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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 summed_probabilities(self, choosers, alternatives):
 """ Returns the sum of probabilities for alternatives across all chooser segments. Parameters choosers : pandas.DataFrame Table describing the agents making choices, e.g. households. Must have a column matching the .segmentation_col attribute. alternatives : pandas.DataFrame Table describing the things from which agents are choosing. Returns ------- probs : pandas.Series Summed probabilities from each segment added together. """

        if len(alternatives) == 0 or len(choosers) == 0:
            return pd.Series()

        logger.debug(
            'start: calculate summed probabilities in LCM group {}'.format(
                self.name))
        probs = []

        for name, df in self._iter_groups(choosers):
            probs.append(
                self.models[name].summed_probabilities(df, alternatives))

        add = tz.curry(pd.Series.add, fill_value=0)
        probs = tz.reduce(add, probs)

        logger.debug(
            'finish: calculate summed probabilities in LCM group {}'.format(
                self.name))
        return probs