How to use the pyflink.table.types.DataTypes function in pyflink

os.remove(result_path)
            else:
                shutil.rmtree(result_path)
        except OSError as e:
            logging.error("Error removing directory: %s - %s.", e.filename, e.strerror)

    logging.info("Results directory: %s", result_path)

    t_env.connect(FileSystem().path(result_path)) \
        .with_format(OldCsv()
                     .field_delimiter(',')
                     .field("word", DataTypes.STRING())
                     .field("count", DataTypes.BIGINT())) \
        .with_schema(Schema()
                     .field("word", DataTypes.STRING())
                     .field("count", DataTypes.BIGINT())) \
        .register_table_sink("Results")

    elements = [(word, 1) for word in content.split(" ")]
    t_env.from_elements(elements, ["word", "count"]) \
         .group_by("word") \
         .select("word, count(1) as count") \
         .insert_into("Results")

    t_env.execute("word_count")

ps_num = 1
        python_file = os.getcwd() + "/../../src/test/python/input_output.py"
        property = {}
        func = "map_func"
        env_path = None
        zk_conn = None
        zk_base_path = None
        property[MLCONSTANTS.ENCODING_CLASS] = "com.alibaba.flink.ml.operator.coding.RowCSVCoding"
        property[MLCONSTANTS.DECODING_CLASS] = "com.alibaba.flink.ml.operator.coding.RowCSVCoding"
        inputSb = "INT_32" + "," + "INT_64" + "," + "FLOAT_32" + "," + "FLOAT_64" + "," + "STRING"
        property["SYS:csv_encode_types"] = inputSb
        property["SYS:csv_decode_types"] = inputSb
        source_file = os.getcwd() + "/../../src/test/resources/input.csv"
        table_source = CsvTableSource(source_file,
                                      ["a", "b", "c", "d", "e"],
                                      [DataTypes.INT(),
                                       DataTypes.INT(),
                                       DataTypes.FLOAT(),
                                       DataTypes.DOUBLE(),
                                       DataTypes.STRING()])
        table_env.register_table_source("source", table_source)
        input_tb = table_env.scan("source")
        output_schema = TableSchema(["a", "b", "c", "d", "e"],
                                    [DataTypes.INT(),
                                     DataTypes.INT(),
                                     DataTypes.FLOAT(),
                                     DataTypes.DOUBLE(),
                                     DataTypes.STRING()]
                                    )
        train(work_num, ps_num, python_file, func, property, env_path, zk_conn, zk_base_path, stream_env, table_env,
              input_tb, output_schema)
        # inference(work_num, ps_num, python_file, func, property, env_path, zk_conn, zk_base_path, stream_env, table_env,

property = {}
        func = "map_func"
        env_path = None
        zk_conn = None
        zk_base_path = None
        property[MLCONSTANTS.ENCODING_CLASS] = "com.alibaba.flink.ml.operator.coding.RowCSVCoding"
        property[MLCONSTANTS.DECODING_CLASS] = "com.alibaba.flink.ml.operator.coding.RowCSVCoding"
        inputSb = "INT_32" + "," + "INT_64" + "," + "FLOAT_32" + "," + "FLOAT_64" + "," + "STRING"
        property["SYS:csv_encode_types"] = inputSb
        property["SYS:csv_decode_types"] = inputSb
        source_file = os.getcwd() + "/../../src/test/resources/input.csv"
        table_source = CsvTableSource(source_file,
                                      ["a", "b", "c", "d", "e"],
                                      [DataTypes.INT(),
                                       DataTypes.INT(),
                                       DataTypes.FLOAT(),
                                       DataTypes.DOUBLE(),
                                       DataTypes.STRING()])
        table_env.register_table_source("source", table_source)
        input_tb = table_env.scan("source")
        output_schema = TableSchema(["a", "b", "c", "d", "e"],
                                    [DataTypes.INT(),
                                     DataTypes.INT(),
                                     DataTypes.FLOAT(),
                                     DataTypes.DOUBLE(),
                                     DataTypes.STRING()]
                                    )
        train(work_num, ps_num, python_file, func, property, env_path, zk_conn, zk_base_path, stream_env, table_env,
              input_tb, output_schema)
        # inference(work_num, ps_num, python_file, func, property, env_path, zk_conn, zk_base_path, stream_env, table_env,