code: def _laplace_fit(self,obj_type):
          
  
          # Get Mode and Inverse Hessian information
          y = self.fit(method='PML',printer=False)
  
          if y.ihessian is None:
              raise Exception("No Hessian information - Laplace approximation cannot be performed")
          else:
  
              self.latent_variables.estimation_method = 'Laplace'
  
              theta, Y, scores, states, states_var, X_names = self._categorize_model_output(self.latent_variables.get_z_values())
  
              # Change this in future
              try:
                  latent_variables_store = self.latent_variables.copy()
              except:
                  latent_variables_store = self.latent_variables
  
              return LaplaceResults(data_name=self.data_name,X_names=X_names,model_name=self.model_name,
                  model_type=self.model_type, latent_variables=latent_variables_store,data=Y,index=self.index,
                  multivariate_model=self.multivariate_model,objective_object=obj_type, 
                  method='Laplace',ihessian=y.ihessian,signal=theta,scores=scores,
                  z_hide=self._z_hide,max_lag=self.max_lag,states=states,states_var=states_var)
docs: Performs a Laplace approximation to the posterior
  
          Parameters
          ----------
          obj_type : method
              Whether a likelihood or a posterior
  
          Returns
          ----------
          None (plots posterior)