// Add a _Retval node to loop body.
  NodeDefBuilder ret_builder(
      absl::StrCat("recv_oc_while_body_ret_", while_node_name), "_Retval");
  ret_builder.Attr("T", DT_STRING);
  ret_builder.Attr("index", 0);
  ret_builder.Input(key_arg->name(), 0, DT_STRING);
  NodeDef ret_def;
  TF_RETURN_IF_ERROR(ret_builder.Finalize(&ret_def));
  TF_ASSIGN_OR_RETURN(Node * ret_node, body_graph->AddNode(ret_def));

                 const GraphDefBuilder::Options& opts) {
  return KnownShapeBase(DT_FLOAT, shape, opts);
}

Node* KeyPlaceholderShape(const GraphDefBuilder::Options& opts) {
  return KnownShapeBase(DT_STRING, {2}, opts);
}

Node* KeyPlaceholder(const string& call_node,
                     const GraphDefBuilder::Options& opts) {
  if (opts.HaveError()) return nullptr;

    return std::nullopt;
  }

  // TensorFlow and TensorFlow Lite use different string encoding formats.
  // Convert to TensorFlow Lite format is it's a constant string tensor.
  if (tensor.dtype() == tensorflow::DT_STRING) {
    ::mlir::TFL::MiniDynamicBuffer dynamic_buffer;
    auto flat = tensor.flat<::tensorflow::tstring>();
    for (int i = 0; i < flat.size(); ++i) {
      const auto& str = flat(i);

  inputs.reserve(assets.size());
  for (const auto& asset : assets) {
    TensorInfo tensor_info;
    tensor_info.set_name(asset.tensor_name);
    tensor_info.set_dtype(DT_STRING);
    tensor_info.mutable_tensor_shape();
    inputs.push_back({asset.tensor_name, tensor_info});
  }

  std::unordered_map<std::string, std::string> tf_name_to_mlir_name;