AddSymbolicGradients is failing on recursive implementation - c++

Question

I'm implementing a simple RNN with the Tensorflow C++ APIs.
Currently using the 1.13 from github.
The call to AddSymbolicGradients (last line) fails into a Segmentation fault.
gdb teels me that the error occurs inside SymbolicGradientBuilder::Initialize
auto input_slices = Split(scope, 1, x, window_size);
auto initial_state = Fill(scope, {batch_size, state_size}, 0);
vector<Output> states;
states.reserve(window_size+1);
states.push_back(initial_state);
for (int i=0; i!=window_size; i++) {
auto concat = Concat(scope, InputList(initializer_list<Input>{input_slices[i], states[i]}), 1);
auto new_state = Tanh(scope, Add(scope, MatMul(scope, concat, w_rnn), b_rnn));
states.push_back(new_state);
}
// dense output
auto out = Tanh(scope, Add(scope, MatMul(scope, states[window_size], w_dense), b_dense));
// loss function
auto loss = ReduceMean(scope, Square(scope, Sub(scope, out, y)), {0, 1});
vector<Output> grad_outputs;
TF_CHECK_OK(AddSymbolicGradients(scope, {loss}, {w_rnn, w_dense, b_rnn, b_dense}, &grad_outputs));
Any idea? Thanks
EDIT: gdb where full output
(gdb) where
#0 0x00005555559b45aa in tensorflow::(anonymous namespace)::SymbolicGradientBuilder::Initialize() ()
#1 0x00005555559b6f1c in tensorflow::AddSymbolicGradients(tensorflow::Scope const&, std::vector<tensorflow::Output, std::allocator<tensorflow::O
utput> > const&, std::vector<tensorflow::Output, std::allocator<tensorflow::Output> > const&, std::vector<tensorflow::Output, std::allocator<tens
orflow::Output> > const&, std::vector<tensorflow::Output, std::allocator<tensorflow::Output> >*) ()
#2 0x00005555559b9bb2 in tensorflow::AddSymbolicGradients(tensorflow::Scope const&, std::vector<tensorflow::Output, std::allocator<tensorflow::O
utput> > const&, std::vector<tensorflow::Output, std::allocator<tensorflow::Output> > const&, std::vector<tensorflow::Output, std::allocator<tens
orflow::Output> >*) ()
#3 0x000055555597ce6c in Rnn::train(Dataset, int, int, int) ()
#4 0x00005555558410d6 in main ()