/*expected_shape*/ make_shape({1, 1, 6}));
  TFE_DeleteOp(fill_op);
  fill_op = nullptr;

  TFE_DeleteTensorHandle(scalar);
  TF_DeleteTensor(scalarTensor);
  TF_DeleteTensor(tensor_1X1X6);
  TF_DeleteTensor(tensor_1X6);
}

TEST(CAPI_EXPERIMENTAL, LibraryPluggableDeviceLoadFunctions) {
  // TODO(penpornk): Enable this test on Windows.
#if !defined(PLATFORM_WINDOWS)

                 1, nullptr, 0, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ(3, *(static_cast<int32*>(TF_TensorData(fetchValues1[0]))));
  TF_DeleteTensor(feedValues1[0]);
  TF_DeleteTensor(fetchValues1[0]);

  // Feed B and fetch (A + 2) + B.
  TF_Output feeds2[] = {TF_Output{b, 0}};
  TF_Output fetches2[] = {TF_Output{plusB, 0}};
  TF_Tensor* feedValues2[] = {Int32Tensor(4)};

// `tensor_id`, associated with `session`. There must be a graph node named
// "fifo_queue_dequeue_<tensor_id>", to be executed by this API call.

// Caller must call TF_DeleteTensor() over the returned tensor. If the queue is
// empty, this call is blocked.
//
// Tensors are enqueued via the corresponding TF enqueue op.
// TODO(hongm): Add support for `timeout_ms`.

    int max_values, TF_Status* status);

// Gets the TF_Tensor valued attribute of `attr_name` of `oper`.
//
// Allocates a new TF_Tensor which the caller is expected to take
// ownership of (and can deallocate using TF_DeleteTensor).
TF_CAPI_EXPORT extern void TF_OperationGetAttrTensor(TF_Operation* oper,
                                                     const char* attr_name,
                                                     TF_Tensor** value,

                   "`inputs` must have a single output. Node split3 has "
                   "3 outputs. Encountered while creating function 'MyFunc'"),
            string(TF_Message(s_)));

  TF_DeleteTensor(tensor_123);
}

TEST_F(CApiFunctionTest, FunctionWithWhileLoop) {
  // Inputs to the while loop and the function as a whole
  TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");