code: def filter_for_rate(self, population: Union[pd.DataFrame, pd.Series, Index],
                          rate: Array, additional_key: Any=None) -> Index:
          
          return self.filter_for_probability(population, rate_to_probability(rate), additional_key)
docs: Decide an event outcome for each individual in a population from rates.
  
          Given a population or its index and an array of associated rates for
          some event to happen, we create and return the sub-population for whom
          the event occurred.
  
          Parameters
          ----------
          population :
              A view on the simulants for which we are determining the
              outcome of an event.
          rate :
              A 1d list of rates of the event under consideration occurring which
              corresponds (i.e. `len(population) == len(probability))` to the
              population view passed in. The rates must be scaled to the simulation
              time-step size either manually or as a post-processing step in a
              rate pipeline.
          additional_key :
              Any additional information used to create the seed.
  
          Returns
          -------
          Index
              The index of the simulants for whom the event occurred.
  
          See Also
          --------
          framework.values:
              Value/rate pipeline management module.