**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
if not execution_date:
            flash('Invalid execution date', 'error')
            return redirect(origin)
        execution_date = timezone.parse(execution_date)
        dag = current_app.dag_bag.get_dag(dag_id)
        if not dag:
            flash(f'Cannot find DAG: {dag_id}', 'error')
            return redirect(origin)
        new_dag_state = set_dag_run_state_to_success(dag, execution_date, commit=confirmed)
        if confirmed:
            flash(f'Marked success on {len(new_dag_state)} task instances')
            return redirect(origin)
        else:
            details = '\n'.join(str(t) for t in new_dag_state)
            response = self.render_template(
                'airflow/confirm.html',
                message="Here's the list of task instances you are about to mark as success",
                details=details,
            )
            return response
    @expose('/dagrun_failed', methods=['POST'])
    @auth.has_access(
        [
            (permissions.ACTION_CAN_READ, permissions.RESOURCE_DAG),
            (permissions.ACTION_CAN_EDIT, permissions.RESOURCE_DAG_RUN),
        ]
    )
    @action_logging
    def dagrun_failed(self):