};

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);

		{Group: "flowcontrol.apiserver.k8s.io", Version: "v1", Kind: "PriorityLevelConfigurationList"}:             {},
	}

	f := fuzz.New().NilChance(.5).NumElements(1, 1).RandSource(rand.NewSource(1))
	f.Funcs(
		func(s *runtime.RawExtension, c fuzz.Continue) {},
		func(s *metav1.LabelSelector, c fuzz.Continue) {
			c.FuzzNoCustom(s)

    // a reasonable amount apart
    T cur = 0;
    for (size_t i = 0; i < tensor->NumElements(); i++) {
      tensor_flat(i) = cur;
      cur += 5e-2;
    }
    // Fischer-Yates shuffle the array
    for (size_t i = tensor->NumElements() - 1; i >= 1; i--) {
      // j <- random integer 0 <= j <= i
      size_t j = random::New64() % (i + 1);
      // swap values at i, j

 public:
  explicit TapeVSpace(AbstractContext* ctx) : ctx_(ctx) {}
  ~TapeVSpace() override {}

  // Returns the number of elements in the gradient tensor.
  int64_t NumElements(AbstractTensorHandle* tensor) const override;

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

  result->clear();
  result->reserve(variable_indices.size());
  for (int var_idx : variable_indices) {
    Var* variable = nullptr;
    if (inputs[var_idx]->NumElements() == 0) {
      return errors::InvalidArgument("Empty resource tensor passed  at index ",
                                     var_idx,
                                     " to GetVariableInfosFromInputs.");
    }

}

// -----

func.func @testSetStaticDimensionBounds(%arg0: tensor<?x?xi32>, %arg1: tensor<4xi32>) -> tensor<?x?xi32> {
  // expected-error @below {{'tf.SetStaticDimensionBounds' op static shape must have num_elements == rank of input tensor}}
  %dyn_arg0 = "tf.SetStaticDimensionBounds" (%arg0, %arg1) :(tensor<?x?xi32>, tensor<4xi32>) -> tensor<?x?xi32>
  func.return %dyn_arg0 : tensor<?x?xi32>
}

// -----

		}
	}
}

func TestResetObjectMetaForStatus(t *testing.T) {
	meta := &ObjectMeta{}
	existingMeta := &ObjectMeta{}

	// fuzz the existingMeta to set every field, no nils
	f := fuzz.New().NilChance(0).NumElements(1, 1).MaxDepth(10)
	f.Fuzz(existingMeta)
	ResetObjectMetaForStatus(meta, existingMeta)

	// not all fields are stomped during the reset.  These fields should not have been set. False

      "SavedModel checkpoint does not contain object graph.");
  if (object_graph_tensor.dtype() != DT_STRING ||
      object_graph_tensor.dims() != 0 ||
      object_graph_tensor.NumElements() != 1) {
    return absl::Status(
        absl::StatusCode::kFailedPrecondition,
        "SavedModel checkpoint object graph was not the correct type.");
  }

    t.reset(imm_t->Resolve(&status));
    if (!status.ok()) {
      return status;
    }
  }
  if (data.size() != t->NumElements()) {
    return tensorflow::errors::InvalidArgument(absl::StrCat(
        "Mismatched number of elements: \n", "Expected: ", data.size(), "\n",
        "Actual: ", t->NumElements(), "\n"));
  }
  memcpy(data.data(), t->Data(), t->ByteSize());
  return ::tensorflow::OkStatus();
}

  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();