**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/www/views.py
sqla.func.max(DagRun.start_date).label('start_date'),
        ).group_by(DagRun.dag_id)
        
        query = query.filter(DagRun.dag_id.in_(filter_dag_ids))  
        resp = {
            r.dag_id.replace('.', '__dot__'): {
                'dag_id': r.dag_id,
                'execution_date': r.execution_date.isoformat(),
                'start_date': r.start_date.isoformat(),
            }
            for r in query
        }
        return wwwutils.json_response(resp)
    @expose('/code')
    @auth.has_access(
        [
            (permissions.ACTION_CAN_READ, permissions.RESOURCE_DAG),
            (permissions.ACTION_CAN_READ, permissions.RESOURCE_DAG_CODE),
        ]
    )
    @provide_session
    def code(self, session=None):
        """Dag Code."""
        all_errors = ""
        dag_orm = None
        dag_id = None
        try:
            dag_id = request.args.get('dag_id')
            dag_orm = DagModel.get_dagmodel(dag_id, session=session)
            code = DagCode.get_code_by_fileloc(dag_orm.fileloc)