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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_csv(self, url, pre_func=None, post_func=None, date_column='date', date_format=None, timezone=pytz.utc.zone, symbol=None, mask=True, symbol_column=None, special_params_checker=None, country_code=None, **kwargs):
 """Fetch a csv from a remote url and register the data so that it is queryable from the ``data`` object. Parameters url : str The url of the csv file to load. pre_func : callable[pd.DataFrame -> pd.DataFrame], optional A callback to allow preprocessing the raw data returned from fetch_csv before dates are paresed or symbols are mapped. post_func : callable[pd.DataFrame -> pd.DataFrame], optional A callback to allow postprocessing of the data after dates and symbols have been mapped. date_column : str, optional The name of the column in the preprocessed dataframe containing datetime information to map the data. date_format : str, optional The format of the dates in the ``date_column``. If not provided ``fetch_csv`` will attempt to infer the format. For information about the format of this string, see :func:`pandas.read_csv`. timezone : tzinfo or str, optional The timezone for the datetime in the ``date_column``. symbol : str, optional If the data is about a new asset or index then this string will be the name used to identify the values in ``data``. For example, one may use ``fetch_csv`` to load data for VIX, then this field could be the string ``'VIX'``. mask : bool, optional Drop any rows which cannot be symbol mapped. symbol_column : str If the data is attaching some new attribute to each asset then this argument is the name of the column in the preprocessed dataframe containing the symbols. This will be used along with the date information to map the sids in the asset finder. country_code : str, optional Country code to use to disambiguate symbol lookups. **kwargs Forwarded to :func:`pandas.read_csv`. Returns ------- csv_data_source : zipline.sources.requests_csv.PandasRequestsCSV A requests source that will pull data from the url specified. """

        if country_code is None:
            country_code = self.default_fetch_csv_country_code(
                self.trading_calendar,
            )

        # Show all the logs every time fetcher is used.
        csv_data_source = PandasRequestsCSV(
            url,
            pre_func,
            post_func,
            self.asset_finder,
            self.trading_calendar.day,
            self.sim_params.start_session,
            self.sim_params.end_session,
            date_column,
            date_format,
            timezone,
            symbol,
            mask,
            symbol_column,
            data_frequency=self.data_frequency,
            country_code=country_code,
            special_params_checker=special_params_checker,
            **kwargs
        )

        # ingest this into dataportal
        self.data_portal.handle_extra_source(csv_data_source.df,
                                             self.sim_params)

        return csv_data_source