human
Solve the following python programming problem given the following code:
def geweke_test(K, N_iter = 10000) : 
    D = 1 
    M = 20 
    l = 10.0 
    L = 100.0 
    v = 1.0 
    N_max = 10 
    Z = np.linspace (0, L, M) [:, None] 
    kernel = RBF (1, lengthscale = l, variance = v) 
    model = MultinomialGP (K, kernel, D = D) 
    (X, psi) = model.generate (Z = Z, N = (N_max * np.ones (M, dtype = np.int)), keep = True, full_output = True) 
    data = model.data_list [0] 
    data ['psi'] = psi 
    model.resample_omega () 
    def _resample_X() : 
        pis = model.pi (data) 
        X = np.array ([np.random.multinomial (N_max, pis [m]) for m in xrange (M)]) 
        N = N_vec (X).astype (np.float) 
        kappa = kappa_vec (X) 
        data ['X'] = X 
        data ['N'] = N 
        data ['kappa'] = kappa 
Xs = [] 
    psi_samples = [] 
    for itr in xrange (N_iter) : 
        if ((itr % 10) == 0) : 
            print ('Iteration ', itr) 
_resample_X () 
        model.resample_model () 
        Xs.append (data ['X'].copy ()) 
        psi_samples.append (data ['psi'].copy ()) 
psi_samples = np.array (psi_samples) 
    psi_mean = psi_samples.mean (0) 
    psi_std = psi_samples.std (0) 
    print ('Mean psi: ', psi_mean, ' +- ', psi_std) 
    (fig, axs) = plt.subplots ((K - 1), 1) 
    for (k, ax) in enumerate (axs) : 
        ax.errorbar (Z, model [:, k], yerr = psi_std [:, k], color = 'b', lw = 2) 
        ax.plot (Z, np.zeros_like (Z), '-k') 
        ax.plot (Z, np.ones_like (Z), ':k') 
        ax.plot (Z, (- np.ones_like (Z)), ':k') 
        ax.set_xlabel ('Z') 
        ax.set_ylabel (('$\\psi_{%d}$' % k)) 
plt.ioff () 
    plt.show () 

    
    

    Fix the buggy line: ax.errorbar (Z, model [:, k], yerr = psi_std [:, k], color = 'b', lw = 2)
assistant
#Here is Python code to fix the buggy line: ax.

ax.errorbar (Z, psi_mean [:, k], yerr = psi_std [:, k], color = 'b', lw = 2)