code: def _create_fit_summary(self):
          
          # Make sure we have all attributes needed to create the results summary
          needed_attributes = ["df_model",
                               "nobs",
                               "null_log_likelihood",
                               "log_likelihood",
                               "rho_squared",
                               "rho_bar_squared",
                               "estimation_message"]
          try:
              assert all([hasattr(self, attr) for attr in needed_attributes])
              assert all([getattr(self, attr) is not None
                          for attr in needed_attributes])
          except AssertionError:
              msg = "Call this function only after setting/calculating all other"
              msg_2 = " estimation results attributes"
              raise NotImplementedError(msg + msg_2)
  
          self.fit_summary = pd.Series([self.df_model,
                                        self.nobs,
                                        self.null_log_likelihood,
                                        self.log_likelihood,
                                        self.rho_squared,
                                        self.rho_bar_squared,
                                        self.estimation_message],
                                       index=["Number of Parameters",
                                              "Number of Observations",
                                              "Null Log-Likelihood",
                                              "Fitted Log-Likelihood",
                                              "Rho-Squared",
                                              "Rho-Bar-Squared",
                                              "Estimation Message"])
  
          return None
docs: Create and store a pandas series that will display to users the
          various statistics/values that indicate how well the estimated model
          fit the given dataset.
  
          Returns
          -------
          None.