for (const Node* n : fn_body->graph.nodes()) {
    stack_traces[n->name()] = n->GetStackTrace();
  }

  TF_Function* tf_function = new TF_Function();
  tf_function->record = new tensorflow::FunctionRecord(
      std::move(fdef), std::move(stack_traces), false);

  return tf_function;
}

TF_Function* TF_GraphToFunction(const TF_Graph* fn_body, const char* fn_name,

}

char* TF_FunctionDebugString(TF_Function* func, size_t* len) {
  const auto& debug_str = DebugString(func->record->fdef());
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

// On success, returns a set of TF_Function instances from `text_proto` of
// GraphDef type. These functions must be deleted by calling TF_DeleteFunction.
//

                       /*tfrt*/ ::testing::Values(false),
                       /*use_function*/ ::testing::Values(true, false)));
#else
INSTANTIATE_TEST_SUITE_P(
    UnifiedCAPI, CppGradients,
    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),
                       /*use_function*/ ::testing::Values(true, false)));
#endif
}  // namespace
}  // namespace internal

}

// Calls the passed-in backward function.
// op_type is the op's name provided in RecordOperation.
Status TapeVSpace::CallBackwardFunction(
    const string& op_type, GradientFunction* gradient_function,
    const std::vector<int64_t>& unneeded_gradients,
    gtl::ArraySlice<AbstractTensorHandle*> output_gradients,
    absl::Span<AbstractTensorHandle*> result) const {
  if (gradient_function == nullptr) {

    default:
      return tensorflow::errors::InvalidArgument("Unsupported Type Codes: ",
                                                 dtype.code);
  }
}

// Wraps the deleter function of DLManagedTensor to match the function signature
// TFE_NewTensorHandleFromDeviceMemory.
void DeallocatorWrapperFunc(void* data, size_t len, void* dlmt_vptr) {
  TFE_CallDLManagedTensorDeleter(dlmt_vptr);
}

// once (i.e., on the main function side) in running distributed functions.
// This test creates a cluster with two workers, create a variable on the
// second worker, and run a distributed function (VariableAddFunction) whose ops
// span the local and remote workers. If the graph optimization pass is executed
// on both the main function side and the component function side, an error will

      handle_methods, status));
}

// For TFE_CustomDevice::copy_tensor_to_device in the parallel device
// registration.
//
// Since this function is used to satisfy the TFE_CustomDevice C API,
// device_info is passed in using a C-style generic. It must always be a
// ParallelDevice.
TFE_TensorHandle* CopyToParallelDevice(TFE_Context* context,

                               TF_Status* status) {
  tensorflow::FunctionDef function_def;
  if (!function_def.ParseFromArray(serialized_function_def, size)) {
    status->status =
        tensorflow::errors::InvalidArgument("Invalid FunctionDef proto");
    return;
  }

  AnnotateEagerRuntimeConstructionContext(function_def);

                               TF_Buffer* buf, TF_Status* status) {
  auto* function_def = tensorflow::unwrap(ctx)->FindFunctionDef(function_name);
  if (function_def == nullptr) {
    status->status = tensorflow::errors::NotFound(
        "Unable to find FunctionDef with name: ", function_name);
    return;
  }
  string str = function_def->SerializeAsString();

  return OkStatus();
}

// Validates the operations supplied by the plugin.
//
// Uses the 4 simpler `ValidateHelper(const TF_...*)` to validate each
// individual function table and then checks that the function table for a
// specific file type exists if the plugin offers support for creating that
// type of files.
static Status ValidateOperations(const TF_FilesystemPluginOps* ops) {