* goal name (required).
     * @return the goal name
     */
    @Nonnull
    String name();

    /**
     * default phase to bind your mojo.
     * @return the default phase
     */
    @Nonnull
    String defaultPhase() default "";

    /**
     * does your mojo requires a project to be executed?
     * @return requires a project
     */
    boolean projectRequired() default true;

    /**

     are reinstated (a) provisionally, unless and until such Contributor
     explicitly and finally terminates Your grants, and (b) on an ongoing basis,
     if such Contributor fails to notify You of the non-compliance by some
     reasonable means prior to 60 days after You have come back into compliance.
     Moreover, Your grants from a particular Contributor are reinstated on an

# Grafana Dashboards

This folder contains Istio's official Grafana dashboards.
These get publish to [Grafana](https://grafana.com/orgs/istio/dashboards) during release, and are bundled into our
[Grafana sample](../../../samples/addons/grafana.yaml).

## Jsonnet

Newer dashboards are generated with [Jsonnet](https://jsonnet.org/) with the [Grafonnet](https://grafana.github.io/grafonnet/index.html).

    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;
      long overWaitUnits = unit.convert(overWaitNanos, TimeUnit.NANOSECONDS);

    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;
      long overWaitUnits = unit.convert(overWaitNanos, TimeUnit.NANOSECONDS);

import java.net.URLClassLoader;
import java.util.Set;
import java.util.concurrent.atomic.AtomicReferenceFieldUpdater;
import junit.framework.TestCase;
import junit.framework.TestSuite;

/**
 * Tests our AtomicHelper fallback strategies in AbstractFuture.
 *
 * <p>On different platforms AbstractFuture uses different strategies for its core synchronization

import java.net.URLClassLoader;
import java.util.Set;
import java.util.concurrent.atomic.AtomicReferenceFieldUpdater;
import junit.framework.TestCase;
import junit.framework.TestSuite;

/**
 * Tests our AtomicHelper fallback strategies in AbstractFuture.
 *
 * <p>On different platforms AbstractFuture uses different strategies for its core synchronization

       * Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup).
     * If error still persists then, apparently your CPU model does not support AVX instruction sets.
       * Refer [hardware requirements](https://www.tensorflow.org/install/pip#hardware-requirements).
   

        api(libs.googleApiClient)       { version { strictly("1.34.0"); because("our GCS version requires 1.34.0") }}
        api(libs.guava)                 { version { strictly("32.1.2-jre"); because("our Google API Client version requires at least 31.1-jre")  }}
        api(libs.googleHttpClientGson)  { version { strictly("1.42.2"); because("our Google API Client version requires 1.42.2")  }}

 *
 * Analysis API platforms need to take care of publishing modification topics via the [analysisMessageBus]. In general, if your platform
 * works with static code and static module structure, you do not need to publish any events. However, keep in mind the contracts of the
 * various modification events. For example, if your platform can guarantee a static module structure but source code can still change,