field.Size = -1
		}
	}

	if p, ok := field.TagSettings["PRECISION"]; ok {
		field.Precision, _ = strconv.Atoi(p)
	}

	if s, ok := field.TagSettings["SCALE"]; ok {
		field.Scale, _ = strconv.Atoi(s)
	}

	// default value is function or null or blank (primary keys)
	field.DefaultValue = strings.TrimSpace(field.DefaultValue)

        document.prependChild(new DocumentType("html", "", ""));
        document.head().
                append("<meta charset='utf-8'>").
                append("<meta name='viewport' content='width=device-width, initial-scale=1'>").
                append("<title>Gradle @version@ Release Notes</title>").
                append("<link rel='stylesheet' type='text/css' href='https://assets.gradle.com/lato/css/lato-font.css'/>");

}

TFE_MonitoringBuckets* TFE_MonitoringNewExponentialBuckets(double scale,
                                                           double growth_factor,
                                                           int bucket_count) {
  return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() {
    return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,

	// can easily eat into the internode bandwidth. This function would be mostly
	// TX saturating, however there are situations where a RX might also saturate.
	// To avoid these problems we must split the work at scale. With 1000 node
	// setup becoming a reality we must try to shard the work properly such as
	// pick 10 nodes that precisely can send those 100 requests the first node

<a href="https://platform.sh/try-it-now/?utm_source=fastapi-signup&utm_medium=banner&utm_campaign=FastAPI-signup-June-2023" target="_blank" title="Build, run and scale your apps on a modern, reliable, and secure PaaS."><img src="https://fastapi.tiangolo.com/img/sponsors/platform-sh.png"></a>

      * If you passed a value to the `loss_scale` argument (the second argument) of `Policy`:

</p>

<pre>
func (p *Point) Length() float64 {
	return math.Sqrt(p.x * p.x + p.y * p.y)
}

func (p *Point) Scale(factor float64) {
	p.x *= factor
	p.y *= factor
}
</pre>

<p>
bind the methods <code>Length</code> and <code>Scale</code>,
with receiver type <code>*Point</code>,
to the base type <code>Point</code>.
</p>

<p>

- From now on, the HPA controller will return an error for the container resource metrics when the feature gate "HPAContainerMetrics" is disabled. As a result, HPA with a container resource metric performs no scale-down and performs only scale-up based on other metrics. ([#116043](https://github.com/kubernetes/kubernetes/pull/116043), [@sanposhiho](https://github.com/sanposhiho)) [SIG API Machinery, Apps and Autoscaling]

</p>

<pre>
func (p *Point) Length() float64 {
	return math.Sqrt(p.x * p.x + p.y * p.y)
}

func (p *Point) Scale(factor float64) {
	p.x *= factor
	p.y *= factor
}
</pre>

<p>
bind the methods <code>Length</code> and <code>Scale</code>,
with receiver type <code>*Point</code>,
to the base type <code>Point</code>.
</p>

<p>

					"process-services",
					"build-configuration",
					"base-services"
				]
			},
			{
				"parallelizationMethod":{
					"name":"TestDistribution"
				},
				"subprojects":[
					"scala",
					"plugins-java-base",
					"build-cache",
					"messaging",
					"war",
					"java-platform",
					"build-profile",
					"resources-gcs",
					"ear",
					"plugins-distribution",