**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
raise DuplicateTaskIdFound(f"Task id '{task.task_id}' has already been added to the DAG")
        else:
            self.task_dict[task.task_id] = task
            task.dag = self
            
            self._task_group.used_group_ids.add(task.task_id)
        self.task_count = len(self.task_dict)
    def add_tasks(self, tasks):
        """
        Add a list of tasks to the DAG
        :param tasks: a lit of tasks you want to add
        :type tasks: list of tasks
        """
        for task in tasks:
            self.add_task(task)
    def run(
        self,
        start_date=None,
        end_date=None,
        mark_success=False,
        local=False,
        executor=None,
        donot_pickle=conf.getboolean('core', 'donot_pickle'),
        ignore_task_deps=False,
        ignore_first_depends_on_past=True,
        pool=None,
        delay_on_limit_secs=1.0,
        verbose=False,
        conf=None,
        rerun_failed_tasks=False,