**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_sig = signature(DAG.__init__)
        dag_bound_args = dag_sig.bind_partial(*dag_args, **dag_kwargs)
        @functools.wraps(f)
        def factory(*args, **kwargs):
            
            
            
            f_sig = signature(f).bind(*args, **kwargs)
            
            f_sig.apply_defaults()
            
            dag_id = dag_bound_args.arguments.get('dag_id', f.__name__)
            dag_bound_args.arguments['dag_id'] = dag_id
            
            with DAG(*dag_bound_args.args, **dag_bound_args.kwargs) as dag_obj:
                
                if f.__doc__:
                    dag_obj.doc_md = f.__doc__
                
                
                f_kwargs = {}
                for name, value in f_sig.arguments.items():
                    f_kwargs[name] = dag_obj.param(name, value)