**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
)
                    for ti in tis
                ]
            )
        )
        tasks = []
        for ti in tis:
            
            
            
            try_count = ti.prev_attempted_tries if ti.prev_attempted_tries != 0 else ti.try_number
            task_dict = alchemy_to_dict(ti)
            task_dict['end_date'] = task_dict['end_date'] or timezone.utcnow()
            task_dict['extraLinks'] = dag.get_task(ti.task_id).extra_links
            task_dict['try_number'] = try_count
            tasks.append(task_dict)
        tf_count = 0
        try_count = 1
        prev_task_id = ""
        for failed_task_instance in ti_fails:
            if tf_count != 0 and failed_task_instance.task_id == prev_task_id:
                try_count += 1
            else:
                try_count = 1
            prev_task_id = failed_task_instance.task_id
            tf_count += 1
            task = dag.get_task(failed_task_instance.task_id)
            task_dict = alchemy_to_dict(failed_task_instance)
            end_date = task_dict['end_date'] or timezone.utcnow()
            task_dict['end_date'] = end_date