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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 _additive_estimate(events, timeline, _additive_f, _additive_var, reverse):
 """ Called to compute the Kaplan Meier and Nelson-Aalen estimates. """

    if reverse:
        events = events.sort_index(ascending=False)
        at_risk = events["entrance"].sum() - events["removed"].cumsum().shift(1).fillna(0)

        deaths = events["observed"]

        estimate_ = np.cumsum(_additive_f(at_risk, deaths)).sort_index().shift(-1).fillna(0)
        var_ = np.cumsum(_additive_var(at_risk, deaths)).sort_index().shift(-1).fillna(0)
    else:
        deaths = events["observed"]

        # Why subtract entrants like this? see https://github.com/CamDavidsonPilon/lifelines/issues/497
        # specifically, we kill people, compute the ratio, and then "add" the entrants. This means that
        # the population should not have the late entrants. The only exception to this rule
        # is the first period, where entrants happen _prior_ to deaths.
        entrances = events["entrance"].copy()
        entrances.iloc[0] = 0
        population = events["at_risk"] - entrances

        estimate_ = np.cumsum(_additive_f(population, deaths))
        var_ = np.cumsum(_additive_var(population, deaths))

    timeline = sorted(timeline)
    estimate_ = estimate_.reindex(timeline, method="pad").fillna(0)
    var_ = var_.reindex(timeline, method="pad")
    var_.index.name = "timeline"
    estimate_.index.name = "timeline"

    return estimate_, var_