def METHOD_NAME(generator):
    """Test Covariance Norms compute metric."""
    time = datetime.datetime.now()
    # Multiple tracks present at two timesteps
    tracks = {Track(states=[GaussianState(state_vector=[[1], [2], [1], [2]], timestamp=time,
                                          covar=np.diag([i, i, i, i])),
                            GaussianState(state_vector=[[1.5], [2.5], [1.5], [2.5]],
                                          timestamp=time + datetime.timedelta(seconds=1),
                                          covar=np.diag([i+0.5, i+0.5, i+0.5, i+0.5]))])
              for i in range(5)}
    manager = MultiManager([generator])
    manager.add_data({'tracks': tracks})
    main_metric = generator.compute_metric(manager)
    first_association, second_association = main_metric.value
    assert main_metric.title == f"{generator._type} of Covariance Norms Metric"
    assert main_metric.time_range.start_timestamp == time
    assert main_metric.time_range.end_timestamp == time + datetime.timedelta(
        seconds=1)
    divide = len(tracks) if isinstance(generator, MeanofCovarianceNormsMetric) else 1
    assert first_association.title == f"Covariance Matrix Norm {generator._type}"
    assert first_association.value == 20 / divide
    assert first_association.timestamp == time
    assert first_association.generator == generator
    assert second_association.title == f"Covariance Matrix Norm {generator._type}"
    assert second_association.value == 25 / divide
    assert second_association.timestamp == time + datetime.timedelta(seconds=1)
    assert second_association.generator == generator