code: def add_mixl_specific_results_to_estimation_res(estimator, results_dict):
      
      # Get the probability of each sequence of choices, given the draws
      prob_res = mlc.calc_choice_sequence_probs(results_dict["long_probs"],
                                                estimator.choice_vector,
                                                estimator.rows_to_mixers,
                                                return_type='all')
      # Add the various items to the results_dict.
      results_dict["simulated_sequence_probs"] = prob_res[0]
      results_dict["expanded_sequence_probs"] = prob_res[1]
  
      return results_dict
docs: Stores particular items in the results dictionary that are unique to mixed
      logit-type models. In particular, this function calculates and adds
      `sequence_probs` and `expanded_sequence_probs` to the results dictionary.
      The `constrained_pos` object is also stored to the results_dict.
  
      Parameters
      ----------
      estimator : an instance of the MixedEstimator class.
          Should contain a `choice_vector` attribute that is a 1D ndarray
          representing the choices made for this model's dataset. Should also
          contain a `rows_to_mixers` attribute that maps each row of the long
          format data to a unit of observation that the mixing is being performed
          over.
      results_dict : dict.
          This dictionary should be the dictionary returned from
          scipy.optimize.minimize. In particular, it should have the following
          `long_probs` key.
  
      Returns
      -------
      results_dict.