old_contents: import modules.benchmark_pipeline
  
  
  # ------------------------------------------------------------------------
  # ---------------------- test entire benchmark pipeline -------------------
  # -------------------------------------------------------------------------
  def test_benchmark():
    find_icon_benchmark = modules.benchmark_pipeline.BenchmarkPipeline()
    correctness, avg_time_secs, avg_memory_mibs = find_icon_benchmark.evaluate()
    assert avg_memory_mibs <= 1000
    assert avg_time_secs <= 60
    assert correctness.accuracy >= 0
    assert correctness.precision >= 0
    assert correctness.recall >= 0
  
  
  def test_single_instance_benchmark():
    find_icon_single_instance = modules.benchmark_pipeline.BenchmarkPipeline(
        tfrecord_path="datasets/benchmark_single_instance.tfrecord")
    correctness, avg_time_secs, avg_memory_mibs = find_icon_single_instance.evaluate(
        find_icon_option="shape-context")
    # current results to prevent any regressions due to algorithm changes
    assert avg_memory_mibs <= 1000
    assert avg_time_secs <= 5
    assert correctness.accuracy >= 0.8
    assert correctness.precision >= 0.8
    assert correctness.recall >= 0.8
  
  
  def test_multi_instance():
    find_icon_multi_instance = modules.benchmark_pipeline.BenchmarkPipeline(
        tfrecord_path="datasets/benchmark_multi_instance.tfrecord")
    # test responsiveness to different desired levels of confidence (from 0 to 1)
    correctness, _, _ = find_icon_multi_instance.evaluate(
        find_icon_option="shape-context",
        desired_confidence=0.9, multi_instance_icon=True)
    assert correctness.precision >= 0.7
  
    find_icon_multi_instance = modules.benchmark_pipeline.BenchmarkPipeline(
        tfrecord_path="datasets/benchmark_multi_instance.tfrecord")
    correctness, _, _ = find_icon_multi_instance.evaluate(
        find_icon_option="shape-context",
        desired_confidence=0.2, multi_instance_icon=True)
    assert correctness.recall >= 0.8
  
new_contents: from modules import icon_finder_shape_context
  import modules.benchmark_pipeline
  
  
  # ------------------------------------------------------------------------
  # ---------------------- test entire benchmark pipeline -------------------
  # -------------------------------------------------------------------------
  def test_benchmark():
    find_icon_benchmark = modules.benchmark_pipeline.BenchmarkPipeline()
    correctness, avg_time_secs, avg_memory_mibs = find_icon_benchmark.evaluate()
    assert avg_memory_mibs <= 1000
    assert avg_time_secs <= 60
    assert correctness.accuracy >= 0
    assert correctness.precision >= 0
    assert correctness.recall >= 0
  
  
  def test_single_instance_benchmark():
    find_icon_single_instance = modules.benchmark_pipeline.BenchmarkPipeline(
        tfrecord_path="datasets/benchmark_single_instance.tfrecord")
    correctness, avg_time_secs, avg_memory_mibs = find_icon_single_instance.evaluate(
        icon_finder_object=icon_finder_shape_context.IconFinderShapeContext())
    # current results to prevent any regressions due to algorithm changes
    assert avg_memory_mibs <= 1000
    assert avg_time_secs <= 5
    assert correctness.accuracy >= 0.8
    assert correctness.precision >= 0.8
    assert correctness.recall >= 0.8
  
  
  def test_multi_instance():
    find_icon_multi_instance = modules.benchmark_pipeline.BenchmarkPipeline(
        tfrecord_path="datasets/benchmark_multi_instance.tfrecord")
    # test responsiveness to different desired levels of confidence (from 0 to 1)
    correctness, _, _ = find_icon_multi_instance.evaluate(
        icon_finder_object=icon_finder_shape_context.IconFinderShapeContext(
            desired_confidence=0.9),
        multi_instance_icon=True)
    assert correctness.precision >= 0.7
  
    find_icon_multi_instance = modules.benchmark_pipeline.BenchmarkPipeline(
        tfrecord_path="datasets/benchmark_multi_instance.tfrecord")
    correctness, _, _ = find_icon_multi_instance.evaluate(
        icon_finder_object=icon_finder_shape_context.IconFinderShapeContext(
            desired_confidence=0.1),
        multi_instance_icon=True)
    assert correctness.recall >= 0.8
  
subject: Update integration test