code: def _summary_of_best_pipeline(self, features, target):
          
          if not self._optimized_pipeline:
              raise RuntimeError('There was an error in the TPOT optimization '
                                 'process. This could be because the data was '
                                 'not formatted properly, or because data for '
                                 'a regression problem was provided to the '
                                 'TPOTClassifier object. Please make sure you '
                                 'passed the data to TPOT correctly.')
          else:
              self.fitted_pipeline_ = self._toolbox.compile(expr=self._optimized_pipeline)
  
              with warnings.catch_warnings():
                  warnings.simplefilter('ignore')
                  self.fitted_pipeline_.fit(features, target)
  
              if self.verbosity in [1, 2]:
                  # Add an extra line of spacing if the progress bar was used
                  if self.verbosity >= 2:
                      print('')
  
                  optimized_pipeline_str = self.clean_pipeline_string(self._optimized_pipeline)
                  print('Best pipeline:', optimized_pipeline_str)
  
              # Store and fit the entire Pareto front as fitted models for convenience
              self.pareto_front_fitted_pipelines_ = {}
  
              for pipeline in self._pareto_front.items:
                  self.pareto_front_fitted_pipelines_[str(pipeline)] = self._toolbox.compile(expr=pipeline)
                  with warnings.catch_warnings():
                      warnings.simplefilter('ignore')
                      self.pareto_front_fitted_pipelines_[str(pipeline)].fit(features, target)
docs: Print out best pipeline at the end of optimization process.
  
          Parameters
          ----------
          features: array-like {n_samples, n_features}
              Feature matrix
  
          target: array-like {n_samples}
              List of class labels for prediction
  
          Returns
          -------
          self: object
              Returns a copy of the fitted TPOT object