The focus of this release is **compatibility** with Pydantic v1 and v2, to make sure your current apps keep working. Later there will be more focus on refactors, correctness, code improvements, and then **performance** improvements. Some third-party early beta testers that ran benchmarks on the beta releases of FastAPI reported improvements of **2x - 3x**. Which is not bad for just doing `pip install --upgrade fastapi pydantic`. This was not an official benchmark and I didn't check it myself, but it's...

The ambient CNI agent is the only place where ambient network config and pod redirection machinery happens.

    *   Keras-based LSTM models must be converted with an explicit batch size in
        the input layer.

## Bug Fixes and Other Changes

### TF Core:

*   Set `tf2_behavior` to 1 to enable V2 for early loading cases.
*   Add `execute_fn_for_device function` to dynamically choose the
    implementation based on underlying device placement.
*   Eager:
    *   Add `reduce_logsumexp` benchmark with experiment compile.