}

  std::unordered_set<int> stop_backprop_nodes =
      GetStopBackpropNodes(reachable_nodes, output_nodes);

  // Populate `input_nodes_` from Outputs in `inputs_`.
  input_nodes_.reserve(inputs_.size());
  for (size_t i = 0; i < inputs_.size(); ++i) {
    input_nodes_.insert({inputs_[i], i});
  }

  // TODO(andydavis) Consider a more efficient data structure for `pending_` to

}

class QuantizeModelTest : public testing::Test {
 protected:
  QuantizeModelTest() {
    input_model_ = ReadModel(internal::kConvModelWith0Plus10Weights);
    readonly_model_ = input_model_->GetModel();
    model_ = UnPackFlatBufferModel(*readonly_model_);
  }

  std::unique_ptr<FlatBufferModel> input_model_;
  const Model* readonly_model_;
  tflite::ModelT model_;

                                    " into function '", fn_name, "'");

    input_tensors->emplace_back(node, idx);

    const auto& iter = input_nodes->find(node);
    if (iter == input_nodes->end()) {
      input_nodes->insert({node, {idx}});
    } else {
      auto& indices = iter->second;
      if (std::find(indices.begin(), indices.end(), idx) != indices.end()) {

  ASSERT_NE(if_node, nullptr);
  const Node *input_node;
  TF_CHECK_OK(if_node->input_node(1, &input_node));
  EXPECT_EQ(input_node->name(), "arg1");
  TF_CHECK_OK(if_node->input_node(2, &input_node));
  EXPECT_EQ(input_node->name(), "arg0");
  const Node *ret0_node = node_name_index.at("ret0");
  ASSERT_NE(ret0_node, nullptr);
  TF_CHECK_OK(ret0_node->input_node(0, &input_node));
  EXPECT_EQ(input_node->name(), "if");

              shape_refiner->GetContext(n);
          for (int i = 0; i < n->num_inputs(); i++) {
            const Node* input_node;
            if (n->input_node(i, &input_node).ok()) {
              auto shapes_and_types = context->input_handle_shapes_and_types(i);
              if (shapes_and_types) {
                context->set_output_handle_shapes_and_types(0,

                              const absl::string_view inference_type,
                              QuantizationSpecs* quant_specs) {
  const std::vector<std::string> input_nodes = absl::StrSplit(node_names, ',');
  std::vector<std::optional<double>> node_mins;
  if (!min_values.empty()) {
    std::vector<std::string> node_mins_str = absl::StrSplit(min_values, ',');

  QuantizeWeightsTest() {}

  void LoadBasicModel() {
    input_model_ = ReadTestModel();
    model_ = input_model_->GetModel();
  }

  void LoadSharedWeightsModel() {
    input_model_ = ReadSharedWeightsTestModel();
    model_ = input_model_->GetModel();
  }

  void LoadGatherTestModel() {
    input_model_ = ReadGatherTestModel();
    model_ = input_model_->GetModel();
  }

  void LoadCustomOpTestModel() {

  EXPECT_EQ(identity_node->attrs().FindByString("T")->type(), DT_FLOAT);

  EXPECT_EQ(identity_node->num_inputs(), 1);
  const Node* identity_input_node = nullptr;
  TF_EXPECT_OK(identity_node->input_node(0, &identity_input_node));
  EXPECT_EQ(identity_input_node->name(), "_arg0");

  const Node* arg_node = node_name_index.at("_arg0");
  EXPECT_EQ(arg_node->op_def().name(), "_Arg");

      send_recv_nodes.push_back(n);
    }
  }
  EXPECT_EQ(num_send_from_host, 1);
  EXPECT_EQ(num_recv_at_host, 1);
  for (Node *n : send_recv_nodes) {
    Node *input_node;
    TF_CHECK_OK(n->input_node(n->num_inputs() - 1, &input_node));
    EXPECT_EQ(input_node, key_placeholder);

    bool has_control_edge_to_sequencer = false;
    for (const Edge *e : n->out_edges()) {
      if (e->IsControlEdge() && e->dst() == sequencer) {

  NodeBuilder while_builder(kFunctionalWhileNodeName, "While",
                            builder.opts().op_registry());
  while_builder.Input({input_node, input_node})
      .Attr("cond", compilable)
      .Attr("body", uncompilable);
  builder.opts().FinalizeBuilder(&while_builder);

  GraphDef graph_def;
  TF_EXPECT_OK(builder.ToGraphDef(&graph_def));
  std::unique_ptr<Graph> graph(new Graph(flib_def_.get()));