mlir::LogicalResult LSTMOp::verify() {
  LSTMOp op = *this;
  auto operands = op.GetStatefulOperands();
  if (operands.size() != 2 || operands[0] != 18 || operands[1] != 19) {
    return op.emitOpError("LSTMOp expected to have two stateful operands");
  }

  const auto input_type = op.getInput().getType().cast<ShapedType>();
  // Since TFLite runtime generally supports dynamic shape/rank, if `input_type`

  }

  std::string GetName(OpOrVal op_or_val) override {
    DCHECK(false) << "Unimplemented";
    return "";
  }

  const FunctionLibraryDefinition& flib_;
};

// Stateful helper class to import a TensorFlow model into an MLIR Module.
//
// This is the base class that contains common utilities shared between the
// GraphDef importer and SavedModel importer.
//

// This is used to represent the type of "ref tensors" or tensors that are
// used as variables to track state.
def TFL_StatefulTensor : TypeAlias<AnyTensor, "stateful tensor">;

//===----------------------------------------------------------------------===//
// Rank/Shape helpers.
//===----------------------------------------------------------------------===//

  // mapping.
  void InitializeNamesFromAttribute(FuncOp fn, bool* has_input_attr);

  // Determines if the specified operation op's operand at operand_index
  // is marked as a stateful operand.
  bool IsStatefulOperand(mlir::Operation* op, int operand_index);

  // Returns a unique name for `val`.
  std::string UniqueName(mlir::Value val);

  // `potential_refined_type`. Return true if the type was changed.
  bool RefineResultType(Operation* op, Value result,
                        Type potential_refined_type);

  // Infers the shape from a (Stateful)PartitionedCall operation by looking up
  // the called function and propagating the return type.
  bool InferShapeForCall(CallOpInterface call_op);

  bool InferShapeForCast(Operation* op);

					t.Errorf("expected redirect %v, got %v", tt.redirect, h.RequireTls)
				}
				if tt.expectStatefulSession && h.TypedPerFilterConfig[util.StatefulSessionFilter] == nil {
					t.Errorf("expected per filter config for stateful session filter, but not found")
				}
			}

			if !maps.EqualFunc(tt.expectedVirtualHosts, vh, slices.Equal) {
				t.Errorf("got unexpected virtual hosts. Expected: %v, Got: %v", tt.expectedVirtualHosts, vh)

	if err != nil {
		return err
	}
	for _, claim := range claims {
		claimPodName := getClaimPodName(set, claim)
		if claimPodName == "" {
			continue // Skip claims not related to a stateful set pod.
		}
		if _, found := currentPods[claimPodName]; found {
			continue // Skip claims which still have a current pod.
		}
		for _, refs := range claim.GetOwnerReferences() {

	}
}

func TestPrintStatefulSet(t *testing.T) {
	tests := []struct {
		statefulSet apps.StatefulSet
		options     printers.GenerateOptions
		expected    []metav1.TableRow
	}{
		// Basic stateful set; no generate options.
		{
			statefulSet: apps.StatefulSet{
				ObjectMeta: metav1.ObjectMeta{
					Name:              "test1",
					CreationTimestamp: metav1.Time{Time: time.Now().Add(1.9e9)},
				},