code: def test_pytorch_predict(art_warning, get_pytorch_faster_rcnn):
      object_detector, x_test, _ = get_pytorch_faster_rcnn
      object_seeker = PyTorchObjectSeeker(
          model=object_detector.model,
          input_shape=object_detector.input_shape,
          channels_first=object_detector.channels_first,
          optimizer=object_detector.optimizer,
          clip_values=object_detector.clip_values,
          attack_losses=object_detector.attack_losses,
          detector_type="Faster-RCNN",
          num_lines=3,
          confidence_threshold=0.3,
          iou_threshold=0.4,
          prune_threshold=0.5,
          device_type="cpu",
      )
  
      try:
          result = object_seeker.predict(x=x_test)
  
          assert len(result) == len(x_test)
          assert list(result[0].keys()) == ["boxes", "labels", "scores"]
          assert np.all(result[0]["scores"] >= 0.3)
  
      except ARTTestException as e:
          art_warning(e)
  
file_path: adversarial-robustness-toolbox/tests/estimators/object_detection/test_object_seeker_faster_rcnn.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import numpy
  import pytest
  from art.estimators.certification.object_seeker import PyTorchObjectSeeker
  from tests.utils import ARTTestException
label_desc: Use image node_id 2 for calling a global function with example usage: test_pytorch_predict(art_warning, get_pytorch_faster_rcnn) without return types