human
Solve the following python programming problem given the following code:
def mjci(data, prob = [0.25, 0.5, 0.75], axis = None) : 
    '
    Returns the Maritz-Jarrett estimators of the standard error of selected
    experimental quantiles of the data.

    Parameters
    ----------
    data : ndarray
        Data array.
    prob : sequence, optional
        Sequence of quantiles to compute.
    axis : int or None, optional
        Axis along which to compute the quantiles. If None, use a flattened
        array.

    ' 
    def _mjci_1D(data, p) : 
        data = np.sort (data.compressed ()) 
        n = data.size 
        prob = ((np.array (p) * n) + 0.5).astype (int_) 
        betacdf = beta.cdf 
        mj = np.empty (len (prob), float_) 
        x = (np.arange (1, (n + 1), dtype = float_) / n) 
        y = (x - (1.0 / n)) 
        for (i, m) in enumerate (prob) : 
            W = (betacdf (x, (m - 1), (n - m)) - betacdf (y, (m - 1), (n - m))) 
            C1 = np.dot (W, data) 
            C2 = np.dot (W, (data ** 2)) 
            mj [i] = np.sqrt ((C2 - (C1 ** 2))) 
return mj 
data = ma.array (data, copy = False) 
    if (data.ndim > 2) : 
        raise ValueError (("Array 'data' must be at most two dimensional, but got data.ndim = %d" % data.ndim)) 
p = np.array (prob, copy = False, ndmin = 1) 
    if (axis is None) : 
        return _mjci_1D (data, p) 
else : 
        return ma.apply_along_axis (p, axis, data, p) 

    
    

    Fix the buggy line: return ma.apply_along_axis (p, axis, data, p)
assistant
#Here is Python code to fix the buggy line: return ma.

return ma.apply_along_axis (_mjci_1D, axis, data, p)