**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
"""Runs Task Instance."""
        dag_id = request.form.get('dag_id')
        task_id = request.form.get('task_id')
        origin = get_safe_url(request.form.get('origin'))
        dag = current_app.dag_bag.get_dag(dag_id)
        task = dag.get_task(task_id)
        execution_date = request.form.get('execution_date')
        execution_date = timezone.parse(execution_date)
        ignore_all_deps = request.form.get('ignore_all_deps') == "true"
        ignore_task_deps = request.form.get('ignore_task_deps') == "true"
        ignore_ti_state = request.form.get('ignore_ti_state') == "true"
        executor = ExecutorLoader.get_default_executor()
        valid_celery_config = False
        valid_kubernetes_config = False
        try:
            from airflow.executors.celery_executor import CeleryExecutor  
            valid_celery_config = isinstance(executor, CeleryExecutor)
        except ImportError:
            pass
        try:
            from airflow.executors.kubernetes_executor import KubernetesExecutor  
            valid_kubernetes_config = isinstance(executor, KubernetesExecutor)
        except ImportError:
            pass
        if not valid_celery_config and not valid_kubernetes_config:
            flash("Only works with the Celery or Kubernetes executors, sorry", "error")
            return redirect(origin)
        ti = models.TaskInstance(task=task, execution_date=execution_date)
        ti.refresh_from_db()