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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 fit(self, choosers, alternatives, current_choice):
 """ Fit and save models based on given data after segmenting the `choosers` table. Segments that have not already been explicitly added will be automatically added with default model. Parameters choosers : pandas.DataFrame Table describing the agents making choices, e.g. households. Must have a column with the same name as the .segmentation_col attribute. alternatives : pandas.DataFrame Table describing the things from which agents are choosing, e.g. buildings. current_choice Name of column in `choosers` that indicates which alternative they have currently chosen. Returns ------- log_likelihoods : dict of dict Keys will be model names and values will be dictionaries of log-liklihood values as returned by MNLDiscreteChoiceModel.fit. """

        logger.debug('start: fit models in segmented LCM {}'.format(self.name))

        choosers, alternatives = self.apply_fit_filters(choosers, alternatives)
        unique = choosers[self.segmentation_col].unique()

        # Remove any existing segments that may no longer have counterparts
        # in the data. This can happen when loading a saved model and then
        # calling this method with data that no longer has segments that
        # were there the last time this was called.
        gone = set(self._group.models) - set(unique)
        for g in gone:
            del self._group.models[g]

        for x in unique:
            if x not in self._group.models:
                self.add_segment(x)

        results = self._group.fit(choosers, alternatives, current_choice)
        logger.debug(
            'finish: fit models in segmented LCM {}'.format(self.name))
        return results