TF_RETURN_IF_ERROR(resource.Initialize());
  }
  return Status();
}

}  // namespace

Status TFSavedModelAPI::GetFunction(const std::string& function_path,
                                    ConcreteFunction** function) {
  absl::optional<int> node =
      internal::FindNodeAtPath(function_path, bundle_.saved_object_graph());
  if (!node.has_value()) {

  auto cleanup_tensors = absl::MakeCleanup([outs]() {
    for (auto t : outs) {
      t->Unref();
    }
  });
  return Unflatten(outs, concrete_fn.output_signature);
}

StatusOr<Function::ConcreteFunction> Function::GetConcreteFunction(
    TaggedValue value) const {
  if (concrete_fns_.empty()) {
    return tensorflow::errors::FailedPrecondition(
        "No registered ConcreteFunctions.");
  }

  // TODO(bmzhao): Change this to expect TF_OK when loading is implemented.
  // That unblocks writing other tests that require a TF_SavedModel*,
  // like loading a ConcreteFunction. This test at least checks that the
  // C API builds and can be minimally run.
  EXPECT_EQ(TF_GetCode(status), TF_UNIMPLEMENTED);

  TF_DeleteSavedModel(saved_model);
  TF_DeleteStatus(status);

* Introducing `tf.types.experimental.AtomicFunction` as the fastest way to perform TF computations in Python.

    * Can be accessed through `inference_fn` property of `ConcreteFunction`s
    * Does not support gradients.