AssertEqual(t, err, nil)

	defVal, ok := columnType.DefaultValue()
	AssertEqual(t, defVal, "null")
	AssertEqual(t, ok, true)

	columnType2, err := findColumnType(tableName, "active")
	AssertEqual(t, err, nil)

	defVal, ok = columnType2.DefaultValue()
	bv, _ := strconv.ParseBool(defVal)
	AssertEqual(t, bv, false)
	AssertEqual(t, ok, true)

    return op.emitOpError(
        llvm::formatv(kBadArrayAttrLengthMsg, tensorflow::kOutputShardingAttr,
                      op.getNumResults(), output_shardings.size()));

  // Set retvals metadata in proto.
  for (auto output_sharding_and_idx : llvm::enumerate(output_shardings))
    if (failed(SetOpSharding(op, output_sharding_and_idx.value(),
                             tensorflow::kOutputShardingAttr,

static llvm::SmallVector<Value, 4> PrepareValsForJump(
    Location loc, const std::function<Value(int)>& get_val, Block* block,
    OpBuilder* builder) {
  llvm::SmallVector<Value, 4> result;
  int num_vals = block->getNumArguments();
  result.reserve(num_vals);
  for (int i = 0; i < num_vals; ++i) {
    Value val = get_val(i);
    Type expected = block->getArgument(i).getType();
    if (val.getType() != expected) {
      val =

		sz := len(sv.fieldMap)
		ntvMap := reflect.MakeMapWithSize(typeDesc, sz)
		for name, val := range sv.fieldMap {
			refVal, err := val.Ref.ConvertToNative(elemType)
			if err != nil {
				return nil, err
			}
			ntvMap.SetMapIndex(reflect.ValueOf(name), reflect.ValueOf(refVal))
		}
		return ntvMap.Interface(), nil
	}

	if typeDesc.Kind() == reflect.Struct {
		ntvObjPtr := reflect.New(typeDesc)

		panic("variable value type must be a pointer")
	}
	defVal := reflect.ValueOf(val)
	if defVal.Kind() == reflect.Ptr {
		defVal = defVal.Elem()
	}
	if defVal.Type() != ptrVal.Type().Elem() {
		panic(fmt.Sprintf("default type does not match variable type: %v != %v", defVal.Type(), ptrVal.Type().Elem()))
	}
	ptrVal.Elem().Set(defVal)
	return textValue{p}
}

func (v textValue) Set(s string) error {

	ports := make([]*workloadapi.Port, 0, len(svc.Spec.Ports))
	for _, p := range svc.Spec.Ports {
		ports = append(ports, &workloadapi.Port{
			ServicePort: uint32(p.Port),
			TargetPort:  uint32(p.TargetPort.IntVal),
		})
	}

	addresses, err := slices.MapErr(getVIPs(svc), a.toNetworkAddress)
	if err != nil {
		log.Warnf("fail to parse service %v: %v", config.NamespacedName(svc), err)
		return nil
	}

		case int64:
			ii = intstr.FromInt32(int32(item))
		case string:
			ii = intstr.FromString(item)
		default:
			ii = intstr.FromInt32(0)
		}
		intVal, err := intstr.GetScaledValueFromIntOrPercent(&ii, 100, false)
		if err != nil || intVal == 0 {
			return true
		}
		return false
	}
	if spec["maxUnavailable"] != nil && spec["minAvailable"] != nil {

			return
		}

		p.expectKeyword("i")
		reval := constant.MakeFromLiteral(re, token.FLOAT, 0)
		if reval == nil {
			p.error("could not parse real component of complex literal")
		}
		imval := constant.MakeFromLiteral(im+"i", token.IMAG, 0)
		if imval == nil {
			p.error("could not parse imag component of complex literal")
		}
		val = constant.BinaryOp(reval, token.ADD, imval)
		typ = types.Typ[types.UntypedComplex]

  auto a = ops::_Arg(scope.WithOpName("A"), DT_INT32, 0);
  auto b = ops::_Arg(scope.WithOpName("B"), DT_INT32, 1);
  auto c = ops::Add(scope.WithOpName("C"), a, b);
  auto d = ops::_Retval(scope.WithOpName("D"), c, 0);
  TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));
  return graph;
}

absl::StatusOr<FunctionDef> SampleFuntionAddXY(const std::string& name) {

    func.func @main(%arg0: tensor<3x3x10xbf16>, %arg1: tensor<3xi32>) -> tensor<1x?x4xbf16> attributes {allow_soft_placement = false, tf.entry_function = {control_outputs = "", inputs = "_arg0,_arg1,_arg2", outputs = "_retval0"}} {
      %cst = "tf.Const"() {value = dense<[1, -1, 4]> : tensor<3xi32>} : () -> tensor<3xi32>