// so not passing it during conversion. This design might change if needed.
  ArrayRef<double> scales = qtype.getScales();
  ArrayRef<int64_t> zero_points = qtype.getZeroPoints();
  const int num_channels = scales.size();
  TensorType scale_type = RankedTensorType::get(
      {static_cast<int64_t>(num_channels)}, rewriter.getF32Type());
  TensorType zero_point_type = scale_type.clone(rewriter.getI32Type());

                .session_metadata()
                .name()
          : ""}
      .Run();
}

std::atomic<int64_t>* GetPointerToFuel(int64_t initial_value) {
  static std::atomic<int64_t>* fuel = [&]() {
    std::atomic<int64_t>* fuel = new std::atomic<int64_t>;
    *fuel = initial_value;
    return fuel;
  }();

  return fuel;
}

}  // anonymous namespace

  a.insert(a.end(), b.begin(), b.end());
}

template <typename Vector>
void Append(Vector& a, const Vector& b) {
  a.insert(a.end(), b.begin(), b.end());
}

int64_t GetNumOps(func::FuncOp func) {
  int64_t num_ops = 0;
  for (auto it = func.begin(); it != func.end(); ++it) ++num_ops;
  return num_ops;
}

std::vector<Value> ResultsAsVector(Operation* op) {
  std::vector<Value> vec;

  const std::vector<float>& bias_scales = bias_tensor->quantization->scale;
  const std::vector<float>& weights_scales =
      weights_tensor->quantization->scale;
  const std::vector<int64_t>& weights_zero_points =
      weights_tensor->quantization->zero_point;
  const int out_channel_size = weights_tensor->shape[0];
  ASSERT_THAT(bias_scales, SizeIs(out_channel_size));

} TF_TransactionToken;

// The named union is needed here (as opposed to
// inside the `TF_Filesystem_Option_Value` struct)
// as MSVC does not recognize `typeof`.
typedef union TF_Filesystem_Option_Value_Union {
  int64_t int_val;
  double real_val;
  struct {
    char* buf;
    int buf_length;
  } buffer_val;
} TF_Filesystem_Option_Value_Union;

typedef struct TF_Filesystem_Option_Value {

              "ranked tensors.";
  ArrayRef<int64_t> in_shape = ranked_type.getShape();
  if (in_shape.empty() || in_shape[0] < 0) {
    return context_op->emitOpError()
           << "A map_outside_compilation op's input and output shapes must "
              "have rank at least one and the first dimension must be known.";
  }
  int64_t split_size = in_shape[0] / num_cores_per_replica;

      {min_maxs[0], min_maxs[1]});
  mlir::ElementsAttr axis_stats;
  mlir::IntegerAttr axis;
  if (mins.size() > 1) {
    llvm::SmallVector<int64_t, 4> axis_stats_shape{
        static_cast<int64_t>(mins.size()), 2};
    axis_stats = mlir::DenseFPElementsAttr::get(
        tensorflow::GetTypeFromTFTensorShape(axis_stats_shape, b.getF32Type()),
        min_maxs);

  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_Context* ctx = TFE_NewContext(opts, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteContextOptions(opts);

  std::vector<int64_t> dims(4, 1);
  TFE_Op* op = TFE_NewOp(ctx, "AvgPool", status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TF_Tensor* tensor =

  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);

  Output delta = ops::Placeholder(scope.WithOpName("delta"), DT_FLOAT);

  ops::AssignAddVariableOp increment_op(
      scope.WithOpName("IncrementIteration"), iter,
      ops::Const(scope.WithOpName("one"), static_cast<int64_t>(1)));

  ops::AssignAddVariableOp weights_0_update_op(
      scope.WithOpName("weights_0_update"), weights_0, delta);
  ops::AssignAddVariableOp weights_1_update_op(