**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
:param rerun_failed_tasks:
        :type: bool
        :param run_backwards:
        :type: bool
        """
        from airflow.jobs.backfill_job import BackfillJob
        if not executor and local:
            from airflow.executors.local_executor import LocalExecutor
            executor = LocalExecutor()
        elif not executor:
            from airflow.executors.executor_loader import ExecutorLoader
            executor = ExecutorLoader.get_default_executor()
        job = BackfillJob(
            self,
            start_date=start_date,
            end_date=end_date,
            mark_success=mark_success,
            executor=executor,
            donot_pickle=donot_pickle,
            ignore_task_deps=ignore_task_deps,
            ignore_first_depends_on_past=ignore_first_depends_on_past,
            pool=pool,
            delay_on_limit_secs=delay_on_limit_secs,
            verbose=verbose,
            conf=conf,
            rerun_failed_tasks=rerun_failed_tasks,
            run_backwards=run_backwards,
        )
        job.run()
    def cli(self):