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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 _summary_of_best_pipeline(self, features, target):
 """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 """

        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)