typedef typename RealType<T>::type RealT;
      Tensor t(DataTypeToEnum<RealT>::v(), shape);
      if (t.NumElements() != static_cast<int64_t>(v.size())) {
        status = absl::InvalidArgumentError(absl::StrCat(
            "Cannot construct a tensor with ", t.NumElements(),
            " from an initializer list with ", v.size(), " elements"));
        return;
      }

                                              StatusCallback done) {
  tsl::profiler::TraceMe traceme("PjRtDeviceContext::CopyDeviceTensorToCPU");
  if (device_tensor->NumElements() == 0) {
    VLOG(2) << "CopyDeviceTensorToCPU empty tensor";
    done(absl::OkStatus());
    return;
  }
  auto literal = std::make_unique<xla::MutableBorrowingLiteral>();

	metav1beta1 "k8s.io/apimachinery/pkg/apis/meta/v1beta1"

	"github.com/google/go-cmp/cmp"
	fuzz "github.com/google/gofuzz"
)

func TestAsPartialObjectMetadata(t *testing.T) {
	f := fuzz.New().NilChance(.5).NumElements(0, 1).RandSource(rand.NewSource(1))

	for i := 0; i < 100; i++ {
		m := &metav1.ObjectMeta{}
		f.Fuzz(m)
		partial := AsPartialObjectMetadata(m)
		if !reflect.DeepEqual(&partial.ObjectMeta, m) {

               ->pjrt_context->CopyDeviceTensorToCPUSync(&tensor, "", device,
                                                         &dest_cpu_tensor);
  for (int i = 0; i < tensor.NumElements(); ++i) {
    EXPECT_EQ(dest_cpu_tensor.flat<int32_t>().data()[i], data[i]);
  }
}

}  // namespace

	}

	simpleChains := unsafe.Slice(simpleChain, count)
	lastChain := simpleChains[count-1]
	elements := unsafe.Slice(lastChain.Elements, lastChain.NumElements)
	for i := 0; i < int(lastChain.NumElements); i++ {
		// Copy the buf, since ParseCertificate does not create its own copy.
		cert := elements[i].CertContext
		encodedCert := unsafe.Slice(cert.EncodedCert, cert.Length)

  ClientSession session(scope_test_);
  std::vector<Tensor> grad_result;
  TF_EXPECT_OK(session.Run({{x, {3.0f}}}, grad_outputs, &grad_result));
  EXPECT_EQ(grad_result.size(), 1);
  EXPECT_EQ(grad_result[0].NumElements(), 1);
  EXPECT_EQ(grad_result[0].flat<float>()(0), 17502.0f);
}

TEST_F(GradientsTest, MultiOutputNodeDependentOutputs) {
  auto x = Placeholder(scope_test_, DT_FLOAT);
  auto y0 = Square(scope_test_, x);

		if invalidLength(offset, t.length, len(bytes)) {
			err = SyntaxError{"truncated sequence"}
			return
		}
		offset += t.length
		numElements++
	}
	ret = reflect.MakeSlice(sliceType, numElements, numElements)
	params := fieldParameters{}
	offset := 0
	for i := 0; i < numElements; i++ {
		offset, err = parseField(ret.Index(i), bytes, offset, params)
		if err != nil {
			return
		}
	}
	return
}

#include "tensorflow/compiler/tf2xla/shape_util.h"
#include "xla/shape_util.h"

namespace tensorflow {

/*static*/ XlaTensor* XlaTensor::FromTensor(const Tensor* tensor) {
  if (tensor->NumElements() == 0) {
    return nullptr;
  }
  XlaTensor* xla_tensor =
      FromOpaquePointer(const_cast<char*>(tensor->tensor_data().data()));
  return xla_tensor;
}

  ~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;
  bool CanMove() const override;
  std::string SummarizeValue() const override;

};

std::vector<int64_t> IntTensorAsVector(const Tensor& t) {
  DCHECK(t.dtype() == DT_INT32 || t.dtype() == DT_INT64);
  std::vector<int64_t> result;
  result.reserve(t.NumElements());
  for (int i = 0; i < t.NumElements(); i++) {
    int64_t element = t.dtype() == DT_INT32
                          ? static_cast<int64_t>(t.flat<int32>()(i))
                          : t.flat<int64_t>()(i);
    result.push_back(element);