import kotlin.reflect.KClass
import kotlin.reflect.KType

internal fun SmartPrinter.printTypeWithShortNames(type: KType, shouldRenderFqName: (KType) -> Boolean = { false }) {
    fun typeConversion(type: KType): String {
        val nullableSuffix = if (type.isMarkedNullable) "?" else ""
        val simpleName = if (shouldRenderFqName(type)) {
            type.qualifiedName
        } else {
            type.simpleName
        }

  Tensor tensor = Tensor::FromBuffer(
      /*dtype=*/dtype, /*shape=*/shape,
      /*data=*/value.data(),
      /*len=*/value.size() * sizeof(typename TypeParam::type),
      /*deleter=*/[](void*, size_t) {}, &status);
  ASSERT_TRUE(status.ok()) << status.message();

  EXPECT_EQ(tensor.dims(), 1);
  EXPECT_EQ(tensor.dtype(), dtype);
  absl::Span<const typename TypeParam::type> tensor_view(

  owner->reference.Unref();
  delete owner;
}

// Converts TF_DATAType to DLPack data type.
DLDataType GetDlDataType(TF_DataType data_type, TF_Status* status) {
  DLDataType dtype;
  dtype.lanes = 1;
  dtype.bits = TF_DataTypeSize(data_type) * 8;
  switch (data_type) {
    case TF_DataType::TF_BOOL:
      dtype.code = DLDataTypeCode::kDLBool;
      break;
    case TF_DataType::TF_HALF:
    case TF_DataType::TF_FLOAT:

    override fun getTypeConfiguredByLambda(type: KType): KType? = if (isConfigureLambdaType(type)) type.arguments[0].type else null
    override fun isConfigureLambdaForType(configuredType: KType, maybeLambdaType: KType): Boolean = isConfigureLambdaType(maybeLambdaType, configuredType)
    override fun produceValueCaptor(lambdaType: KType): ConfigureLambdaHandler.ValueCaptor {
        lateinit var value: Any

// 1. does not cause an error
// 2. preserves dtype and shape.
TEST_P(SavedVariableLoadingTest, LoadSavedVariableSuccessful) {
  auto& test_params = GetParam();
  DataType dtype = std::get<0>(test_params);
  TensorShape shape(std::get<1>(test_params));

  SavedVariable saved_variable;
  saved_variable.set_dtype(dtype);
  shape.AsProto(saved_variable.mutable_shape());

  std::unique_ptr<Variable> var;

      T41, T42, T43, T44, T45, T46, T47, T48, T49> type;
};

namespace internal {

# define GTEST_TEMPLATE_ template <typename T> class

// The template "selector" struct TemplateSel<Tmpl> is used to
// represent Tmpl, which must be a class template with one type
// parameter, as a type.  TemplateSel<Tmpl>::Bind<T>::type is defined
// as the type Tmpl<T>.  This allows us to actually instantiate the

      T41, T42, T43, T44, T45, T46, T47, T48, T49> type;
};

namespace internal {

# define GTEST_TEMPLATE_ template <typename T> class

// The template "selector" struct TemplateSel<Tmpl> is used to
// represent Tmpl, which must be a class template with one type
// parameter, as a type.  TemplateSel<Tmpl>::Bind<T>::type is defined
// as the type Tmpl<T>.  This allows us to actually instantiate the

                                           DataType dtype, TensorShape shape,
                                           const char* raw_device_name,
                                           ImmediateTensorHandlePtr* handle) {
  ImmediateOpPtr varhandle_op(ctx->CreateOperation());

  TF_RETURN_IF_ERROR(varhandle_op->Reset("VarHandleOp", raw_device_name));
  TF_RETURN_IF_ERROR(varhandle_op->SetAttrType("dtype", dtype));

	_type{"int16", 16, true},
	_type{"uint16", 16, false},
	_type{"int32", 32, true},
	_type{"uint32", 32, false},
	_type{"int64", 64, true},
	_type{"uint64", 64, false},
	_type{"int", 8 * uint(unsafe.Sizeof(int(0))), true},
	_type{"uint", 8 * uint(unsafe.Sizeof(uint(0))), false},
	_type{"uintptr", 8 * uint(unsafe.Sizeof((*byte)(nil))), false},
}

type binop struct {
	name string
	eval func(x, y *big.Int) *big.Int
}

				{
					Type:   cType,
					Status: v1.ConditionTrue,
				},
			},
		},
		{
			description: "replace",
			conditions: []v1.PodCondition{
				{
					Type:   cType,
					Status: v1.ConditionTrue,
				},
			},
			condition: v1.PodCondition{
				Type:   cType,
				Status: v1.ConditionFalse,
			},
			wantConditions: []v1.PodCondition{
				{
					Type:   cType,