* Add a flag allowing contention profiling of the API server ([#36756](https://github.com/kubernetes/kubernetes/pull/36756), [@gmarek](https://github.com/gmarek))

- Profiling is enabled by default in the scheduler ([#84835](https://github.com/kubernetes/kubernetes/pull/84835), [@denkensk](https://github.com/denkensk))

async def run_endpoint_function(
    *, dependant: Dependant, values: dict[str, Any], is_coroutine: bool
) -> Any:
    # Only called by get_request_handler. Has been split into its own function to
    # facilitate profiling endpoints, since inner functions are harder to profile.
    assert dependant.call is not None, "dependant.call must be a function"

    if is_coroutine:
        return await dependant.call(**values)
    else:

* Fix comment and optimize code ([#38084](https://github.com/kubernetes/kubernetes/pull/38084), [@tanshanshan](https://github.com/tanshanshan))
* Add flag to enable contention profiling in scheduler. ([#37357](https://github.com/kubernetes/kubernetes/pull/37357), [@gmarek](https://github.com/gmarek))

- LoadBalancerIPMode feature was marked as GA. ([#127348](https://github.com/kubernetes/kubernetes/pull/127348), [@RyanAoh](https://github.com/RyanAoh)) [SIG Apps, Network and Testing]
- Locked the custom profiling feature in `kubectl debug` to true. ([#127187](https://github.com/kubernetes/kubernetes/pull/127187), [@ardaguclu](https://github.com/ardaguclu)) [SIG CLI and Testing]
- Output for the `ScalingReplicaSet` event has changed from:

- Kube-controller-manager and kube-scheduler expose profiling by default to match the kube-apiserver.  Use `--profiling=false` to disable. ([#88663](https://github.com/kubernetes/kubernetes/pull/88663), [@deads2k](https://github.com/deads2k)) [SIG API Machinery, Cloud Provider and Scheduling]

    formats during training, improving performance by up to 3x on GPUs and 60%
    on TPUs. Please see below for additional details.

*   TensorFlow Profiler now supports profiling `MultiWorkerMirroredStrategy` and
    tracing multiple workers using the
    [sampling mode API](https://www.tensorflow.org/guide/profiler#profiling_apis).

*   TFLite Profiler for Android is available. See the detailed

* Refactor calling each a path operation's handler function in an isolated function, to simplify profiling. PR [#1027](https://github.com/tiangolo/fastapi/pull/1027) by [@sm-Fifteen](https://github.com/sm-Fifteen).

    /**
     * Construct a new <code>MojoFailureException</code> exception providing a message.
     *
     * @param message
     */
    public MojoFailureException(String message) {
        super(message);
    }

    /**
     * Construct a new <code>MojoFailureException</code> exception wrapping an underlying <code>Throwable</code>
     * and providing a <code>message</code>.
     *
     * @param message
     * @param cause

     * and providing a <code>message</code>.
     *
     * @param message
     * @param cause
     */
    public MojoExecutionException(String message, Exception cause) {
        super(message, cause);
    }

    /**
     * Construct a new <code>MojoExecutionException</code> exception wrapping an underlying <code>Throwable</code>
     * and providing a <code>message</code>.
     *