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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def multi_gaussian_lens(self, kwargs_lens, model_bool_list=None, e1=0, e2=0, n_comp=20):
 """ multi-gaussian lens model in convergence space :param kwargs_lens: :param n_comp: :return: """

        if 'center_x' in kwargs_lens[0]:
            center_x = kwargs_lens[0]['center_x']
            center_y = kwargs_lens[0]['center_y']
        else:
            raise ValueError('no keyword center_x defined!')
        theta_E = self._lensModelExtensions.effective_einstein_radius(kwargs_lens)
        r_array = np.logspace(-4, 2, 200) * theta_E
        x_coords, y_coords = param_util.transform_e1e2(r_array, np.zeros_like(r_array), e1=-e1, e2=-e2)
        x_coords += center_x
        y_coords += center_y
        #r_array = np.logspace(-2, 1, 50) * theta_E
        if model_bool_list is None:
            model_bool_list = [True] * len(kwargs_lens)
        kappa_s = np.zeros_like(r_array)
        for i in range(len(kwargs_lens)):
            if model_bool_list[i] is True:
                kappa_s += self.LensModel.kappa(x_coords, y_coords, kwargs_lens, k=i)
        amplitudes, sigmas, norm = mge.mge_1d(r_array, kappa_s, N=n_comp)
        return amplitudes, sigmas, center_x, center_y