human
Solve the following python programming problem given the following code:
def boston_example(optimize = True, plot = True) : 
    raise NotImplementedError ('Needs updating') 
    import sklearn 
    from sklearn.cross_validation import KFold 
    optimizer = 'bfgs' 
    messages = 0 
    data = datasets.boston_housing () 
    degrees_freedoms = [3, 5, 8, 10] 
    X = data ['X'].copy () 
    Y = data ['Y'].copy () 
    X = (X - X.mean (axis = 0)) 
    X = (X / X.std (axis = 0)) 
    Y = (Y - Y.mean ()) 
    Y = (Y / Y.std ()) 
    num_folds = 10 
    kf = KFold (len (Y), n_folds = num_folds, indices = True) 
    num_models = (len (degrees_freedoms) + 3) 
    score_folds = np.zeros ((num_models, num_folds)) 
    pred_density = score_folds.copy () 
    def rmse(Y, Ystar) : 
        return np.sqrt (np.mean (((Y - Ystar) ** 2))) 
for (n, (train, test)) in enumerate (kf) : 
        (X_train, X_test, Y_train, Y_test) = (X [train], X [test], Y [train], Y [test]) 
        print ('Fold {}'.format (n)) 
        noise = 0.1 
        rbf_len = 0.5 
        data_axis_plot = 4 
        kernelstu = ((GPy.kern.RBF (X.shape [1]) + GPy.kern.white (X.shape [1])) + GPy.kern.bias (X.shape [1])) 
        kernelgp = ((GPy.kern.RBF (X.shape [1]) + GPy.kern.white (X.shape [1])) + GPy.kern.bias (X.shape [1])) 
        score_folds [(0, n)] = rmse (Y_test, np.mean (Y_train)) 
        print ('Gauss GP') 
        mgp = GPy.models.GPRegression (X_train.copy (), Y_train.copy (), kernel = kernelgp.copy ()) 
        mgp.constrain_fixed ('.*white', 1e-05) 
        mgp ['.*len'] = rbf_len 
        mgp ['.*noise'] = noise 
        print (mgp) 
        if optimize : 
            mgp.optimize (optimizer = optimizer, messages = messages) 
Y_test_pred = mgp.predict (X_test) 
        score_folds [(1, n)] = rmse (Y_test, Y_test_pred [0]) 
        pred_density [(1, n)] = np.mean (mgp.log_predictive_density (X_test, Y_test)) 
        print (mgp) 
        print (pred_density) 
        print ('Gaussian Laplace GP') 
        (N, D) = Y_train.shape 
        g_distribution = GPy.likelihoods.noise_model_constructors.gaussian...

    
    

    Fix the buggy line: print ('Average scores: {}'.format (np.mean (ax, 1)))
assistant
#Here is Python code to fix the buggy line: print ('Average scores: {}'.

print ('Average scores: {}'.format (np.mean (score_folds, 1)))