Currently, the celery executor imports the `Task` model for the Celery database backend directly:

https://github.com/apache/airflow/blob/2dd4e96045d4a7f45cc8c06df3d25c4f1479392c/airflow/executors/celery_executor.py#L38

However, Celery, being highly customisable, uses `self.task_cls` in the backend implementation; this defaults to either `Task` or `TaskExtended` depending on the Celery configuration, see [`celery.backends.database:66`](https://github.com/celery/celery/blob/406f04a082949ac42ec7a4af94fed896c515aaa4/celery/backends/database/__init__.py#L66):

> ```python
>     task_cls = Task
> ```

and [`celery.backends.database:76,77`](https://github.com/celery/celery/blob/406f04a082949ac42ec7a4af94fed896c515aaa4/celery/backends/database/__init__.py#L76-L77):

> ```python
>         if self.extended_result:
>             self.task_cls = TaskExtended
> ```

Airflow should support this flexibility, and instead of importing a fixed class just use `backend.task_cls`, so in `_get_many_from_db_backend` just use:

```python
        Task = app.backend.task_cls
        with session_cleanup(session):
            tasks = session.query(Task).filter(Task.task_id.in_(task_ids)).all()
```

I suppose there is one downside to this: `TaskExtended` fetches a few more columns per task, but given that the result of `task.to_dict()` is passed to `backend.meta_from_decoded()`, which could also have been customised to make use of information contained in a custom model, that's neither here nor there. It would be the price anyone using the `result_extended` option would need to pay elsewhere anyway.
airflow/executors/celery_executor.py
"""
    Preload some "expensive" airflow modules so that every task process doesn't have to import it again and
    again.
    Loading these for each task adds 0.3-0.5s *per task* before the task can run. For long running tasks this
    doesn't matter, but for short tasks this starts to be a noticeable impact.
    """
    import jinja2.ext  
    import numpy  
    import airflow.jobs.local_task_job
    import airflow.macros
    import airflow.operators.bash
    import airflow.operators.python
    import airflow.operators.subdag_operator  
    try:
        import kubernetes.client  
    except ImportError:
        pass
class CeleryExecutor(BaseExecutor):
    """
    CeleryExecutor is recommended for production use of Airflow. It allows
    distributing the execution of task instances to multiple worker nodes.
    Celery is a simple, flexible and reliable distributed system to process
    vast amounts of messages, while providing operations with the tools
    required to maintain such a system.
    """
    def __init__(self):