}
  }
  return resource_vec;
}

LogicalResult _TfrtGetResourceOp::verify() {
  _TfrtGetResourceOp get_resource_op = *this;
  // The sizes of indices, shared_name and container must be equal.
  int32_t indices_size =
      get_resource_op->getAttrOfType<mlir::ArrayAttr>("indices").size();
  int32_t shared_name_size =
      get_resource_op->getAttrOfType<mlir::ArrayAttr>("shared_name").size();
  int32_t container_size =

  require(name.isNotEmpty()) { "name is empty" }
  for (i in name.indices) {
    val c = name[i]
    require(c in '\u0021'..'\u007e') {
      "Unexpected char 0x${c.charCode()} at $i in header name: $name"
    }
  }
}

internal fun headersCheckValue(
  value: String,
  name: String,
) {
  for (i in value.indices) {
    val c = value[i]
    require(c == '\t' || c in '\u0020'..'\u007e') {

  if (!ranked_ty) return UnrankedTensorType::get(element_ty);
  int64_t rank = ranked_ty.getRank();

  DenseIntElementsAttr indices;
  if (!matchPattern(reduction_indices, m_Constant(&indices))) {
    // Output type is unranked if reduction indices are not constant and reduced
    // dimensions are not kept.
    if (!keep_dims.getValue()) return UnrankedTensorType::get(element_ty);

  let description = [{
    The op compiles and executes a GPU cluster function.

    func_name is the name of the function to be executed on GPU.
    resource_indices are the indices of inputs that are resources.
    used_output_indices are the indices of outputs that have users.

    Example:
      %results = gpurt.compile_and_execute {func_name = "xla_func_0", resource_indices = [1] ...}
  }];

  let arguments = (ins

  // CHECK: [[ch3:%.*]], [[results:%.*]]:2 = tfrt_fallback_async.get_resource [[ready_ch]] {device = "/device:CPU:0", indices = [0, 1]}
  // CHECK: tfrt_fallback_async.executeop key({{.*}}) cost({{.*}}) device("/device:CPU:0") "tf.MatMul"([[results]]#0, [[results]]#1)
  %a, %b = "tf._TfrtGetResource"() {device = "/device:CPU:0", indices = [0, 1], shared_name = ["", ""], container = ["", ""]} : () -> (tensor<3x1xf32>, tensor<1x3xf32>)

import org.codelibs.opensearch.runner.OpenSearchRunner;
import org.junit.AfterClass;
import org.junit.Before;
import org.junit.BeforeClass;
import org.junit.Test;
import org.opensearch.action.admin.indices.analyze.AnalyzeAction.AnalyzeToken;
import org.opensearch.index.IndexNotFoundException;

public class AnalyzerSettingsTest {
    String id = "analyzerSettingsTest";

    static SuggestSettings settings;

    Stateful operands correspond to TF's variables semantics. An op that has 1
    or more stateful operands is a stateful op.
  }];

  let methods = [
    InterfaceMethod<
      [{Returns the indices of stateful operands.}],
      "std::vector<int>", "GetStatefulOperands", (ins)
    >,
  ];
}

//===----------------------------------------------------------------------===//
// TFL op interface for sparse operands.

    CustomOpInfo new_node_info;
    switch (update_option) {
      case CustomOpUpdateOptions::kInputIndices: {
        const std::vector<std::string> indices =
            absl::StrSplit(node_specification, '-');
        for (const std::string& cur_index : indices) {
          custom_op_map[node_name].quantizable_input_indices.push_back(
              std::stoi(cur_index));
        }
        break;
      }

    exit 1
  fi
  sleep 1
done

pushd /tmp >/dev/null
git clone https://github.com/codelibs/fess-testdata.git
popd >/dev/null

cat ${temp_log_file} ./fess-*/logs/*.log
curl -s "http://localhost:9201/_cat/indices?v"

 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.suggest.analysis;

import java.util.List;

import org.opensearch.action.admin.indices.analyze.AnalyzeAction.AnalyzeToken;

public interface SuggestAnalyzer {
    List<AnalyzeToken> analyze(String text, String field, String lang);