How to use the awswrangler.athena.Athena.normalize_column_name function in awswrangler

:param preserve_index: Should preserve index on S3?
        :param mode: "append", "overwrite", "overwrite_partitions"
        :param compression: None, gzip, snappy, etc
        :param procs_cpu_bound: Number of cores used for CPU bound tasks
        :param procs_io_bound: Number of cores used for I/O bound tasks
        :param cast_columns: Dictionary of columns names and Athena/Glue types to be casted. (E.g. {"col name": "bigint", "col2 name": "int"}) (Only for "parquet" file_format)
        :param extra_args: Extra arguments specific for each file formats (E.g. "sep" for CSV)
        :param inplace: True is cheapest (CPU and Memory) but False leaves your DataFrame intact
        :return: List of objects written on S3
        """
        if partition_cols is None:
            partition_cols = []
        if cast_columns is None:
            cast_columns = {}
        dataframe = Pandas.normalize_columns_names_athena(dataframe, inplace=inplace)
        cast_columns = {Athena.normalize_column_name(k): v for k, v in cast_columns.items()}
        logger.debug(f"cast_columns: {cast_columns}")
        partition_cols = [Athena.normalize_column_name(x) for x in partition_cols]
        logger.debug(f"partition_cols: {partition_cols}")
        dataframe = Pandas.drop_duplicated_columns(dataframe=dataframe, inplace=inplace)
        if compression is not None:
            compression = compression.lower()
        file_format = file_format.lower()
        if file_format == "csv":
            if compression not in Pandas.VALID_CSV_COMPRESSIONS:
                raise InvalidCompression(
                    f"{compression} isn't a valid CSV compression. Try: {Pandas.VALID_CSV_COMPRESSIONS}")
        elif file_format == "parquet":
            if compression not in Pandas.VALID_PARQUET_COMPRESSIONS:
                raise InvalidCompression(
                    f"{compression} isn't a valid PARQUET compression. Try: {Pandas.VALID_PARQUET_COMPRESSIONS}")
        else:

:param compression: None, gzip, snappy, etc
        :param procs_cpu_bound: Number of cores used for CPU bound tasks
        :param procs_io_bound: Number of cores used for I/O bound tasks
        :param cast_columns: Dictionary of columns names and Athena/Glue types to be casted. (E.g. {"col name": "bigint", "col2 name": "int"}) (Only for "parquet" file_format)
        :param extra_args: Extra arguments specific for each file formats (E.g. "sep" for CSV)
        :param inplace: True is cheapest (CPU and Memory) but False leaves your DataFrame intact
        :return: List of objects written on S3
        """
        if partition_cols is None:
            partition_cols = []
        if cast_columns is None:
            cast_columns = {}
        dataframe = Pandas.normalize_columns_names_athena(dataframe, inplace=inplace)
        cast_columns = {Athena.normalize_column_name(k): v for k, v in cast_columns.items()}
        logger.debug(f"cast_columns: {cast_columns}")
        partition_cols = [Athena.normalize_column_name(x) for x in partition_cols]
        logger.debug(f"partition_cols: {partition_cols}")
        dataframe = Pandas.drop_duplicated_columns(dataframe=dataframe, inplace=inplace)
        if compression is not None:
            compression = compression.lower()
        file_format = file_format.lower()
        if file_format == "csv":
            if compression not in Pandas.VALID_CSV_COMPRESSIONS:
                raise InvalidCompression(
                    f"{compression} isn't a valid CSV compression. Try: {Pandas.VALID_CSV_COMPRESSIONS}")
        elif file_format == "parquet":
            if compression not in Pandas.VALID_PARQUET_COMPRESSIONS:
                raise InvalidCompression(
                    f"{compression} isn't a valid PARQUET compression. Try: {Pandas.VALID_PARQUET_COMPRESSIONS}")
        else:
            raise UnsupportedFileFormat(file_format)
        if dataframe.empty:

def normalize_columns_names_athena(dataframe, inplace=True):
        if inplace is False:
            dataframe = dataframe.copy(deep=True)
        dataframe.columns = [Athena.normalize_column_name(x) for x in dataframe.columns]
        return dataframe