code: def summed_probabilities(self, choosers, alternatives):
          
          def normalize(s):
              return s / s.sum()
  
          choosers, alternatives = self.apply_predict_filters(
              choosers, alternatives)
          probs = self.probabilities(choosers, alternatives, filter_tables=False)
  
          # groupby the the alternatives ID and sum
          if self.probability_mode == 'single_chooser':
              return (
                  normalize(probs) * len(choosers)
                  ).reset_index(level=0, drop=True)
          elif self.probability_mode == 'full_product':
              return probs.groupby(level=0).apply(normalize)\
                  .groupby(level=1).sum()
          else:
              raise ValueError(
                  'Unrecognized probability_mode option: {}'.format(
                      self.probability_mode))
docs: Calculate total probability associated with each alternative.
  
          Parameters
          ----------
          choosers : pandas.DataFrame
              Table describing the agents making choices, e.g. households.
          alternatives : pandas.DataFrame
              Table describing the things from which agents are choosing.
  
          Returns
          -------
          probs : pandas.Series
              Total probability associated with each alternative.