import org.opensearch.action.admin.indices.exists.indices.IndicesExistsResponse;
import org.opensearch.action.admin.indices.mapping.get.GetFieldMappingsResponse;
import org.opensearch.action.admin.indices.mapping.get.GetFieldMappingsResponse.FieldMappingMetadata;
import org.opensearch.action.admin.indices.mapping.get.GetMappingsResponse;
import org.opensearch.action.admin.indices.mapping.put.PutMappingRequestBuilder;

	VONE	V16
	VREPH	$0, V0, V1
	CMPBGE	R9, R2, index2to16
index2loop:
	VL	0(R7), V2          // 16 bytes, even indices
	VL	1(R7), V4          // 16 bytes, odd indices
	VCEQH	V1, V2, V5         // compare even indices
	VCEQH	V1, V4, V6         // compare odd indices
	VSEL	V5, V6, V31, V7    // merge even and odd indices
	VFEEBS	V16, V7, V17       // find leftmost index, set condition to 1 if found
	BLT	foundV17

     *
     * @param target the target location
     * @param source the source location
     * @param indices the list of integers for the indices
     * @return the merged location
     */
    public static InputLocation merge(InputLocation target, InputLocation source, Collection<Integer> indices) {
        if (source == null) {
            return target;

  for i in range(len(shape)):
    rdims = tf.raw_ops.OneHot(
        indices=i, depth=len(shape), on_value=True, off_value=False, axis=-1)
    rarray = tf.raw_ops.Reverse(tensor=input_, dims=rdims)

    left_padding_size = tf.raw_ops.GatherNd(params=paddings, indices=[i, 0])
    right_padding_size = tf.raw_ops.GatherNd(params=paddings, indices=[i, 1])

    if mode == 'REFLECT':
      left_padding, _ = tf.raw_ops.SplitV(

namespace {

// Returns contiguous offset (address) of the position represented by `indices`
// in a `shape` shaped tensor. Assumes row-major order. `indices` and `shape`
// should have the same size.
// Example: Index (2, 3) of a (4, 5)-shaped tensor has the contiguous offset of
// 2 * 5 + 3 = 13.
int64_t GetContiguousOffset(const ArrayRef<int64_t> indices,
                            const ArrayRef<int64_t> shape) {

func.func @main() -> (!tf_mlrt.tensor, !tf_mlrt.tensor) {
  // CHECK-NEXT: [[r:%.*]]:3 = tf_mlrt.get_resource {indices = [2, 0, 1]}
  // CHECK-NEXT: [[v:%.*]] = tf_mlrt.executeop([[r]]#0, [[r]]#1)
  // CHECK-NEXT: return [[v]], [[r]]#2
  %0 = tf_mlrt.get_resource {indices = [2]} : !tf_mlrt.tensor
  %1 = tf_mlrt.get_resource {indices = [0]} : !tf_mlrt.tensor

     *
     * @param target the target location
     * @param source the source location
     * @param indices the list of integers for the indices
     * @return the merged location
     */
    public static InputLocation merge(InputLocation target, InputLocation source, Collection<Integer> indices) {
        if (source == null) {
            return target;

import java.util.List;

import org.codelibs.fess.suggest.settings.AnalyzerSettings;
import org.codelibs.fess.suggest.settings.SuggestSettings;
import org.opensearch.action.admin.indices.analyze.AnalyzeAction;
import org.opensearch.action.admin.indices.analyze.AnalyzeAction.AnalyzeToken;
import org.opensearch.client.Client;

public class AnalyzerNormalizer implements Normalizer {
    protected final Client client;

public final class ProjectIndex {

    private final Map<String, MavenProject> projects;

    private final Map<String, Integer> indices;

    public ProjectIndex(List<MavenProject> projects) {
        this.projects = new HashMap<>(projects.size() * 2);
        this.indices = new HashMap<>(projects.size() * 2);

        for (int i = 0; i < projects.size(); i++) {
            MavenProject project = projects.get(i);

                         std::vector<Output>* grad_outputs) {
  // Running example:
  // indices = {2, [1, 0]}
  // data = {[d_1, d_2], [[d_3, d_4], [d_5, d_6]]}
  // out = [[d_5, d_6], [d_3, d_4], [d_1, d_2]]
  // grad = [[g_1, g_2], [g_3, g_4], [g_5, g_6]]

  // indices and data are two equal-sized lists passed
  // into DynamicStitch.
  // num_values = 2