**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
task_log_reader = TaskLogReader()
        if task_log_reader.supports_external_link:
            external_log_name = task_log_reader.log_handler.log_name
        else:
            external_log_name = None
        data = self._get_tree_data(dag_runs, dag, base_date)
        
        data = htmlsafe_json_dumps(data, separators=(',', ':'))
        return self.render_template(
            'airflow/tree.html',
            operators=sorted({op.task_type: op for op in dag.tasks}.values(), key=lambda x: x.task_type),
            root=root,
            form=form,
            dag=dag,
            doc_md=doc_md,
            data=data,
            num_runs=num_runs,
            show_external_log_redirect=task_log_reader.supports_external_link,
            external_log_name=external_log_name,
        )
    @expose('/calendar')
    @auth.has_access(
        [
            (permissions.ACTION_CAN_READ, permissions.RESOURCE_DAG),
            (permissions.ACTION_CAN_READ, permissions.RESOURCE_TASK_INSTANCE),
        ]
    )
    @gzipped  
    @action_logging  
    def calendar(self):