def METHOD_NAME(self):
    # 9 samples -> plot them for a better visualization (e.g. with GeoGebra)
    a = [2, 4]
    b = [4, 1]
    b_bis = [4, 1]
    c = [-2, 1]
    d = [-1, 5]
    e = [1.6, 3.6]
    f = [3, 1]
    g = [1, 2]
    h = [9, 5]
    i = [49, 4]  # This sample is not sent to the diversity filter
    samples = np.array([a, b, b_bis, c, d, e, f, g, h, i])
    selected_samples = al.diversity_filter(samples, np.arange(9), 4)
    self.assertTrue(0 not in selected_samples)
    self.assertTrue(
        (1 in selected_samples) ^ (2 in selected_samples)
    )  # Either b or b_bis is kept
    self.assertTrue(3 in selected_samples)
    self.assertTrue(4 in selected_samples)
    self.assertTrue(5 not in selected_samples)
    self.assertTrue(6 not in selected_samples)
    self.assertTrue(7 not in selected_samples)
    self.assertTrue(8 in selected_samples)
    self.assertTrue(
        (samples == np.array([a, b, b_bis, c, d, e, f, g, h, i])).all()
    )  # Check that the original array was not modified