void testSuccess() {
        assertEquals("SUCCESS", NbtException.getErrorString(NbtException.SUCCESS, 0));
    }

    @Test
    @DisplayName("getErrorString for name service error with known code")
    void testNameServiceKnown() {
        String expected = "ERR_NAM_SRVC/FMT_ERR: Format ErrorUnknown error code: 1";

# Run any CI script and env configuration to bisect a failing target in some
# build configuration. You must set the following variables to control this
# script:
#
#   TF_BISECT_GOOD: Last known good commit (e.g. commit from the last passing job)
#   TF_BISECT_BAD: First bad commit (e.g. commit from the first failing job)
#   TF_BISECT_SCRIPT: The build script path relative to the TF root dir, e.g.
#     ci/official/wheel.sh

        // "safe" edges which together form a cycle. Preventing this race
        // condition efficiently without _introducing_ deadlock is probably
        // tricky. For now, just accept the race condition---missing a warning
        // now and then is still better than having no deadlock detection.
        allowedPriorLocks.put(acquiredLock, new ExampleStackTrace(acquiredLock, this));
      } else {

 * under the License.
 */
package org.apache.maven.project;

import java.io.File;
import java.util.HashMap;
import java.util.Map;

/**
 * Holds all POM files that are known to the reactor. This allows the project builder to resolve imported POMs from the
 * reactor when building another project's effective model.
 */
class ReactorModelPool {

/**
 * Partial identification of an {@link Artifact} in a Maven repository.
 * Each {@code ArtifactCoordinates} instance is basically a pointer to a file in the Maven repository,
 * except that the exact version may not be known yet.
 *
 * @since 4.0.0
 */
@Experimental
@Immutable
public interface ArtifactCoordinates {
    /**
     * {@return the group identifier of the artifact}.
     */
    @Nonnull
    String getGroupId();

                        request.addRemoteRepository(MavenRepositorySystem.buildArtifactRepository(remoteRepository));
                    } catch (InvalidRepositoryException e) {
                        // do nothing for now
                    }
                }

                List<Repository> pluginRepositories = rawProfile.getPluginRepositories();
                for (Repository pluginRepo : pluginRepositories) {

			Size:  l.Totals[i].Size + o.Totals[i].Size,
		}
	}
	return merged
}

// Add  a new duration data
func (l *lastMinuteLatency) add(t time.Duration) {
	sec := time.Now().Unix()
	l.forwardTo(sec)
	winIdx := sec % 60
	l.Totals[winIdx].add(t)
	l.LastSec = sec
}

// Add  a new duration data
func (l *lastMinuteLatency) addAll(sec int64, a AccElem) {
	l.forwardTo(sec)

    return user


def create_access_token(data: dict, expires_delta: Union[timedelta, None] = None):
    to_encode = data.copy()
    if expires_delta:
        expire = datetime.now(timezone.utc) + expires_delta
    else:
        expire = datetime.now(timezone.utc) + timedelta(minutes=15)
    to_encode.update({"exp": expire})
    encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
    return encoded_jwt

WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

        TimeoutManager.getInstance().addTimeoutTarget(() -> {
            if (logger.isDebugEnabled()) {
                logger.debug("Updating scheduled jobs: time={}", schedulerTime);
            }
            final long now = systemHelper.getCurrentTimeAsLong();
            scheduledJobService.getScheduledJobListAfter(schedulerTime).forEach(scheduledJob -> {
                if (logger.isDebugEnabled()) {