code: def test_predict(art_warning, get_pytorch_yolo):
      try:
          object_detector, x_test, _ = get_pytorch_yolo
  
          result = object_detector.predict(x=x_test)
  
          assert list(result[0].keys()) == ["boxes", "labels", "scores"]
  
          assert result[0]["boxes"].shape == (10647, 4)
          expected_detection_boxes = np.asarray(
              [0.0000000e00, 0.0000000e00, 1.6367816e02, 4.4342079e01]
          )
          np.testing.assert_array_almost_equal(
              result[0]["boxes"][2, :],
              expected_detection_boxes,
              decimal=3,
          )
  
          assert result[0]["scores"].shape == (10647,)
          expected_detection_scores = np.asarray(
              [
                  4.3653536e-08,
                  3.3987994e-06,
                  2.5681820e-06,
                  3.9782722e-06,
                  2.1766680e-05,
                  2.6138965e-05,
                  6.3377396e-05,
                  7.6248516e-06,
                  4.3447722e-06,
                  3.6515078e-06,
              ]
          )
          np.testing.assert_array_almost_equal(
              result[0]["scores"][:10],
              expected_detection_scores,
              decimal=6,
          )
  
          assert result[0]["labels"].shape == (10647,)
          expected_detection_classes = np.asarray(
              [0, 0, 14, 14, 14, 14, 14, 14, 14, 0]
          )
          np.testing.assert_array_almost_equal(
              result[0]["labels"][:10],
              expected_detection_classes,
              decimal=6,
          )
  
      except ARTTestException as e:
          art_warning(e)
  
file_path: adversarial-robustness-toolbox/tests/estimators/object_detection/test_pytorch_yolo.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import numpy
  import pytest
  from tests.utils import ARTTestException
label_desc: Use image node_id 1 for calling a global function with example usage: test_predict(art_warning, get_pytorch_yolo) without return types