**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
f(**f_kwargs)
            
            return dag_obj
        return factory
    return wrapper
STATICA_HACK = True
globals()['kcah_acitats'[::-1].upper()] = False
if STATICA_HACK:  
    from airflow.models.serialized_dag import SerializedDagModel
    DagModel.serialized_dag = relationship(SerializedDagModel)
class DagContext:
    """
    DAG context is used to keep the current DAG when DAG is used as ContextManager.
    You can use DAG as context:
    .. code-block:: python
        with DAG(
            dag_id="example_dag",
            default_args=default_args,
            schedule_interval="0 0 * * *",
            dagrun_timeout=timedelta(minutes=60),
        ) as dag:
            ...
    If you do this the context stores the DAG and whenever new task is created, it will use
    such stored DAG as the parent DAG.
    """
    _context_managed_dag: Optional[DAG] = None
    _previous_context_managed_dags: List[DAG] = []
    @classmethod
    def push_context_managed_dag(cls, dag: DAG):