human
Solve the following python programming problem given the following code:
def _apply(self, func, name = None, window = None, center = None, check_minp = None, how = None, **kwargs) : 
    '
        Rolling statistical measure using supplied function. Designed to be
        used with passed-in Cython array-based functions.

        Parameters
        ----------
        func : string/callable to apply
        name : string, optional
           name of this function
        window : int/array, default to _get_window()
        center : boolean, default to self.center
        check_minp : function, default to _use_window
        how : string, default to None (DEPRECATED)
            how to resample

        Returns
        -------
        y : type of input
        ' 
    if (center is None) : 
        center = self.center 
if (window is None) : 
        window = self._get_window () 
if (check_minp is None) : 
        check_minp = _use_window 
(blocks, obj) = self._create_blocks (how = how) 
    results = [] 
    for b in blocks : 
        try : 
            values = self._prep_values (b.values) 
except TypeError : 
            results.append (b.values.copy ()) 
            continue 
if (values.size == 0) : 
            results.append (values.copy ()) 
            continue 
if isinstance (func, compat.string_types) : 
            if (not hasattr (algos, func)) : 
                raise ValueError ('we do not support this function algos.{0}'.format (func)) 
cfunc = getattr (algos, func) 
            def func(arg, window, min_periods = None) : 
                minp = check_minp (min_periods, window) 
                return cfunc (com._ensure_float64 (arg), window, minp, ** kwargs) 
if center : 
            offset = _offset (window, center) 
            additional_nans = np.array (([np.NaN] * self)) 
            def calc(x) : 
                return func (np.concatenate ((x, additional_nans)), window, min_periods = self.min_periods) 
else : 
            def calc(x) : 
                return func (x, window, min_periods = self.min_periods) 
if (values.ndim > 1) : 
            result =...

    
    

    Fix the buggy line: additional_nans = np.array (([np.NaN] * self))
assistant
#Here is Python program to fix the buggy line: additional_nans = np.

additional_nans = np.array (([np.NaN] * offset))