testing::CreateTensorHandle(ctx, DT_FLOAT, {2, 4}, value)) {}
};

FunctionDef FuncDefWithNumInputsOutputs(int num_inputs, int num_outputs) {
  FunctionDef func;
  OpDef* signature = func.mutable_signature();
  for (int i = 0; i < num_inputs; ++i) {
    signature->add_input_arg();
  }
  for (int i = 0; i < num_outputs; ++i) {
    signature->add_output_arg();
  }
  return func;
}

  }

  auto stateful_string = mlir::TF::TensorFlowDialect::GetStatefulAttrName();
  if (auto attr = function->getAttrOfType<mlir::UnitAttr>(stateful_string)) {
    func_def.mutable_signature()->set_is_stateful(true);
  }

  // Ignore the gradient and is_stateful attribute on the function as they have
  // been handled above. Ignore the entry func attribute as it is an MLIR

  }

  auto stateful_string = mlir::TF::TensorFlowDialect::GetStatefulAttrName();
  if (auto attr = function->getAttrOfType<mlir::UnitAttr>(stateful_string)) {
    func_def.mutable_signature()->set_is_stateful(true);
  }

  // Ignore the gradient and is_stateful attribute on the function as they have
  // been handled above. Ignore the entry func attribute as it is an MLIR