var _ = &Pointer[int]{}

// A Pointer is an atomic pointer of type *T. The zero value is a nil *T.
type Pointer[T any] struct {
	// Mention *T in a field to disallow conversion between Pointer types.
	// See go.dev/issue/56603 for more details.
	// Use *T, not T, to avoid spurious recursive type definition errors.
	_ [0]*T

	_ noCopy
	v unsafe.Pointer
}

    return mlir::TF::ShapeAttr::get(type.getContext(), tensor_type.getShape());
  }

  // If type is not a RankedTensor or UnrankedTensor, it must be a scalar.
  // Empty TensorShape indicates a scalar.
  return mlir::TF::ShapeAttr::get(type.getContext(), ArrayRef<int64_t>());
}

absl::StatusOr<TensorSpecProto> ConvertTypeToTensorSpecProto(
    const mlir::Type& type) {
  DataType dtype;

func (p *Package) recordTypedefs(dtype dwarf.Type, pos token.Pos) {
	p.recordTypedefs1(dtype, pos, map[dwarf.Type]bool{})
}

func (p *Package) recordTypedefs1(dtype dwarf.Type, pos token.Pos, visited map[dwarf.Type]bool) {
	if dtype == nil {
		return
	}
	if visited[dtype] {
		return
	}
	visited[dtype] = true
	switch dt := dtype.(type) {
	case *dwarf.TypedefType:

  ASSERT_TRUE(status.ok()) << status.message();

  TF_DataType dtype = TypeParam::kDType;
  // This is our 1D tensor of varying dtype.
  std::vector<typename TypeParam::type> value = {42, 100, 0, 1, 4, 29};
  // Shape is Rank 2 vector with shape 2 x 3.
  std::vector<int64_t> shape({2, 3});

  Tensor original_tensor = Tensor::FromBuffer(
      /*dtype=*/dtype, /*shape=*/shape,
      /*data=*/value.data(),

  // CHECK: tf.ReadVariableOp
  // CHECK-SAME: dtype = !corert.quint8
  %0 = "tf.ReadVariableOp"(%arg0) {_output_shapes = ["tfshape$dim { size: 1 } dim { size: 3 }"], device = "/device:CPU:0", dtype = !tf_type.quint8} : (tensor<!tf_type.resource<tensor<1x3x!tf_type.quint8>>>) -> tensor<1x3x!tf_type.quint8>

  // CHECK: tf.ReadVariableOp
  // CHECK-SAME: dtype = !corert.quint16

  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(

 *
 */
public class AccessResultDataImpl<IDTYPE> implements AccessResultData<IDTYPE> {
    protected IDTYPE id;

    protected String transformerName;

    protected byte[] data;

    protected String encoding;

    /*
     * (non-Javadoc)
     *
     * @see org.codelibs.fess.crawler.entity.AccessResultData#getId()
     */
    @Override
    public IDTYPE getId() {
        return id;
    }

    /*

  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:

// 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