indices += absl::StrCat("[dim", dim, "]");
      count *= shape.dimensions(dim);
    }
  }
  rewrites->push_back({"{{I}}", absl::StrCat(i)});
  rewrites->push_back({"{{TYPE}}", type});
  rewrites->push_back({"{{DIM_VARS}}", absl::StrJoin(dim_vars, ", ")});
  rewrites->push_back({"{{DIM_SIZES}}", dim_sizes});
  rewrites->push_back({"{{INDICES}}", indices});

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;

                                     ArrayRef<Value>{a, b});
}

namespace {

int64_t GetFirstIfIndicesAreContiguous(Value indices) {
  auto type = mlir::dyn_cast<RankedTensorType>(indices.getType());
  if (!type) return -1;
  auto indices_op = indices.getDefiningOp();
  if (!indices_op) return -1;
  auto const_op = llvm::dyn_cast<TF::ConstOp>(indices_op);
  if (!const_op) return -1;

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;

import org.opensearch.action.admin.indices.alias.IndicesAliasesRequestBuilder;
import org.opensearch.action.admin.indices.create.CreateIndexResponse;
import org.opensearch.action.admin.indices.exists.indices.IndicesExistsResponse;
import org.opensearch.action.admin.indices.flush.FlushResponse;
import org.opensearch.action.admin.indices.get.GetIndexResponse;
import org.opensearch.action.admin.indices.mapping.get.GetMappingsResponse;

  //
  // T* argN_data()
  //   Returns the buffer of type T for positional argument N.
  //
  // T& argN(...dim indices...)
  //   Returns a reference to the value of type T for positional argument N,
  //   with dim indices specifying which value. No bounds checking is performed
  //   on dim indices.

  void set_arg0_data(const void* data) {
    set_arg_data(0, data);
  }
  float* arg0_data() {

  let description = [{
This operation computes

    # Scalar indices
    ref[indices, ...] = max(ref[indices, ...], updates[...])

    # Vector indices (for each i)
    ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...])

    # High rank indices (for each i, ..., j)
    ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])

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);

  auto indices = Const(scope_, {{0, 0}, {1, 1}});
  TensorShape y_shape({2});
  auto y = GatherNd(scope_, x, indices);
  RunTest(x, x_shape, y, y_shape);
}

TEST_F(ArrayGradTest, GatherNdGrad_SliceIndexing) {
  TensorShape shape({2, 2});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto indices = Const(scope_, {{1}, {0}});