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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 filter_for_rate(self, population: Union[pd.DataFrame, pd.Series, Index], rate: Array, additional_key: Any=None) -> Index: """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. """

        return self.filter_for_probability(population, rate_to_probability(rate), additional_key)