1 files changed, 32 insertions, 0 deletions
diff --git a/runtimes/tests/neural_networks_test/generated/models/rnn_state.model.cpp b/runtimes/tests/neural_networks_test/generated/models/rnn_state.model.cpp
new file mode 100644
index 000000000..72fcb6703
--- /dev/null
+++ b/runtimes/tests/neural_networks_test/generated/models/rnn_state.model.cpp
@@ -0,0 +1,32 @@
+// Generated file (from: rnn_state.mod.py). Do not edit
+void CreateModel(Model *model) {
+  OperandType type5(Type::INT32, {});
+  OperandType type2(Type::TENSOR_FLOAT32, {16, 16});
+  OperandType type1(Type::TENSOR_FLOAT32, {16, 8});
+  OperandType type3(Type::TENSOR_FLOAT32, {16});
+  OperandType type4(Type::TENSOR_FLOAT32, {2, 16});
+  OperandType type0(Type::TENSOR_FLOAT32, {2, 8});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto weights = model->addOperand(&type1);
+  auto recurrent_weights = model->addOperand(&type2);
+  auto bias = model->addOperand(&type3);
+  auto hidden_state_in = model->addOperand(&type4);
+  auto activation_param = model->addOperand(&type5);
+  auto hidden_state_out = model->addOperand(&type4);
+  auto output = model->addOperand(&type4);
+  // Phase 2, operations
+  static int32_t activation_param_init[] = {1};
+  model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
+  model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, weights, recurrent_weights, bias, hidden_state_in},
+    {hidden_state_out, output});
+  assert(model->isValid());
+}
+
+bool is_ignored(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}