How to use the pyflink.datastream.stream_execution_environment.StreamExecutionEnvironment function in pyflink

:param python_file: The python file which is going to be run
    :param func: The user-defined function that runs TF inference. If it's None, inference is run via Java API.
    :param properties: User-defined properties
    :param env_path: Path to the virtual env
    :param zk_conn: The Zookeeper connection string
    :param zk_base_path: The Zookeeper base path
    :param stream_env: The StreamExecutionEnvironment. If it's None, this method will create one and execute the job
                       at the end. Otherwise, caller is responsible to trigger the job execution
    :param input_ds: The input DataStream
    :param output_row_type: The RowType for the output DataStream. If it's None, a dummy sink will be added to the
                      output DataStream. Otherwise, caller is responsible to add sink before executing the job.
    :return: The output DataStream. Currently it's always of type Row.
    """
    tf_config = TFConfig(num_worker, num_ps, python_file, func, properties, env_path, zk_conn, zk_base_path)
    if stream_env is None:
        stream_env = StreamExecutionEnvironment.get_execution_environment()
    if input_ds is not None:
        if isinstance(input_ds, DataStreamSource):
            input_ds = input_ds._j_datastream_source
        else:
            input_ds = input_ds._j_datastream
    output_ds = get_gateway().jvm.com.alibaba.flink.ml.tensorflow.client.TFUtils.inference(stream_env._j_stream_execution_environment,
                                                                                           input_ds,
                                                                                           tf_config.java_config(),
                                                                                           to_java_type_info(output_row_type))
    stream_env.execute()
    return DataStream(output_ds)

def addTrainChiefAloneTable():
        stream_env = StreamExecutionEnvironment.get_execution_environment()
        table_env = StreamTableEnvironment.create(stream_env)
        work_num = 2
        ps_num = 1
        python_file = os.getcwd() + "/../../src/test/python/add.py"
        func = "map_func"
        property = {}
        property[TFCONSTANS.TF_IS_CHIEF_ALONE] = "ture"
        env_path = None
        zk_conn = None
        zk_base_path = None
        input_tb = None
        output_schema = None

        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,

def addTrainTable():
        stream_env = StreamExecutionEnvironment.get_execution_environment()
        table_env = StreamTableEnvironment.create(stream_env)
        work_num = 2
        ps_num = 1
        python_file = os.getcwd() + "/../../src/test/python/add.py"
        func = "map_func"
        property = None
        env_path = None
        zk_conn = None
        zk_base_path = None
        input_tb = None
        output_schema = None

        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,

:param python_file: The python file which is going to be run
    :param func: The user-defined function that runs TF training
    :param properties: User-defined properties
    :param env_path: Path to the virtual env
    :param zk_conn: The Zookeeper connection string
    :param zk_base_path: The Zookeeper base path
    :param stream_env: The StreamExecutionEnvironment. If it's None, this method will create one and execute the job
                       at the end. Otherwise, caller is responsible to trigger the job execution
    :param input_ds: The input DataStream
    :param output_row_type: The RowType for the output DataStream. If it's None, a dummy sink will be added to the
                      output DataStream. Otherwise, caller is responsible to add sink before executing the job.
    :return: The output DataStream. Currently it's always of type Row.
    """
    tf_config = TFConfig(num_worker, num_ps, python_file, func, properties, env_path, zk_conn, zk_base_path)
    if stream_env is None:
        stream_env = StreamExecutionEnvironment.get_execution_environment()
    if input_ds is not None:
        if isinstance(input_ds, DataStreamSource):
            input_ds = input_ds._j_datastream_source
        else:
            input_ds = input_ds._j_datastream
    output_ds = get_gateway().jvm.com.alibaba.flink.ml.tensorflow.client.TFUtils.train(stream_env._j_stream_execution_environment,
                                                                                       input_ds,
                                                                                       tf_config.java_config(),
                                                                                       to_java_type_info(output_row_type))

    stream_env.execute()
    return DataStream(output_ds)