String message = "Waited " + timeout + " " + unit.toString().toLowerCase(Locale.ROOT);
    // Only report scheduling delay if larger than our spin threshold - otherwise it's just noise
    if (remainingNanos + SPIN_THRESHOLD_NANOS < 0) {
      // We over-waited for our timeout.
      message += " (plus ";
      long overWaitNanos = -remainingNanos;

    String message = "Waited " + timeout + " " + unit.toString().toLowerCase(Locale.ROOT);
    // Only report scheduling delay if larger than our spin threshold - otherwise it's just noise
    if (remainingNanos + SPIN_THRESHOLD_NANOS < 0) {
      // We over-waited for our timeout.
      message += " (plus ";
      long overWaitNanos = -remainingNanos;

- Reduced log verbosity for high-frequency, low-value log entries in Job, IPAM, and ReplicaSet controllers by adjusting them to V(2), V(4) and V(4) respectively. This change minimizes log noise while maintaining access to these logs when needed. ([#130591](https://github.com/kubernetes/kubernetes/pull/130591), [@fmuyassarov](https://github.com/fmuyassarov)) [SIG Apps and Network]

 - Reduced state change noise when volume expansion fails. Also mark certain failures as infeasible.
  
  ACTION REQUIRED:  If you are using the `RecoverVolumeExpansionFailure` alpha feature gate

        pull request
        [47772](https://github.com/tensorflow/tensorflow/pull/47772).
    *   In previous versions of TensorFlow, when a GPU was available,
        `tf.sparse.sparse_dense_matmul` introduced truly random noise in the
        forward path for data of type `tf.float32` but not for data of type
        `tf.float64` (for which there was no GPU implementation). In this
        current release, GPU support for other floating-point types