
          )
          test_image = preprocess([img])
          predictions = TinyYolov8(test_image)
          post_predictions = postprocess(
              preds=predictions, img=test_image, orig_imgs=[img]
          )
          labels = label_predictions(post_predictions)
          assert (
              labels == {5: 1, 0: 4, 11: 1}
              if i == 0
              else labels == {0: 13, 29: 1, 32: 1}
          )
  
file_path: tinygrad/test/external/external_test_yolov8.py
imports: import numpy
  from examples.yolov8 import YOLOv8, get_variant_multiples, preprocess, postprocess, label_predictions
  import unittest
  import io, cv2
  import onnxruntime
  import ultralytics
  from tinygrad.nn.state import safe_load, load_state_dict
  from tinygrad.helpers import fetch
label_desc: Use image node_id 2 for calling the TestYOLOv8 obj's underlying member method code with example usage: obj.test_predictions() without return types