const Scope& scope, const OutputList& xs,
    const std::vector<TensorShape>& x_shapes,
    const std::vector<Tensor>& x_datas, const OutputList& ys,
    const std::vector<TensorShape>& y_shapes,
    std::vector<Tensor>* jacobian_ts) {
  size_t y_num = y_shapes.size();
  size_t x_num = x_shapes.size();
  // Call AddSymbolicGradients to get 'dxs' (we will feed 'dys').
  OutputList dys;
  dys.reserve(y_shapes.size());

using tensorflow::AbstractFunction;
using tensorflow::AbstractTensorHandle;
using tensorflow::DataType;
using tensorflow::dyn_cast;
using tensorflow::OutputList;
using tensorflow::Status;
using tensorflow::unwrap;
using tensorflow::wrap;
using tensorflow::tracing::CreateTracingExecutionContext;
using tensorflow::tracing::SetDefaultTracingEngine;

namespace TF {
using tensorflow::AbstractFunction;
using tensorflow::AbstractOperation;
using tensorflow::AbstractTensorHandle;
using tensorflow::AbstractTensorInterface;
using tensorflow::dyn_cast;
using tensorflow::OutputList;
using tensorflow::string;
using tensorflow::errors::FailedPrecondition;
using tensorflow::errors::InvalidArgument;
using tensorflow::errors::Unimplemented;
using tensorflow::tracing::TracingContext;

                               absl::MakeSpan(model_outputs)));
      for (auto func_input : func_inputs) {
        func_input->Unref();
      }
      AbstractFunction* func = nullptr;
      OutputList output_list;
      output_list.expected_num_outputs = 0;
      output_list.outputs.reserve(outputs.size());
      for (int i = 0; i < model_outputs.size(); i++) {
        if (model_outputs[i]) {

    const auto& api_def_arg(api_def.out_arg(i));
    CHECK_EQ(arg.name(), api_def_arg.name());

    bool is_list = ArgIsList(arg);
    output_types.push_back(
        strings::StrCat("::tensorflow::", is_list ? "OutputList" : "Output"));
    output_names.push_back(AvoidCPPKeywords(api_def_arg.rename_to()));
    is_list_output.push_back(is_list);
  }

  strings::StrAppend(&comment, "\nReturns:\n");

/// or a list of tensors.
typedef std::vector<Output> OutputList;

/// A type for representing the input to ops that require a list of tensors.
class InputList {
 public:
  /// Implicitly convert a list of outputs to a list of inputs. This is useful
  /// to write code such as ops::Concat(ops::Split(x, 4)).
  InputList(const OutputList& out) {  // NOLINT(runtime/explicit)
    for (auto const& x : out) {

  EXPECT_EQ(predicate_map[ControlOutputFor(id_true)], "*cond_ref:0");
}

void CreateSwitchN(const Scope& scope, Input data, Input output_index,
                   int64_t num_outs, OutputList* outputs) {
  if (!scope.ok()) return;
  auto _data = ops::AsNodeOut(scope, data);
  if (!scope.ok()) return;
  auto _output_index = ops::AsNodeOut(scope, output_index);
  if (!scope.ok()) return;

Status BuildWhileLoop(const Scope& scope, const std::vector<Output>& inputs,
                      const CondGraphBuilderFn& cond,
                      const BodyGraphBuilderFn& body, const string& frame_name,
                      OutputList* outputs, bool create_while_ctx,
                      Output* cond_output) {
  DCHECK(!inputs.empty());
  DCHECK(outputs != nullptr);
  DCHECK(outputs->empty());

  TF_RETURN_IF_ERROR(scope.status());

    }
    inputs_.push_back(t->output_);
    *output = tensorflow::down_cast<TracingTensorHandle*>(outputs[0]);
    return absl::OkStatus();
  }

  Status Finalize(OutputList* outputs, AbstractFunction** f) override {
    std::vector<TF_Output> graph_outputs;
    graph_outputs.reserve(outputs->outputs.size());
    for (auto* abstract_output : outputs->outputs) {

      NewInternalScope(&parent->graph, &status->status, &parent->refiner)
          .NewSubScope(params->name);

  const int first_new_node_id = parent->graph.num_node_ids();

  tensorflow::OutputList loop_outputs;
  status->status = tensorflow::ops::BuildWhileLoop(
      scope, OutputsFromTFOutputs(parent_inputs, num_loop_vars), cond_fn,
      body_fn, params->name, &loop_outputs);