code: def test_compute_loss(get_pytorch_detr):
      object_detector, x_test, _ = get_pytorch_detr
      # Create labels
      result = object_detector.predict(x_test)
  
      y = [
          {
              "boxes": result[0]["boxes"],
              "labels": result[0]["labels"],
              "scores": np.ones_like(result[0]["labels"]),
          },
          {
              "boxes": result[1]["boxes"],
              "labels": result[1]["labels"],
              "scores": np.ones_like(result[1]["labels"]),
          },
      ]
  
      # Compute loss
      loss = object_detector.compute_loss(x=x_test, y=y)
  
      assert pytest.approx(3.9634, abs=0.01) == float(loss)
  
file_path: adversarial-robustness-toolbox/tests/estimators/object_detection/test_pytorch_detection_transformer.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import numpy
  import pytest
label_desc: Use image node_id 7 for calling a global function with example usage: test_compute_loss(get_pytorch_detr) without return types