**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
nodes = []
        edges = []
        for dag, dependencies in SerializedDagModel.get_dag_dependencies().items():
            dag_node_id = f"dag:{dag}"
            nodes.append(self._node_dict(dag_node_id, dag, "dag"))
            for dep in dependencies:
                nodes.append(self._node_dict(dep.node_id, dep.dependency_id, dep.dependency_type))
                edges.extend(
                    [
                        {"u": f"dag:{dep.source}", "v": dep.node_id},
                        {"u": dep.node_id, "v": f"dag:{dep.target}"},
                    ]
                )
        self.nodes = nodes
        self.edges = edges
    @staticmethod
    def _node_dict(node_id, label, node_class):
        return {
            "id": node_id,
            "value": {"label": label, "rx": 5, "ry": 5, "class": node_class},
        }
class CustomPermissionModelView(PermissionModelView):