**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
"""Shows task instances."""
        dag_id = request.args.get('dag_id')
        dag = current_app.dag_bag.get_dag(dag_id)
        dttm = request.args.get('execution_date')
        if dttm:
            dttm = timezone.parse(dttm)
        else:
            return "Error: Invalid execution_date"
        task_instances = {ti.task_id: alchemy_to_dict(ti) for ti in dag.get_task_instances(dttm, dttm)}
        return json.dumps(task_instances, cls=utils_json.AirflowJsonEncoder)
    @expose('/object/tree_data')
    @auth.has_access(
        [
            (permissions.ACTION_CAN_READ, permissions.RESOURCE_DAG),
            (permissions.ACTION_CAN_READ, permissions.RESOURCE_TASK_INSTANCE),
        ]
    )
    @action_logging
    def tree_data(self):