**Description**

For a DAG that looks like this:

```
A >> B
```

If A fails, B goes into "upstream_failed" state. If a user then marks A "success", B will remain in "upstream_failed" state. It will not automatically start running. This scenario often happens if the failure of A is dealt with outside of Airflow and the user does not want Airflow to run A again. But he usually expect Airflow to run B after A is marked "success".

**Use case / motivation**

After A is marked "success", its downstream tasks B should be cleared automatically and get ready to be scheduled. To avoid changing this behaviour completely, there can be a toggle button next to "Success" that lets the user decide if he wants Airflow to automatically clear downstream tasks that are in "upstream_failed" state.
airflow/models/dag.py
return settings.ALLOW_FUTURE_EXEC_DATES and self.schedule_interval is None
    @provide_session
    def get_concurrency_reached(self, session=None) -> bool:
        """
        Returns a boolean indicating whether the concurrency limit for this DAG
        has been reached
        """
        TI = TaskInstance
        qry = session.query(func.count(TI.task_id)).filter(
            TI.dag_id == self.dag_id,
            TI.state == State.RUNNING,
        )
        return qry.scalar() >= self.concurrency
    @property
    def concurrency_reached(self):
        """This attribute is deprecated. Please use `airflow.models.DAG.get_concurrency_reached` method."""
        warnings.warn(
            "This attribute is deprecated. Please use `airflow.models.DAG.get_concurrency_reached` method.",
            DeprecationWarning,
            stacklevel=2,
        )
        return self.get_concurrency_reached()
    @provide_session
    def get_is_paused(self, session=None) -> Optional[None]: