switch n := x.(type) {
	default:
		error_(token.NoPos, "unexpected type %T in walk", x)
		panic("unexpected type")

	case *ast.IndexListExpr:
		f.walk(&n.X, ctxExpr, visit)
		f.walk(n.Indices, ctxExpr, visit)
	}
}

func funcTypeTypeParams(n *ast.FuncType) *ast.FieldList {
	return n.TypeParams
}

func typeSpecTypeParams(n *ast.TypeSpec) *ast.FieldList {
	return n.TypeParams

    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"

  let description = [{
    Get a tensor in resource array.

    indices: the indices in the resource array.
    results: the tensor values for the corresponding indices.

    in_ch and out_ch are for control dependencies.
  }];

  let arguments = (ins
    TFRT_ChainType:$in_ch,
    StrAttr:$device,
    I64ArrayAttr:$indices
  );

  let results = (outs
    TFRT_ChainType:$out_ch,

    /**
     * Method merge.
     *
     * @param target
     * @param indices
     * @param source
     * @return InputLocation
     */
    public static InputLocation merge(
            InputLocation target, InputLocation source, java.util.Collection<Integer> indices) {
        if (source == null) {
            return target;
        } else if (target == null) {

 * 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);

//===----------------------------------------------------------------------===//

def LowerTensorScatterAdd: Pat<
  (TF_TensorScatterAddOp $input, $indices, $updates),
  (TF_TensorScatterUpdateOp $input, $indices,
     (TF_AddOp $updates, (CreateGatherNdOp $updates, $input, $indices)))>;

//===----------------------------------------------------------------------===//
// AddV2 op patterns.

  (replaceWithValue $params),
  [(CanOptimizeIdentityGatherNdOrScatterNdOp $params, $indices, $output)]>;

def OptimizeIdentityScatterNdOp : Pat<
  (TFL_ScatterNdOp:$output (Arith_ConstantOp I32ElementsAttr: $indices), $params, $ignored),
  (replaceWithValue $params),
  [(CanOptimizeIdentityGatherNdOrScatterNdOp $params, $indices, $output)]>;

    }
  }

  return indices;
}

// Given a list of tensor indices, returns true if any of the tensors have
// non-empty name strings.
bool HasNonEmptyNames(const tflite::SubGraphT& subgraph,
                      ArrayRef<int32_t> indices) {
  return llvm::any_of(
      indices, [&](int i) { return !subgraph.tensors.at(i)->name.empty(); });
}

  %indices = "tf.Const"() {value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
  %values = "tf.Const"() {value = dense<[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
  %write = "tf.TensorArrayScatterV3"(%ta#0, %indices, %values, %ta#1) : (tensor<!tf_type.resource>, tensor<2xi32>, tensor<2x3xf32>, tensor<f32>) -> tensor<f32>

    // final_op = "Div"
    llvm::SmallVector<int64_t> indices(group_size.getInt());
    std::iota(indices.begin(), indices.end(), 0);

    auto replica_groups = mlir::DenseIntElementsAttr::get(
        mlir::RankedTensorType::get({1, group_size.getInt()},
                                    rewriter.getI64Type()),
        indices);

    {