**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
"""
        Returns the number of task instances in the given DAG.
        :param session: ORM session
        :param dag_id: ID of the DAG to get the task concurrency of
        :type dag_id: unicode
        :param task_ids: A list of valid task IDs for the given DAG
        :type task_ids: list[unicode]
        :param states: A list of states to filter by if supplied
        :type states: list[state]
        :return: The number of running tasks
        :rtype: int
        """
        qry = session.query(func.count(TaskInstance.task_id)).filter(
            TaskInstance.dag_id == dag_id,
        )
        if task_ids:
            qry = qry.filter(
                TaskInstance.task_id.in_(task_ids),
            )
        if states:
            if None in states:
                if all(x is None for x in states):
                    qry = qry.filter(TaskInstance.state.is_(None))
                else:
                    not_none_states = [state for state in states if state]
                    qry = qry.filter(
                        or_(TaskInstance.state.in_(not_none_states), TaskInstance.state.is_(None))
                    )
            else:
                qry = qry.filter(TaskInstance.state.in_(states))