* worst case time complexity of O(N^2). You are extremely unlikely to hit this quadratic case on
 * randomly ordered data (the probability decreases faster than exponentially in N), but if you are
 * passing in unsanitized user data then a malicious user could force it. A light shuffle of the
 * data using an unpredictable seed should normally be enough to thwart this attack.
 *

}

// exponentialMovingAverage calculates the exponential moving average
func exponentialMovingAverage(beta, previousAvg, incrementAvg float64) float64 {
	return (1-beta)*incrementAvg + beta*previousAvg
}

// getExpMovingAvgBytesPerSecond returns the exponential moving average for the bucket in bytes
func (m *bucketMeasurement) getExpMovingAvgBytesPerSecond() float64 {

}

// exponentialMovingAverage calculates the exponential moving average
func exponentialMovingAverage(beta, previousAvg, incrementAvg float64) float64 {
	return (1-beta)*incrementAvg + beta*previousAvg
}

// getExpMovingAvgBytesPerSecond returns the exponential moving average for the bucket/target in bytes
func (m *rateMeasurement) getExpMovingAvgBytesPerSecond() float64 {

	// If not set the client will not keep track of health.
	// Calling this returns true or false if the target
	// is online or offline.
	HealthCheckFn func() bool

	// HealthCheckRetryUnit will be used to calculate the exponential
	// backoff when trying to reconnect to an offline node
	HealthCheckReconnectUnit time.Duration

	// HealthCheckTimeout determines timeout for each call.
	HealthCheckTimeout time.Duration

                                                           int bucket_count) {
  return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() {
    return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,
                                                        bucket_count);
  });
}

void TFE_MonitoringDeleteBuckets(TFE_MonitoringBuckets* buckets) {
  delete buckets;
}

- Fixed an issue where failed pods associated with a job with `parallelism = 1` are recreated by the job controller honoring exponential backoff delay again. However, for jobs with `parallelism > 1`, pods might be created without exponential backoff delay. ([#114516](https://github.com/kubernetes/kubernetes/pull/114516), [@nikhita](https://github.com/nikhita))

- Failed pods associated with a job with `parallelism = 1` are recreated by the job controller honoring exponential backoff delay again. However, for jobs with `parallelism > 1`, pods might be created without exponential backoff delay. ([#115021](https://github.com/kubernetes/kubernetes/pull/115021), [@nikhita](https://github.com/nikhita)) [SIG Apps]

- Improved exponential backoff in Reflector, significantly reducing the load on Kubernetes apiserver in case of throttling of requests. ([#118132](https://github.com/kubernetes/kubernetes/pull/118132), [@marseel](https://github.com/marseel)) [SIG API Machinery and Scalability]...

- Failed pods associated with a job with `parallelism = 1` are recreated by the job controller honoring exponential backoff delay again. However, for jobs with `parallelism > 1`, pods might be created without exponential backoff delay. ([#115022](https://github.com/kubernetes/kubernetes/pull/115022), [@nikhita](https://github.com/nikhita)) [SIG Apps]

- Failed pods associated with a job with `parallelism = 1` are recreated by the job controller honoring exponential backoff delay again. However, for jobs with `parallelism > 1`, pods might be created without exponential backoff delay. ([#115027](https://github.com/kubernetes/kubernetes/pull/115027), [@nikhita](https://github.com/nikhita)) [SIG Apps]