import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import org.codelibs.core.lang.StringUtil;
import org.codelibs.fess.cors.CorsHandler;
import org.codelibs.fess.cors.CorsHandlerFactory;
import org.codelibs.fess.util.ComponentUtil;

public class CorsFilter implements Filter {

    private static final Logger logger = LogManager.getLogger(CorsFilter.class);

import org.apache.logging.log4j.Logger;
import org.codelibs.core.exception.IORuntimeException;
import org.codelibs.fess.Constants;

public class ThreadDumpUtil {
    private static final Logger logger = LogManager.getLogger(ThreadDumpUtil.class);

    protected ThreadDumpUtil() {
        // noop
    }

    public static void printThreadDump() {
        processThreadDump(logger::info);
    }

* **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.

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());
        }

 */
package org.codelibs.core.lang;

import org.codelibs.core.exception.InterruptedRuntimeException;
import org.codelibs.core.log.Logger;

/**
 * Utility class for Thread class
 *
 * @author shinsuke
 *
 */
public abstract class ThreadUtil {

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

    public static void sleep(final long millis) {
        if (millis < 1L) {
            return;

    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));

        try {
            if (logger.isDebugEnabled()) {
                logger.debug("Shutting down thread executor.");
            }
            executor.shutdown();
            executor.awaitTermination(executorTerminationTimeout, TimeUnit.SECONDS);
        } catch (final InterruptedException e) {
            if (logger.isDebugEnabled()) {
                logger.debug("Failed to interrupt executor.", e);
            }

import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import org.codelibs.fess.helper.CrawlerStatsHelper.StatsKeyObject;
import org.codelibs.fess.unit.UnitFessTestCase;
import org.codelibs.fess.util.ComponentUtil;

public class CrawlerStatsHelperTest extends UnitFessTestCase {

    private static final Logger logger = LogManager.getLogger(CrawlerStatsHelperTest.class);

import javax.xml.XMLConstants;
import javax.xml.validation.SchemaFactory;

import org.codelibs.core.log.Logger;
import org.xml.sax.SAXException;

/**
 * {@link SchemaFactory}のためのユーティリティ・クラスです。
 *
 * @author koichik
 */
public abstract class SchemaFactoryUtil {

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

    /**
     * W3C XML Schemaのための{@link SchemaFactory}を生成します。
     *

			g.errs[index].Err = nil
			if err := f(); err != nil {
				g.errs[index].Err = err
				// Last iteration log the error.
				if i == g.retryCount-1 {
					reqInfo := (&logger.ReqInfo{}).AppendTags("peerAddress", addr.String())
					ctx := logger.SetReqInfo(ctx, reqInfo)
					peersLogOnceIf(ctx, err, addr.String())
				}
				// Wait for a minimum of 100ms and dynamically increase this based on number of attempts.