**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
"""Exposes a CLI specific to this DAG"""
        from airflow.cli import cli_parser
        parser = cli_parser.get_parser(dag_parser=True)
        args = parser.parse_args()
        args.func(args, self)
    @provide_session
    def create_dagrun(
        self,
        state: State,
        execution_date: Optional[datetime] = None,
        run_id: Optional[str] = None,
        start_date: Optional[datetime] = None,
        external_trigger: Optional[bool] = False,
        conf: Optional[dict] = None,
        run_type: Optional[DagRunType] = None,
        session=None,
        dag_hash: Optional[str] = None,