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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _apply(self, func, name=None, window=None, center=None, check_minp=None, **kwargs):
 """ Rolling statistical measure using supplied function. Designed to be used with passed-in Cython array-based functions. Parameters func : str/callable to apply name : str, optional name of this function window : int/array, default to _get_window() center : bool, default to self.center check_minp : function, default to _use_window 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, index = self._create_blocks()
        index, indexi = self._get_index(index=index)
        results = []
        for b in blocks:
            values = self._prep_values(b.values)

            if values.size == 0:
                results.append(values.copy())
                continue

            # if we have a string function name, wrap it
            if isinstance(func, str):
                cfunc = getattr(libwindow, func, None)
                if cfunc is None:
                    raise ValueError("we do not support this function "
                                     "in libwindow.{func}".format(func=func))

                def func(arg, window, min_periods=None, closed=None):
                    minp = check_minp(min_periods, window)
                    # ensure we are only rolling on floats
                    arg = ensure_float64(arg)
                    return cfunc(arg,
                                 window, minp, indexi, closed, **kwargs)

            # calculation function
            if center:
                offset = _offset(window, center)
                additional_nans = np.array([np.NaN] * offset)

                def calc(x):
                    return func(np.concatenate((x, additional_nans)),
                                window, min_periods=self.min_periods,
                                closed=self.closed)
            else:

                def calc(x):
                    return func(x, window, min_periods=self.min_periods,
                                closed=self.closed)

            with np.errstate(all='ignore'):
                if values.ndim > 1:
                    result = np.apply_along_axis(calc, self.axis, values)
                else:
                    result = calc(values)

            if center:
                result = self._center_window(result, window)

            results.append(result)

        return self._wrap_results(results, blocks, obj)