Also, please include a link to a GraphDef or the model if possible.

**Any other info / logs**

Include any logs or source code that would be helpful to diagnose the problem.
If including tracebacks, please include the full traceback. Large logs and files

the problem. If possible, please share a link to or attach code demonstrating
the problem.

**Any other info / logs**
Include any logs or source code that would be helpful to diagnose the problem.
If including tracebacks, please include the full traceback. Large logs and files

class MySuperContextManager:
    def __init__(self):
        self.db = DBSession()

    def __enter__(self):
        return self.db

    def __exit__(self, exc_type, exc_value, traceback):
        self.db.close()


async def get_db():
    with MySuperContextManager() as db:

If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title.

### 5. (optional) Any other info / logs

Only valid on functions that declare a frame size of 0.
</li>
<li>
<code>TOPFRAME</code> = 2048
<br>
(For <code>TEXT</code> items.)
Function is the outermost frame of the call stack. Traceback should stop at this function.
</li>
</ul>

<h3 id="special-instructions">Special instructions</h3>

<p>

    `tf.debugging.disable_traceback_filtering()`, and can be re-enabled via
    `tf.debugging.enable_traceback_filtering()`. If you are debugging a
    TensorFlow-internal issue (e.g. to prepare a TensorFlow PR), make sure to
    disable traceback filtering. You can check whether this feature is currently
    enabled by calling `tf.debugging.is_traceback_filtering_enabled()`.

    ```
    """

    def __init__(
        self: AppType,
        *,
        debug: Annotated[
            bool,
            Doc(
                """
                Boolean indicating if debug tracebacks should be returned on server
                errors.

                Read more in the
                [Starlette docs for Applications](https://www.starlette.io/applications/#instantiating-the-application).