char result = (char) value;
    checkArgument(result == value, "Out of range: %s", value);
    return result;
  }

  /**
   * Returns the {@code char} nearest in value to {@code value}.
   *
   * @param value any {@code long} value
   * @return the same value cast to {@code char} if it is in the range of the {@code char} type,

   *
   * @param value any value in the range of the {@code short} type
   * @return the {@code short} value that equals {@code value}
   * @throws IllegalArgumentException if {@code value} is greater than {@link Short#MAX_VALUE} or
   *     less than {@link Short#MIN_VALUE}
   */
  public static short checkedCast(long value) {
    short result = (short) value;
    checkArgument(result == value, "Out of range: %s", value);
    return result;
  }

   *
   * @param value any value in the range of the {@code short} type
   * @return the {@code short} value that equals {@code value}
   * @throws IllegalArgumentException if {@code value} is greater than {@link Short#MAX_VALUE} or
   *     less than {@link Short#MIN_VALUE}
   */
  public static short checkedCast(long value) {
    short result = (short) value;
    checkArgument(result == value, "Out of range: %s", value);
    return result;
  }

import com.google.common.collect.Range;
import java.util.Random;
import org.jspecify.annotations.NullUnmarked;

/** Benchmarks some algorithms providing the same functionality as {@link Quantiles}. */
@NullUnmarked
public class QuantilesBenchmark {

  private static final ContiguousSet<Integer> ALL_DECILE_INDEXES =
      ContiguousSet.create(Range.closed(0, 10), DiscreteDomain.integers());

         */
        public boolean isExistNextPage() {
            return currentPageNumber != allPageCount;
        }

        /**
         * Sets the page range size for navigation.
         *
         * @param pageRangeSize the page range size to set
         */
        public void setPageRangeSize(final int pageRangeSize) {
            this.pageRangeSize = pageRangeSize;
        }

        /**

* **Machine Learning**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande planilha com números e em multiplicar todos eles juntos e ao mesmo tempo.

* **Deep Learning**: Esse é um subcampo do Machine Learning, então, o mesmo se aplica. A diferença é que não há apenas uma grande planilha com números para multiplicar, mas um grande conjunto delas, e em muitos casos, você utiliza um processador especial para construir e/ou usar esses modelos.

   * constraining a {@code long} input to an {@code int} range.
   *
   * @param value the {@code int} value to constrain
   * @param min the lower bound (inclusive) of the range to constrain {@code value} to
   * @param max the upper bound (inclusive) of the range to constrain {@code value} to
   * @throws IllegalArgumentException if {@code min > max}
   * @since 21.0
   */

                            node.getArtifact().setVersionRange(currentRange.restrict(previousRange));

                            // Select an appropriate available version from the (now restricted) range
                            // Note this version was selected before to get the appropriate POM
                            // But it was reset by the call to setVersionRange on restricting the version

 */
package org.codelibs.fess.rank.fusion;

import java.util.Locale;

import org.codelibs.core.lang.StringUtil;
import org.codelibs.fess.entity.SearchRequestParams;
import org.codelibs.fess.mylasta.action.FessUserBean;
import org.dbflute.optional.OptionalThing;

/**
 * Abstract base class for rank fusion searchers in the Fess search system.
 * Rank fusion searchers are responsible for executing search queries and

	"http://dbflute.org/meta/lastadi10.dtd">
<components>
	<component name="rankFusionProcessor"
		class="org.codelibs.fess.rank.fusion.RankFusionProcessor">
		<postConstruct name="setSeacher">
			<arg>
				<component
					class="org.codelibs.fess.rank.fusion.DefaultSearcher">
				</component>
			</arg>
		</postConstruct>
	</component>