: EnumSet.of(IssueLocality.EXTERNAL);

        if (hasAnythingToReport(issuesMap, issueLocalitiesToReport)) {
            logger.warn("");
            logger.warn("Plugin {} validation issues were detected in following plugin(s)", issueLocalitiesToReport);
            logger.warn("");
            for (Map.Entry<String, PluginValidationIssues> entry : issuesMap.entrySet()) {

                if (logger.isDebugEnabled()) {
                    logger.debug("Interrupted.", e);
                }
            }
        }

        public void awaitTermination(final long mills) {
            try {
                join(mills);
            } catch (final InterruptedException e) {
                if (logger.isDebugEnabled()) {

public class LabelTypeHelper extends AbstractConfigHelper {
    private static final Logger logger = LogManager.getLogger(LabelTypeHelper.class);

    protected volatile List<LabelTypeItem> labelTypeItemList;

    protected volatile List<LabelTypePattern> labelTypePatternList;

    @PostConstruct
    public void init() {
        if (logger.isDebugEnabled()) {
            logger.debug("Initialize {}", this.getClass().getSimpleName());
        }

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.
* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

          if (!buffer.isProbablyUtf8()) {
            logger.log("")
            logger.log("<-- END HTTP (${totalMs}ms, binary ${buffer.size}-byte body omitted)")
            return response
          }

          if (contentLength != 0L) {
            logger.log("")
            logger.log(buffer.clone().readString(charset))
          }

          logger.log(
            buildString {

          return 0;
        }
      };

  // We use a holder class to delay initialization: https://github.com/google/guava/issues/6566
  private static final class LoggerHolder {
    static final Logger logger = Logger.getLogger(CacheBuilder.class.getName());
  }

  static final int UNSET_INT = -1;

  boolean strictParsing = true;

  int initialCapacity = UNSET_INT;
  int concurrencyLevel = UNSET_INT;

        logger.debug("Populating class realm {}", classRealm.getId());

        for (ClassRealmConstituent constituent : constituents) {
            File file = constituent.getFile();

            if (logger.isDebugEnabled()) {
                String id = getId(constituent);
                logger.debug("  Included: {}", id);
            }

            try {

                            if (logger.isDebugEnabled()) {
                                logger.debug("generator path: {}", filePath);
                            }
                            return filePath;
                        }
                    }
                }
                if (logger.isDebugEnabled()) {
                    logger.debug("generator path: {}", s);
                }

            if (logger.isDebugEnabled()) {
                logger.debug("SuggestCreator is stopped.", e);
            } else if (logger.isInfoEnabled()) {
                logger.info("SuggestCreator is stopped.");
            }
            exitCode = Constants.EXIT_FAIL;
        } catch (final Throwable t) {
            logger.error("Suggest creator does not work correctly.", t);

    return suppressedTests;
  }

  private static final Logger logger =
      Logger.getLogger(FeatureSpecificTestSuiteBuilder.class.getName());

  /** Creates a runnable JUnit test suite based on the criteria already given. */
  public TestSuite createTestSuite() {
    checkCanCreate();

    logger.fine(" Testing: " + name);
    logger.fine("Features: " + formatFeatureSet(features));