input_handle: the input list
tensor: the gathered result
num_elements: optional. If not -1, the number of elements in the list.
  }];

  let arguments = (ins
    TF_VariantTensor:$input_handle,
    TF_Int32Tensor:$element_shape,

    DefaultValuedOptionalAttr<I64Attr, "-1">:$num_elements
  );

  let results = (outs
    TF_Tensor:$tensor
  );

	if err == nil || !errors.IsAlreadyExists(err) {
		t.Error("Expected already exists error")
	}
}

func TestNewCreateOptionsFromUpdateOptions(t *testing.T) {
	f := fuzz.New().NilChance(0.0).NumElements(1, 1)

	// The goal here is to trigger when any changes are made to either
	// CreateOptions or UpdateOptions types, so we can update the converter.
	for i := 0; i < 20; i++ {
		in := &metav1.UpdateOptions{}

    status->status = result;
    return;
  }

  // Store results in c_outputs[]
  for (int i = 0; i < noutputs; ++i) {
    const Tensor& src = outputs[i];
    if (!src.IsInitialized() || src.NumElements() == 0) {
      c_outputs[i] =
          EmptyTensor(static_cast<TF_DataType>(src.dtype()), src.shape());
      continue;
    }
    c_outputs[i] = TF_TensorFromTensor(src, &status->status);

	seed := int64(5577006791947779410)
	f := fuzzer.FuzzerFor(fuzzerFuncs, rand.NewSource(seed), codecs)
	// fuzz internal types
	schema := &apiextensions.JSONSchemaProps{}
	f.NilChance(0).NumElements(10, 10).MaxDepth(10).Fuzz(schema)

	b.ReportAllocs()
	b.ResetTimer()
	for i := 0; i < b.N; i++ {
		if SchemaHas(schema, func(_ *apiextensions.JSONSchemaProps) bool {
			return false
		}) {