mlir::MLIRContext context;
  RegisterDialects(context);
  mlir::Builder b(&context);

  // Create the sample tensor to convert.
  Tensor tensor(DT_STRING, TensorShape({1, 2, 2, 1}));
  EXPECT_EQ(4, tensor.NumElements());
  auto Tt = tensor.flat<tstring>();
  Tt.setValues({"one", "two", "three", "four"});
  auto value_or_status = ConvertTensor(tensor, &b);
  ASSERT_TRUE(value_or_status.ok());
  auto attr = value_or_status.value();

func writeTableSec(ctxt *ld.Link, fns []*wasmFunc) {
	sizeOffset := writeSecHeader(ctxt, sectionTable)

	numElements := uint64(funcValueOffset + len(fns))
	writeUleb128(ctxt.Out, 1)           // number of tables
	ctxt.Out.WriteByte(0x70)            // type: anyfunc
	ctxt.Out.WriteByte(0x00)            // no max
	writeUleb128(ctxt.Out, numElements) // min

	writeSecSize(ctxt, sizeOffset)
}

	}
	return &schema.GroupVersionKind{Group: gv.Group, Version: gv.Version, Kind: findKind.ObjectKind}, nil
}

// TestObjectFuzzer can randomly populate all the above objects.
var TestObjectFuzzer = fuzz.New().NilChance(.5).NumElements(1, 100).Funcs(
	func(j *runtimetesting.MyWeirdCustomEmbeddedVersionKindField, c fuzz.Continue) {
		c.FuzzNoCustom(j)
		j.APIVersion = ""
		j.ObjectKind = ""
	},
)

}

func getObjectMetaAndOwnerReferences() (myAPIObject2 MyAPIObject2, metaOwnerReferences []metav1.OwnerReference) {
	scheme := runtime.NewScheme()
	codecs := serializer.NewCodecFactory(scheme)
	fuzz.New().NilChance(.5).NumElements(1, 5).Funcs(metafuzzer.Funcs(codecs)...).MaxDepth(10).Fuzz(&myAPIObject2)
	references := myAPIObject2.ObjectMeta.OwnerReferences
	// This is necessary for the test to pass because the getter will return a
	// non-nil slice.

  strings::StrAppend(&ret, "}");
  return ret;
}

string PrintTensor(const TensorProto& tensor_proto) {
  Tensor t(tensor_proto.dtype());
  CHECK(t.FromProto(tensor_proto));
  const int64_t num_elts = t.NumElements();
  switch (t.dtype()) {
    case DT_FLOAT:
      return PrintArray(num_elts, t.flat<float>().data());
    case DT_DOUBLE:
      return PrintArray(num_elts, t.flat<double>().data());
    case DT_INT32:

}

type CertTrustListInfo struct {
	// Not implemented
}

type CertSimpleChain struct {
	Size                       uint32
	TrustStatus                CertTrustStatus
	NumElements                uint32
	Elements                   **CertChainElement
	TrustListInfo              *CertTrustListInfo
	HasRevocationFreshnessTime uint32
	RevocationFreshnessTime    uint32
}

	depth := 0
	maxDepth := 3
	nilChance := func(depth int) float64 {
		return math.Pow(0.9, math.Max(0.0, float64(maxDepth-depth)))
	}
	updateFuzzer := func(depth int) {
		f.NilChance(nilChance(depth))
		f.NumElements(0, max(0, maxDepth-depth))
	}
	updateFuzzer(depth)
	enter := func(o interface{}, recursive bool, c fuzz.Continue) {
		if recursive {
			depth++
			updateFuzzer(depth)
		}

		invisible++

template <typename Gradient, typename BackwardFunction, typename TapeTensor>
class VSpace {
 public:
  virtual ~VSpace() {}

  // Returns the number of elements in the gradient tensor.
  virtual int64_t NumElements(Gradient* tensor) const = 0;

  // Consumes references to the tensors in the gradient_tensors list and returns
  // a tensor with the result.
  virtual Gradient* AggregateGradients(

	codecs = runtimeserializer.NewCodecFactory(scheme)
}

func fuzzAPIGroups(atLeastNumGroups, maxNumGroups int, seed int64) apidiscoveryv2.APIGroupDiscoveryList {
	fuzzer := fuzz.NewWithSeed(seed)
	fuzzer.NumElements(atLeastNumGroups, maxNumGroups)
	fuzzer.NilChance(0)
	fuzzer.Funcs(func(o *apidiscoveryv2.APIGroupDiscovery, c fuzz.Continue) {
		c.FuzzNoCustom(o)

		// The ResourceManager will just not serve the group if its versions

    std::vector<Tensor> grad_result;
    TF_EXPECT_OK(
        session.Run({{x, {-3.0f, 0.0f, 3.0f}}}, grad_outputs, &grad_result));
    EXPECT_EQ(grad_result.size(), 1);
    EXPECT_EQ(grad_result[0].NumElements(), 3);
    EXPECT_EQ(grad_result[0].flat<float>()(0), 0.0f);
    EXPECT_EQ(grad_result[0].flat<float>()(1), 0.0f);
    EXPECT_EQ(grad_result[0].flat<float>()(2), 0.0f);
  }
}