// FuzzerFor can randomly populate api objects that are destined for version.
func FuzzerFor(funcs FuzzerFuncs, src rand.Source, codecs runtimeserializer.CodecFactory) *fuzz.Fuzzer {
	f := fuzz.New().NilChance(.5).NumElements(0, 1)
	if src != nil {
		f.RandSource(src)
	}
	f.Funcs(funcs(codecs)...)
	return f
}

// MergeFuzzerFuncs will merge the given funcLists, overriding early funcs with later ones if there first

Graph* BM_ScalarSummaryOp(TensorShape shape, std::string tag, float value) {
  Graph* g = new Graph(OpRegistry::Global());
  Tensor tags(DT_STRING, shape);
  Tensor values(DT_FLOAT, shape);
  for (int i = 0; i < tags.NumElements(); ++i) {
    tags.flat<tstring>()(i) = tag;
    values.flat<float>()(i) = value;
  }
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("dummy"), "ScalarSummary")
                  .Input(test::graph::Constant(g, tags))

               ->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

  virtual int NumDims() const = 0;
  // Returns size of specified dimension
  virtual int64_t Dim(int dim_index) const = 0;
  // Returns number of elements across all dimensions.
  virtual int64_t NumElements() const = 0;
  // Return size in bytes of the Tensor
  virtual size_t ByteSize() const = 0;
  // Returns a pointer to tensor data
  virtual void* Data() const = 0;

  // Returns if the tensor is aligned

		if tc.expected != actual {
			t.Errorf("%s: expected %v, got %v", tc.name, tc.expected, actual)
		}
	}
}

func TestNewMetadataFields(t *testing.T) {
	f := fuzz.New().NilChance(0.0).NumElements(1, 1)
	for i := 0; i < 100; i++ {
		objMeta := metav1.ObjectMeta{}
		f.Fuzz(&objMeta)
		objMeta.Name = ""
		objMeta.GenerateName = ""
		objMeta.Namespace = ""
		objMeta.SelfLink = ""

	}

	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;