human
Solve the following python programming problem given the following code:
@ skip_if_32bit 
def test_incremental_variance_numerical_stability() : 
    def np_var(A) : 
        return A.var (axis = 0) 
def one_pass_var(X) : 
        n = X.shape [0] 
        exp_x2 = ((X ** 2).sum (axis = 0) / n) 
        expx_2 = ((X.sum (axis = 0) / n) ** 2) 
        return (exp_x2 - expx_2) 
def two_pass_var(X) : 
        mean = X.mean (axis = 0) 
        Y = X.copy () 
        return np.mean (((Y - mean) ** 2), axis = 0) 
def naive_mean_variance_update(x, last_mean, last_variance, last_sample_count) : 
        updated_sample_count = (last_sample_count + 1) 
        samples_ratio = (last_sample_count / float (updated_sample_count)) 
        updated_mean = ((x / updated_sample_count) + (last_mean * samples_ratio)) 
        updated_variance = ((last_variance * samples_ratio) + (((x - last_mean) * (x - updated_mean)) / updated_sample_count)) 
        return (updated_mean, updated_variance, updated_sample_count) 
tol = 200 
    n_features = 2 
    n_samples = 10000 
    x1 = np.array (100000000.0, dtype = np.float64) 
    x2 = np.log (1e-05, dtype = np.float64) 
    A0 = (x1 * np.ones (((n_samples // 2), n_features), dtype = np.float64)) 
    A1 = (x2 * np.ones (((n_samples // 2), n_features), dtype = np.float64)) 
    A = np.vstack ((A0, A1)) 
    if (np.abs ((np_var (A) - two_pass_var (A))).max () < 1e-06) : 
        stable_var = np_var 
else : 
        stable_var = two_pass_var 
assert_greater (np.abs ((stable_var (A) - one_pass_var (A))).max (), tol) 
    (mean, var, n) = (A0 [0, :], np.zeros (n_features), (n_samples // 2)) 
    for i in range (A1.shape [0]) : 
        (mean, var, n) = naive_mean_variance_update (A1 [i, :], mean, var, n) 
assert_equal (n, A.shape [0]) 
    assert_greater (np.abs ((A.mean (axis = 0) - mean)).max (), 1e-06) 
    assert_greater (np.abs ((stable_var (A) - var)).max (), tol) 
    (mean, var, n) = (A0 [0, :], np.zeros (n_features), (n_samples // 2)) 
    for i in range (A1.shape [0]) : 
        (mean, var, n) = _incremental_mean_and_var (A1 [i, :].reshape ((1, A1.shape...

    
    

    Fix the buggy line: (mean, var, n) = _incremental_mean_and_var (A1 [i, :].reshape ((1, A1.shape [1])), mean, A0, n)
assistant
#Here is Python code to fix the buggy line: (mean, var, n) = _incremental_mean_and_var (A1 [i, :].

(mean, var, n) = _incremental_mean_and_var (A1 [i, :].reshape ((1, A1.shape [1])), mean, var, n)