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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 fetch(self, query, decode_geom=False):
 """Pull the result from an arbitrary SELECT SQL query from a CARTO account into a pandas DataFrame. Args: query (str):
 SELECT query to run against CARTO user database. This data will then be converted into a pandas DataFrame. decode_geom (bool, optional):
 Decodes CARTO's geometries into a `Shapely <https://github.com/Toblerity/Shapely>`__ object that can be used, for example, in `GeoPandas <http://geopandas.org/>`__. Returns: pandas.DataFrame: DataFrame representation of query supplied. Pandas data types are inferred from PostgreSQL data types. In the case of PostgreSQL date types, dates are attempted to be converted, but on failure a data type 'object' is used. Examples: This query gets the 10 highest values from a table and returns a dataframe. .. code:: python topten_df = cc.query( ''' SELECT * FROM my_table ORDER BY value_column DESC LIMIT 10 ''' ) This query joins points to polygons based on intersection, and aggregates by summing the values of the points in each polygon. The query returns a dataframe, with a geometry column that contains polygons. .. code:: python points_aggregated_to_polygons = cc.query( ''' SELECT polygons.*, sum(points.values) FROM polygons JOIN points ON ST_Intersects(points.the_geom, polygons.the_geom) GROUP BY polygons.the_geom, polygons.cartodb_id ''', decode_geom=True ) """

        copy_query = 'COPY ({query}) TO stdout WITH (FORMAT csv, HEADER true)'.format(query=query)
        query_columns = get_columns(self, query)

        result = recursive_read(self, copy_query)
        df_types = dtypes(query_columns, exclude_dates=True)

        df = pd.read_csv(result, dtype=df_types,
                         parse_dates=date_columns_names(query_columns),
                         true_values=['t'],
                         false_values=['f'],
                         index_col='cartodb_id' if 'cartodb_id' in df_types.keys() else False,
                         converters={'the_geom': lambda x: _decode_geom(x) if decode_geom else x})

        if decode_geom:
            df.rename({'the_geom': 'geometry'}, axis='columns', inplace=True)

        return df