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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 generate_meta_features(path, base_learner_id):
 """Generates meta-features for specified base learner After generation of meta-features, the file is saved into the meta-features folder Args: path (str):
 Path to Xcessiv notebook base_learner_id (str):
 Base learner ID """

    with functions.DBContextManager(path) as session:
        base_learner = session.query(models.BaseLearner).filter_by(id=base_learner_id).first()
        if not base_learner:
            raise exceptions.UserError('Base learner {} '
                                       'does not exist'.format(base_learner_id))

        base_learner.job_id = get_current_job().id
        base_learner.job_status = 'started'

        session.add(base_learner)
        session.commit()

        try:
            est = base_learner.return_estimator()
            extraction = session.query(models.Extraction).first()
            X, y = extraction.return_train_dataset()
            return_splits_iterable = functions.import_object_from_string_code(
                extraction.meta_feature_generation['source'],
                'return_splits_iterable'
            )

            meta_features_list = []
            trues_list = []
            for train_index, test_index in return_splits_iterable(X, y):
                X_train, X_test = X[train_index], X[test_index]
                y_train, y_test = y[train_index], y[test_index]
                est = est.fit(X_train, y_train)
                meta_features_list.append(
                    getattr(est, base_learner.base_learner_origin.
                            meta_feature_generator)(X_test)
                )
                trues_list.append(y_test)
            meta_features = np.concatenate(meta_features_list, axis=0)
            y_true = np.concatenate(trues_list)

            for key in base_learner.base_learner_origin.metric_generators:
                metric_generator = functions.import_object_from_string_code(
                    base_learner.base_learner_origin.metric_generators[key],
                    'metric_generator'
                )
                base_learner.individual_score[key] = metric_generator(y_true, meta_features)

            meta_features_path = base_learner.meta_features_path(path)

            if not os.path.exists(os.path.dirname(meta_features_path)):
                os.makedirs(os.path.dirname(meta_features_path))

            np.save(meta_features_path, meta_features, allow_pickle=False)
            base_learner.job_status = 'finished'
            base_learner.meta_features_exists = True
            session.add(base_learner)
            session.commit()

        except:
            session.rollback()
            base_learner.job_status = 'errored'
            base_learner.description['error_type'] = repr(sys.exc_info()[0])
            base_learner.description['error_value'] = repr(sys.exc_info()[1])
            base_learner.description['error_traceback'] = \
                traceback.format_exception(*sys.exc_info())
            session.add(base_learner)
            session.commit()
            raise