**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
self,
        state: str = State.RUNNING,
        session: Session = None,
        start_date: Optional[datetime] = None,
        end_date: Optional[datetime] = None,
        dag_ids: List[str] = None,
    ) -> None:
        warnings.warn(
            "This method is deprecated and will be removed in a future version.",
            DeprecationWarning,
            stacklevel=2,
        )
        dag_ids = dag_ids or [self.dag_id]
        query = session.query(DagRun).filter(DagRun.dag_id.in_(dag_ids))
        if start_date:
            query = query.filter(DagRun.execution_date >= start_date)
        if end_date:
            query = query.filter(DagRun.execution_date <= end_date)
        query.update({DagRun.state: state}, synchronize_session='fetch')
    @provide_session
    def clear(