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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 _set_mode_kappa_Chaput(self, i_sigma, i_temp, weights):
 """Calculate mode kappa by the way in Laurent Chaput's PRL paper. This gives the different result from _set_mode_kappa and requires more memory space. """


        X = self._get_X(i_temp, weights, self._gv).ravel()
        num_ir_grid_points = len(self._ir_grid_points)
        num_band = self._primitive.get_number_of_atoms() * 3
        size = num_ir_grid_points * num_band * 3
        v = self._collision_matrix[i_sigma, i_temp].reshape(size, size)
        solver = _select_solver(self._pinv_solver)
        if solver in [1, 2, 4, 5]:
            v = v.T
        e = self._get_eigvals_pinv(i_sigma, i_temp)
        t = self._temperatures[i_temp]

        omega_inv = np.empty(v.shape, dtype='double', order='C')
        np.dot(v, (e * v).T, out=omega_inv)
        Y = np.dot(omega_inv, X)
        self._set_f_vectors(Y, num_ir_grid_points, weights)
        elems = ((0, 0), (1, 1), (2, 2), (1, 2), (0, 2), (0, 1))
        for i, vxf in enumerate(elems):
            mat = self._get_I(vxf[0], vxf[1], num_ir_grid_points * num_band)
            self._mode_kappa[i_sigma, i_temp, :, :, i] = 0
            if mat is not None:
                np.dot(mat, omega_inv, out=mat)
                # vals = (X ** 2 * np.diag(mat)).reshape(-1, 3).sum(axis=1)
                # vals = vals.reshape(num_ir_grid_points, num_band)
                # self._mode_kappa[i_sigma, i_temp, :, :, i] = vals
                w = diagonalize_collision_matrix(mat,
                                                 pinv_solver=self._pinv_solver,
                                                 log_level=self._log_level)
                if solver in [1, 2, 4, 5]:
                    mat = mat.T
                spectra = np.dot(mat.T, X) ** 2 * w
                for s, eigvec in zip(spectra, mat.T):
                    vals = s * (eigvec ** 2).reshape(-1, 3).sum(axis=1)
                    vals = vals.reshape(num_ir_grid_points, num_band)
                    self._mode_kappa[i_sigma, i_temp, :, :, i] += vals

        factor = self._conversion_factor * Kb * t ** 2
        self._mode_kappa[i_sigma, i_temp] *= factor