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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 rolling_max(self, window_start, window_end, min_observations=None):
 """ Calculate a new SArray of the maximum value of different subsets over this SArray. The subset that the maximum is calculated over is defined as an inclusive range relative to the position to each value in the SArray, using `window_start` and `window_end`. For a better understanding of this, see the examples below. Parameters window_start : int The start of the subset to calculate the maximum relative to the current value. window_end : int The end of the subset to calculate the maximum relative to the current value. Must be greater than `window_start`. min_observations : int Minimum number of non-missing observations in window required to calculate the maximum (otherwise result is None). None signifies that the entire window must not include a missing value. A negative number throws an error. Returns ------- out : SArray Examples -------- A rolling max with a window including the previous 2 entries including the current: dtype: int Rows: 5 [None, None, 3, 4, 5] Pandas equivalent: 0 NaN 1 NaN 2 3 3 4 4 5 dtype: float64 Same rolling max operation, but 2 minimum observations: dtype: int Rows: 5 [None, 2, 3, 4, 5] Pandas equivalent: 0 NaN 1 2 2 3 3 4 4 5 dtype: float64 A rolling max with a size of 3, centered around the current: dtype: int Rows: 5 [None, 3, 4, 5, None] Pandas equivalent: 0 NaN 1 3 2 4 3 5 4 NaN dtype: float64 A rolling max with a window including the current and the 2 entries following: dtype: int Rows: 5 [3, 4, 5, None, None] A rolling max with a window including the previous 2 entries NOT including the current: dtype: int Rows: 5 [None, None, 2, 3, 4] """

        min_observations = self.__check_min_observations(min_observations)
        agg_op = '__builtin__max__'
        return SArray(_proxy=self.__proxy__.builtin_rolling_apply(agg_op, window_start, window_end, min_observations))