human
Solve the following python programming problem given the following code:
def hdquantiles_sd(data, prob = list ([0.25, 0.5, 0.75]), axis = None) : 
    '
    The standard error of the Harrell-Davis quantile estimates by jackknife.

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

    Returns
    -------
    hdquantiles_sd : MaskedArray
        Standard error of the Harrell-Davis quantile estimates.

    ' 
    def _hdsd_1D(data, prob) : 
        'Computes the std error for 1D arrays.' 
        xsorted = np.sort (data.compressed ()) 
        n = len (xsorted) 
        hdsd = np.empty (len (prob), float_) 
        if (n < 2) : 
            hdsd.flat = np.nan 
vv = (np.arange (n) / float ((n - 1))) 
        betacdf = beta.cdf 
        for (i, p) in enumerate (prob) : 
            _w = betacdf (vv, ((n + 1) * p), ((n + 1) * (1 - p))) 
            w = (_w [1 :] - _w [: (- 1)]) 
            mx_ = np.fromiter ([np.dot (w, xsorted [np.r_ [(list (range (0, k)), list (range ((k + 1), n)))].astype (int_)]) for k in range (n)], dtype = float_) 
            mx_var = ((np.array (mx_.var (), copy = False, ndmin = 1) * n) / float ((n - 1))) 
            hdsd [i] = (float ((n - 1)) * np.sqrt ((np.diag (mx_var).diagonal () / float (n)))) 
return hdsd 
data = ma.array (data, copy = False, dtype = float_) 
    p = np.array (prob, copy = False, ndmin = 1) 
    if (axis is None) : 
        result = _hdsd_1D (data, p) 
else : 
        if (data.ndim > 2) : 
            raise ValueError (("Array 'data' must be at most two dimensional, but got data.ndim = %d" % data.ndim)) 
result = ma.apply_along_axis (axis, axis, data, p) 
return ma.fix_invalid (result, copy = False).ravel () 

    
    

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

result = ma.apply_along_axis (_hdsd_1D, axis, data, p)