How to use the retentioneering.core.utils.BaseTrajectory function in retentioneering

Parameters
        -------
        metric_type: str, optional
            Type of metrics, e.g. node centrality.

        Returns
        -------
        Nothing
        """
        raise NotImplementedError('Sorry! This function is not ready now')
        func = getattr(node_metrics, metric_type)
        return func


class BaseDataset(BaseTrajectory):

    def __init__(self, pandas_obj):
        super(BaseDataset, self).__init__(pandas_obj)
        self._embedding_types = ['tfidf', 'counts', 'frequency']
        self._locals = None

    def extract_features(self, feature_type='tfidf', drop_targets=True, metadata=None, **kwargs):
        """
        User trajectories vectorizer.

        Parameters
        --------
        feature_type: str, optional
            Type of vectorizer. Available vectorization methods:
            - TFIDF (``feature_type='tfidf'``). For more information refer to ``retentioneering.core.feature_extraction.tfidf_embedder``.
            - Event frequencies (``feature_type='frequency'``). For more information refer to ``retentioneering.core.feature_extraction.frequency_embedder``.

'target_event_list': [
                config.get('negative_target_event'),
                config.get('positive_target_event'),
            ]
        })
    if 'columns_map' not in config:
        config['columns_map'] = {
            'user_pseudo_id': config.get('index_col'),
            'event_name': config.get('event_col'),
            'event_timestamp':  config.get('event_time_col'),
        }
    if not os.path.exists(config['experiments_folder']):
        os.mkdir(config['experiments_folder'])

    @pd.api.extensions.register_dataframe_accessor("trajectory")
    class RetentioneeringTrajectory(BaseTrajectory):

        def __init__(self, pandas_obj):
            super(RetentioneeringTrajectory, self).__init__(pandas_obj)
            self.retention_config = config

    @pd.api.extensions.register_dataframe_accessor("retention")
    class RetentioneeringDataset(BaseDataset):

        def __init__(self, pandas_obj):
            super(RetentioneeringDataset, self).__init__(pandas_obj)
            self.retention_config = config