**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
while graph_unsorted:
            
            
            
            
            
            
            
            
            
            
            
            
            acyclic = False
            for node in list(graph_unsorted.values()):
                for edge in node.upstream_list:
                    if edge.task_id in graph_unsorted:
                        break
                
                else:
                    acyclic = True
                    del graph_unsorted[node.task_id]
                    graph_sorted.append(node)
                    if include_subdag_tasks and isinstance(node, SubDagOperator):
                        graph_sorted.extend(node.subdag.topological_sort(include_subdag_tasks=True))
            if not acyclic:
                raise AirflowException(f"A cyclic dependency occurred in dag: {self.dag_id}")
        return tuple(graph_sorted)
    @provide_session
    def set_dag_runs_state(