def METHOD_NAME(self):
    x = np.linspace(0.01, 2, 10)
    y = np.zeros_like(x)
    kwargs = {"alpha_Rs": 0.5, "Rs": 2, "center_x": 0, "center_y": 0}
    f_xx_nfw, f_xy_nfw, f_yx_nfw, f_yy_nfw = self.nfw.hessian(x, y, **kwargs)
    f_xx_cse, f_xy_cse, f_yx_cse, f_yy_cse = self.nfw_cse.hessian(
        x, y, e1=0, e2=0, **kwargs
    )
    npt.assert_almost_equal(f_xx_cse, f_xx_nfw, decimal=5)
    npt.assert_almost_equal(f_xy_cse, f_xy_nfw, decimal=5)
    npt.assert_almost_equal(f_yx_cse, f_yx_nfw, decimal=5)
    npt.assert_almost_equal(f_yy_cse, f_yy_nfw, decimal=5)
    f_xx_cse, f_xy_cse, f_yx_cse, f_yy_cse = self.nfw_cse_low_accuracy.hessian(
        x, y, e1=0, e2=0, **kwargs
    )
    npt.assert_almost_equal(f_xx_cse / f_xx_nfw, 1, decimal=1)
    npt.assert_almost_equal(f_xy_cse, f_xy_nfw, decimal=5)
    npt.assert_almost_equal(f_yx_cse, f_yx_nfw, decimal=5)
    npt.assert_almost_equal(f_yy_cse / f_yy_nfw, 1, decimal=1)