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Solve the following python programming problem given the following code:
def hdquantiles(data, prob = list ([0.25, 0.5, 0.75]), axis = None, var = False) : 
    '
    Computes quantile estimates with the Harrell-Davis method.

    The quantile estimates are calculated as a weighted linear combination
    of order statistics.

    Parameters
    ----------
    data : array_like
        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.
    var : bool, optional
        Whether to return the variance of the estimate.

    Returns
    -------
    hdquantiles : MaskedArray
        A (p,) array of quantiles (if `var` is False), or a (2,p) array of
        quantiles and variances (if `var` is True), where ``p`` is the
        number of quantiles.

    ' 
    def _hd_1D(data, prob, var) : 
        'Computes the HD quantiles for a 1D array. Returns nan for invalid data.' 
        xsorted = np.squeeze (np.sort (data.compressed ().view (ndarray))) 
        n = xsorted.size 
        hd = np.empty ((2, len (prob)), float_) 
        if (n < 2) : 
            hd.flat = np.nan 
            if var : 
                return hd 
return hd [0] 
v = (np.arange ((n + 1)) / float (n)) 
        betacdf = beta.cdf 
        for (i, p) in enumerate (prob) : 
            _w = betacdf (v, ((n + 1) * p), ((n + 1) * (1 - p))) 
            w = (_w [1 :] - _w [: (- 1)]) 
            hd_mean = np.dot (w, xsorted) 
            hd [(0, i)] = hd_mean 
            hd [(1, i)] = np.dot (w, ((xsorted - hd_mean) ** 2)) 
hd [(0, (prob == 0))] = xsorted [0] 
        hd [(0, (prob == 1))] = xsorted [(- 1)] 
        if var : 
            hd [(1, (prob == 0))] = hd [(1, (prob == 1))] = np.nan 
            return hd 
return hd [0] 
data = ma.array (data, copy = False, dtype = float_) 
    p = np.array (prob, copy = False, ndmin = 1) 
    if ((axis is None) or (data.ndim == 1)) : 
        result = _hd_1D (data, p, var) 
else : 
        if (_hd_1D.ndim > 2) : 
           ...

    
    

    Fix the buggy line: if (_hd_1D.ndim > 2) :
assistant
#Here is Python program to fix the buggy line: if (_hd_1D.

if (data.ndim > 2) :