**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
"""This method is deprecated in favor of partial_subset"""
        warnings.warn(
            "This method is deprecated and will be removed in a future version. Please use partial_subset",
            DeprecationWarning,
            stacklevel=2,
        )
        return self.partial_subset(*args, **kwargs)
    def partial_subset(
        self,
        task_ids_or_regex: Union[str, RePatternType, Iterable[str]],
        include_downstream=False,
        include_upstream=True,
        include_direct_upstream=False,
    ):
        """
        Returns a subset of the current dag as a deep copy of the current dag
        based on a regex that should match one or many tasks, and includes
        upstream and downstream neighbours based on the flag passed.
        :param task_ids_or_regex: Either a list of task_ids, or a regex to