scope, OutputsFromTFOutputs(parent_inputs, num_loop_vars), cond_fn,
      body_fn, params->name, &loop_outputs);

  // Update name_map with newly-created ops.
  // TODO(skyewm): right now BuildWhileLoop() may alter the graph if it returns
  // a bad status. Once we fix this, we may want to return early instead of
  // executing the following code.

}

static void RenameFile(const TF_Filesystem* filesystem, const char* src,
                       const char* dst, TF_Status* status) {
  // If target is a directory return TF_FAILED_PRECONDITION.
  // Target might be missing, so don't error in that case.
  struct stat st;
  if (stat(dst, &st) != 0) {
    if (errno != ENOENT) {
      TF_SetStatusFromIOError(status, errno, dst);
      return;
    }

        // parallel device.
        device_(device),
        executor_(
            TFE_NewExecutor(is_async, /*enable_streaming_enqueue=*/true,
                            /*in_flight_nodes_limit=*/in_flight_nodes_limit)),
        op_(nullptr),
        thread_(tensorflow::Env::Default()->StartThread(
            tensorflow::ThreadOptions(), "parallel_device_execute",
            std::bind(&DeviceThread::Run, this))) {}

        CreatePerDeviceValues(context, components, device_name, status.get());
    variable->AssignAdd(context, combined_value.get(), status.get());
  }

  // Read the variable and verify that each component has the right modified
  // value.
  {
    TensorHandlePtr read = variable->Read(context, status.get());
    std::array<TensorHandlePtr, 2> components;
    ExtractPerDeviceValues(context, read.get(), &components, status.get());

  std::vector<ParallelTensor*> parallel_inputs;
  std::vector<std::unique_ptr<ParallelTensor>> implicitly_broadcast_tensors;
  parallel_inputs.reserve(inputs.size());
  implicitly_broadcast_tensors.reserve(inputs.size());  // not tight
  for (const auto& input : inputs) {
    if (absl::holds_alternative<TFE_TensorHandle*>(input)) {
      if (operation_name == std::string("_EagerConst")) {

    ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
    TFE_DeleteContextOptions(opts);
    TFE_Executor* executor = TFE_NewExecutor(
        /*is_async=*/false, /*enable_streaming_enqueue=*/true,
        /*in_flight_nodes_limit=*/0);
    TFE_ContextSetExecutorForThread(ctx, executor);

    TFE_DeleteContext(ctx);
    TFE_DeleteExecutor(executor);
  }

  {
    TFE_ContextOptions* opts = TFE_NewContextOptions();

  // root directory. Otherwise, we need to add the `<scheme>://` in front of
  // this path.
  //
  // Note that some filesystem might require a different approach here, for
  // example they might require the root directory path to be in a special
  // format, etc. When we get there, we might decide to move this class to
  // `modular_filesystem_test.h` and extend the instantiation to also take as

  }
  TFE_DeleteOp(add_op);

  TF_Tensor* t = TFE_TensorHandleResolve(retval, status);
  if (async) {
    if (forward_input) {
      // Since the input was forwarded, we released the input handle right away
      // and hence expect the input to be forwarded to the return tensor.
      EXPECT_EQ(orig_ptr, TF_TensorData(t));
    } else {
      // In async mode we expect forwarding to work without releasing the input

                              &Deallocator, &deallocator_called);

  TF_Tensor* o = TF_TensorMaybeMove(t);
  ASSERT_TRUE(o == nullptr);  // It is unsafe to move memory TF might not own.
  TF_DeleteTensor(t);
  EXPECT_TRUE(deallocator_called);
}

TEST(CAPI, LibraryLoadFunctions) {
  // TODO(b/73318067): Fix linking for the GPU test generated by the

  status->status = absl::OkStatus();
}

TFE_Executor* TFE_NewExecutor(bool is_async, bool enable_streaming_enqueue,
                              int in_flight_nodes_limit) {
  return new TFE_Executor(is_async, enable_streaming_enqueue,
                          in_flight_nodes_limit);
}

void TFE_DeleteExecutor(TFE_Executor* executor) { delete executor; }

bool TFE_ExecutorIsAsync(TFE_Executor* executor) {