**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
else:
            suc_count = fail_count = 0
            for k, v in variable_dict.items():
                try:
                    models.Variable.set(k, v, serialize_json=not isinstance(v, str))
                except Exception as e:  
                    logging.info('Variable import failed: %s', repr(e))
                    fail_count += 1
                else:
                    suc_count += 1
            flash(f"{suc_count} variable(s) successfully updated.")
            if fail_count:
                flash(f"{fail_count} variable(s) failed to be updated.", 'error')
            self.update_redirect()
            return redirect(self.get_redirect())
class JobModelView(AirflowModelView):
    """View to show records from Job table"""
    route_base = '/job'
    datamodel = AirflowModelView.CustomSQLAInterface(BaseJob)  
    class_permission_name = permissions.RESOURCE_JOB
    method_permission_name = {
        'list': 'read',
    }
    base_permissions = [
        permissions.ACTION_CAN_READ,
        permissions.ACTION_CAN_ACCESS_MENU,
    ]
    list_columns = [
        'id',
        'dag_id',