namespace tensorflow {

// Set the shapes and types of the output's handle.
//
// The lengths of the arrays pointed to by `shapes`, `ranks`, and `types` must
// all be equal to `num_shapes_and_types`. If `ranks[i] != -1`, (i.e., if the
// rank is known), then it must be equal to the length of `shapes[i]`; if
// `ranks[i] == 1`, then `shapes[i]` may be nullptr.
//
// TODO(akshayka): Implement a corresponding getter method.

  }

  tensorflow::shape_inference::ShapeHandle shape = ic->output(output.index);

  int rank = -1;
  if (ic->RankKnown(shape)) {
    rank = ic->Rank(shape);
  }

  if (num_dims != rank) {
    status->status = InvalidArgument("Expected rank is ", num_dims,
                                     " but actual rank is ", rank);
    return;
  }

  if (num_dims == 0) {
    // Output shape is a scalar.

        as `tf.tpu.experimental.embedding.serving_embedding_lookup` which can
        take arbitrary rank of dense and sparse tensor. For ragged tensor,
        though the input tensor remains to be rank 2, the activations now can be
        rank 2 or above by specifying the output shape in the feature config or
        via the build method.

*   Add

    }

    /**
     * Loads the available searcher names from system properties.
     * Parses the "rank.fusion.searchers" system property to determine which searchers
     * are available for use in rank fusion processing.
     */
    protected void load() {
        final String value = System.getProperty("rank.fusion.searchers");
        if (StringUtil.isBlank(value)) {

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

        final ArtifactStatus that = (ArtifactStatus) o;

        return rank == that.rank;
    }

    @Override
    public int hashCode() {
        return rank;
    }

    @Override
    public String toString() {
        return key;
    }

    @Override
    public int compareTo(ArtifactStatus s) {
        return rank - s.rank;
    }

    this.rankMap = rankMap;
  }

  @Override
  public int compare(T left, T right) {
    return rank(left) - rank(right); // safe because both are nonnegative
  }

  private int rank(T value) {
    Integer rank = rankMap.get(value);
    if (rank == null) {
      throw new IncomparableValueException(value);
    }
    return rank;
  }

  @Override
  public boolean equals(@Nullable Object object) {

    this.rankMap = rankMap;
  }

  @Override
  public int compare(T left, T right) {
    return rank(left) - rank(right); // safe because both are nonnegative
  }

  private int rank(T value) {
    Integer rank = rankMap.get(value);
    if (rank == null) {
      throw new IncomparableValueException(value);
    }
    return rank;
  }

  @Override
  public boolean equals(@Nullable Object object) {

  abstract Map<Integer, Double> multipleQuantiles(
      Collection<Integer> indexes, int scale, double[] dataset);

  static double getMinValue(double[] array, int from) {
    // This is basically a copy of com.google.math.Rank#getMinValue, with a small change in the
    // method signature: we always search to the end of the array.
    int min = from;
    for (int i = from + 1; i < array.length; i++) {
      if (array[min] > array[i]) {

 */
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