def METHOD_NAME():
    mean = np.array([0.1, 0.2, 0.3])
    cov = np.array([
        [25.0, 0.0, 0.0],
        [0.0, 4.0, 0.0],
        [0.0, 0.0, 9.0]
    ])
    ellipsoid2origin, radii = pu.to_ellipsoid(mean, cov)
    assert_array_almost_equal(ellipsoid2origin[:3, 3], mean)
    assert_array_almost_equal(
        ellipsoid2origin[:3, :3],
        np.array([
            [0, 0, 1],
            [1, 0, 0],
            [0, 1, 0]
        ])
    )
    assert_array_almost_equal(radii, np.array([2.0, 3.0, 5.0]))
    rng = np.random.default_rng(28)
    for _ in range(5):
        R = pr.matrix_from_axis_angle(pr.random_axis_angle(rng))
        std_devs = np.array([1.0, 2.0, 3.0])
        cov = np.dot(R, np.dot(np.diag(std_devs ** 2), R.T))
        ellipsoid2origin, radii = pu.to_ellipsoid(mean, cov)
        assert_array_almost_equal(ellipsoid2origin[:3, 3], mean)
        # multiple solutions for the rotation matrix are possible because of
        # ellipsoid symmetries, hence, we only check the absolute values
        assert_array_almost_equal(np.abs(ellipsoid2origin[:3, :3]), np.abs(R))
        assert_array_almost_equal(radii, std_devs)