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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 regression_tikhonov(G, y, M, tau=0):
 r"""Solve a regression problem on graph via Tikhonov minimization. The function solves .. math:: \operatorname*{arg min}_x \| M x - y \|_2^2 + \tau \ x^T L x if :math:`\tau > 0`, and .. math:: \operatorname*{arg min}_x x^T L x \ \text{ s. t. } \ y = M x otherwise. Parameters G : :class:`pygsp.graphs.Graph` y : array, length G.n_vertices Measurements. M : array of boolean, length G.n_vertices Masking vector. tau : float Regularization parameter. Returns ------- x : array, length G.n_vertices Recovered values :math:`x`. Examples -------- Create a smooth ground truth signal: Construct a measurement signal from a binary mask: Solve the regression problem by reconstructing the signal: Plot the results: """


    if tau > 0:

        y[M == False] = 0

        if sparse.issparse(G.L):

            def Op(x):
                return (M * x.T).T + tau * (G.L.dot(x))

            LinearOp = sparse.linalg.LinearOperator([G.N, G.N], Op)

            if y.ndim > 1:
                sol = np.empty(shape=y.shape)
                res = np.empty(shape=y.shape[1])
                for i in range(y.shape[1]):
                    sol[:, i], res[i] = sparse.linalg.cg(
                        LinearOp, y[:, i])
            else:
                sol, res = sparse.linalg.cg(LinearOp, y)

            # TODO: do something with the residual...
            return sol

        else:

            # Creating this matrix may be problematic in term of memory.
            # Consider using an operator instead...
            if type(G.L).__module__ == np.__name__:
                LinearOp = np.diag(M*1) + tau * G.L
            return np.linalg.solve(LinearOp, M * y)

    else:

        if np.prod(M.shape) != G.n_vertices:
            raise ValueError("M should be of size [G.n_vertices,]")

        indl = M
        indu = (M == False)

        Luu = G.L[indu, :][:, indu]
        Wul = - G.L[indu, :][:, indl]

        if sparse.issparse(G.L):
            sol_part = sparse.linalg.spsolve(Luu, Wul.dot(y[indl]))
        else:
            sol_part = np.linalg.solve(Luu, np.matmul(Wul, y[indl]))

        sol = y.copy()
        sol[indu] = sol_part

        return sol