code: 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,
      ):
          
  
          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)
docs: 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.