
**Apache Airflow version**:
1.10.13

**What happened**:
`force_logout_after` should be `force_log_out_after` in the code section https://github.com/apache/airflow/blob/master/airflow/settings.py#L372-L381. As `force_log_out_after` is actually used and written in https://github.com/apache/airflow/blob/c5700a56bb3b9a5b872bda0fe0d3de82b0128bdf/UPDATING.md#unify-user-session-lifetime-configuration.

**What you expected to happen**:
`force_logout_after` is renamed to `force_log_out_after`.
airflow/settings.py
import rich.console
    return rich.console.Console(file=file)
def custom_show_warning(message, category, filename, lineno, file=None, line=None):
    """Custom function to print rich and visible warnings"""
    
    from rich.markup import escape
    msg = f"[bold]{line}" if line else f"[bold][yellow]{filename}:{lineno}"
    msg += f" {category.__name__}[/bold]: {escape(str(message))}[/yellow]"
    write_console = _get_rich_console(file or sys.stderr)
    write_console.print(msg, soft_wrap=True)
warnings.showwarning = custom_show_warning
def task_policy(task) -> None:  
    """
    This policy setting allows altering tasks after they are loaded in
    the DagBag. It allows administrator to rewire some task's parameters.
    Alternatively you can raise ``AirflowClusterPolicyViolation`` exception
    to stop DAG from being executed.
    To define policy, add a ``airflow_local_settings`` module
    to your PYTHONPATH that defines this ``task_policy`` function.
    Here are a few examples of how this can be useful:
    * You could enforce a specific queue (say the ``spark`` queue)
        for tasks using the ``SparkOperator`` to make sure that these
        tasks get wired to the right workers
    * You could enforce a task timeout policy, making sure that no tasks run
        for more than 48 hours
    :param task: task to be mutated
    :type task: airflow.models.baseoperator.BaseOperator
    """
def dag_policy(dag) -> None: