**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
dag = session.query(DagModel).filter(DagModel.dag_id == self.dag_id).first()
        dp = None
        if dag and dag.pickle_id:
            dp = session.query(DagPickle).filter(DagPickle.id == dag.pickle_id).first()
        if not dp or dp.pickle != self:
            dp = DagPickle(dag=self)
            session.add(dp)
            self.last_pickled = timezone.utcnow()
            session.commit()
            self.pickle_id = dp.id
        return dp
    def tree_view(self) -> None:
        """Print an ASCII tree representation of the DAG."""
        def get_downstream(task, level=0):
            print((" " * level * 4) + str(task))
            level += 1
            for t in task.downstream_list:
                get_downstream(t, level)
        for t in self.roots:
            get_downstream(t)
    @property
    def task(self):