How to use the geoalchemy2.functions.ST_AsGeoJSON function in GeoAlchemy2

def get_latest_predictions_in_bbox(model_id: int, version_id: int, bbox: list):
        """
        Fetch latest predictions for the specified model intersecting
        the given bbox

        :param model_id, version_id, bbox
        :return list of predictions
        """

        query = db.session.query(Prediction.id, Prediction.created, Prediction.dockerhub_hash, ST_AsGeoJSON(ST_Envelope(Prediction.bbox)).label('bbox'),
                                 Prediction.model_id, Prediction.tile_zoom, Prediction.version_id).filter(Prediction.model_id == model_id).filter(
                                     Prediction.version_id == version_id).filter(ST_Intersects(
                                         Prediction.bbox, ST_MakeEnvelope(bbox[0], bbox[1], bbox[2], bbox[3], 4326))).order_by(
                                             Prediction.created.desc()).limit(1)

        return query.all()

if tablename in seen_classes:
        cls = current_app.class_references[tablename]
    else:
        db.metadata.reflect(bind=db.engine)
        seen_classes.add(tablename)
        cls = type(str(tablename), (GeoPoly, db.Model,), {'__tablename__':tablename,
           '__table_args__' : {'extend_existing': True}})
        current_app.class_references[tablename] = cls

    if field == config.geom_column:
        vector = cls.query.with_entities(geofuncs.ST_AsGeoJSON(getattr(cls, field))).all()
        response['data'] = [v[0] for v in vector]
    elif field == 'geojson':
        #TODO: How can this be cleaner?  Do I need 2 queries go get geojson?
        #rows = cls.query.all()
        geoms = cls.query.with_entities(geofuncs.ST_AsGeoJSON(getattr(cls, config.geom_column))).all()
	features = []
        for i, row in enumerate(geoms):
            #attributes = row.as_dict()
            #attributes.pop('wkb_geometry', None)
            #for k, v in attributes.iteritems():
     	        #if isinstance(v, decimal.Decimal):
   	            #attributes[k] = float(v)
            current_feature = {'type':'Feature',
                    'geometry':ast.literal_eval(geoms[i][0])}
                    #'properties':attributes}
            features.append(current_feature)
        geojson = {"type": "FeatureCollection","features": features}
	#geojson = {"type":"FeatureCollection", "features": geoms}
        response['data']['geojson'] = geojson
    elif field == 'topojson':
        #TODO: Add topojson support if the DB is postgresql

def get(prediction_id: int):
        """
        Get prediction with the given ID
        :param prediction_id
        :return prediction if found otherwise None
        """
        query = db.session.query(Prediction.id, Prediction.created, Prediction.dockerhub_hash,
                                 ST_AsGeoJSON(ST_Envelope(Prediction.bbox)).label('bbox'), Prediction.model_id, Prediction.tile_zoom,
                                 Prediction.version_id).filter(Prediction.id == prediction_id)
        return query.one()

def get_predictions_by_model(model_id: int):
        """
        Gets predictions for a specified ML Model
        :param model_id: ml model ID in scope
        :return predictions if found otherwise None
        """
        query = db.session.query(Prediction.id, Prediction.created, Prediction.dockerhub_hash,
                                 ST_AsGeoJSON(ST_Envelope(Prediction.bbox)).label('bbox'), Prediction.model_id, Prediction.tile_zoom,
                                 Prediction.version_id).filter(Prediction.model_id == model_id)
        return query.all()

def get_all_predictions_in_bbox(model_id: int, bbox: list):
        """
        Fetch all predictions for the specified model intersecting the given
        bbox
        :param model_id, bbox
        :return list of predictions
        """
        query = db.session.query(Prediction.id, Prediction.created, Prediction.dockerhub_hash, ST_AsGeoJSON(ST_Envelope(Prediction.bbox)).label('bbox'),
                                 Prediction.model_id, Prediction.tile_zoom, Prediction.version_id).filter(
                                     Prediction.model_id == model_id).filter(
                                         ST_Intersects(Prediction.bbox, ST_MakeEnvelope(bbox[0], bbox[1], bbox[2], bbox[3], 4326)))

        return query.all()