namespace {

std::vector<int64_t> TensorShapeAsVector(const tensorflow::TensorHandle& handle,
                                         tensorflow::Status* status) {
  std::vector<int64_t> shape;
  int rank = -1;
  *status = handle.NumDims(&rank);
  if (!status->ok()) {
    return shape;
  }
  shape.reserve(rank);
  for (int i = 0; i < rank; ++i) {
    int64_t dim;
    *status = handle.Dim(i, &dim);
    if (!status->ok()) {
      return shape;

  TF_EXPECT_OK(status) << "Failed to convert tensorhandle to tensor";
  EXPECT_EQ(revived_tensor->Type(), expected.dtype());
  EXPECT_EQ(revived_tensor->NumElements(), expected.NumElements());
  EXPECT_EQ(revived_tensor->NumDims(), expected.dims());
  for (int i = 0; i < expected.dims(); ++i) {
    EXPECT_EQ(revived_tensor->Dim(i), expected.dim_size(i));
  }

  testing::CheckBufferDataIsEqual(expected.dtype(), expected.NumElements(),

  TensorInterface() {}
  explicit TensorInterface(tensorflow::Tensor t) : tensor_(std::move(t)) {}
  ~TensorInterface() override {}

  void Release() override;

  DataType Type() const override;
  int NumDims() const override;
  int64_t Dim(int dim_index) const override;
  int64_t NumElements() const override;
  size_t ByteSize() const override;
  void* Data() const override;
  bool IsAligned() const override;

// is needed a static_cast can be applied.
class ImmediateExecutionTensorHandle : public AbstractTensorHandle {
 public:
  // Returns number of dimensions.
  virtual Status NumDims(int* num_dims) const = 0;
  // Returns number of elements across all dimensions.
  virtual Status NumElements(int64_t* num_elements) const = 0;
  // Returns size of specified dimension
  //