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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 parameterize( self, country: Optional[str] = "South Sudan", state: Optional[str] = None, year: Optional[int] = None, month: Optional[int] = None, unit: Optional[str] = None, fallback_aggaxes: List[str] = ["year", "month"], aggfunc: Callable = np.mean, ):
 """ Parameterize the analysis graph. Args: country year month fallback_aggaxes: An iterable of strings denoting the axes upon which to perform fallback aggregation if the desired constraints cannot be met. aggfunc: The function that will be called to perform the aggregation if there are multiple matches. """


        valid_axes = ("country", "state", "year", "month")

        if any(map(lambda axis: axis not in valid_axes, fallback_aggaxes)):
            raise ValueError(
                "All elements of the fallback_aggaxes set must be one of the "
                f"following: {valid_axes}"
            )

        for n in self.nodes(data=True):
            for indicator in n[1]["indicators"].values():
                indicator.mean, indicator.unit = get_indicator_value(
                    indicator,
                    country,
                    state,
                    year,
                    month,
                    unit,
                    fallback_aggaxes,
                    aggfunc,
                )
                indicator.stdev = 0.1 * abs(indicator.mean)