Ensure that this works:

```python
@setup
def mytask():
    print("I am a setup task")

with dag_maker() as dag:
    mytask.override(task_id='newtask')

assert len(dag.task_group.children) == 1
setup_task = dag.task_group.children["newtask"]
assert setup_task._is_setup
```
and teardown also works
should be marked as skipped, and depends_on_past is true, the ti will stay on None state
        waiting the task of the previous run
    :param wait_for_downstream: when set to true, an instance of task
        X will wait for tasks immediately downstream of the previous instance
        of task X to finish successfully or be skipped before it runs. This is useful if the
        different instances of a task X alter the same asset, and this asset
        is used by tasks downstream of task X. Note that depends_on_past
        is forced to True wherever wait_for_downstream is used. Also note that
        only tasks *immediately* downstream of the previous task instance are waited
        for; the statuses of any tasks further downstream are ignored.
    :param dag: a reference to the dag the task is attached to (if any)
    :param priority_weight: priority weight of this task against other task.
        This allows the executor to trigger higher priority tasks before
        others when things get backed up. Set priority_weight as a higher
        number for more important tasks.
    :param weight_rule: weighting method used for the effective total
        priority weight of the task. Options are:
        ``{ downstream | upstream | absolute }`` default is ``downstream``
        When set to ``downstream`` the effective weight of the task is the
        aggregate sum of all downstream descendants. As a result, upstream
        tasks will have higher weight and will be scheduled more aggressively
        when using positive weight values. This is useful when you have
        multiple dag run instances and desire to have all upstream tasks to
        complete for all runs before each dag can continue processing
        downstream tasks. When set to ``upstream`` the effective weight is the
        aggregate sum of all upstream ancestors. This is the opposite where
        downstream tasks have higher weight and will be scheduled more
        aggressively when using positive weight values. This is useful when you
        have multiple dag run instances and prefer to have each dag complete
        before starting upstream tasks of other dags.  When set to