std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> s(TF_NewStatus(),
                                                           TF_DeleteStatus);

  TF_Tensor* tensor_shape = Int32Tensor({37, 1});
  TF_Operation* shape = Const(tensor_shape, func_graph.get(), s.get(), "shape");
  TF_Operation* random =
      RandomUniform(shape, TF_FLOAT, func_graph.get(), s.get());

  TF_Output outputs[] = {{random, 0}};

#include "tensorflow/compiler/mlir/tensorflow/utils/convert_type.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/mangling_util.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"

namespace mlir {

namespace {

constexpr char kDeviceAttr[] = "device";

#define GEN_PASS_DEF_RESOURCEOPLIFTINGPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_device_passes.h.inc"

#include "tensorflow/compiler/mlir/tensorflow/utils/dynamic_shape_utils.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/mangling_util.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/lite/experimental/remat/metadata_util.h"
#include "tensorflow/lite/graph_info.h"

#include "tensorflow/core/framework/partial_tensor_shape.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/graph/tensor_id.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/lib/io/path.h"

    return false;
  }

  const TensorProto* proto = nullptr;
  if (!TryGetNodeAttr(const_input->def(), "value", &proto)) {
    return false;
  }

  return TensorShapeUtils::IsScalar(proto->tensor_shape());
}

Status MarkForCompilationPassImpl::RunEdgeContractionLoop() {
  TF_RET_CHECK(initialized_ && !edges_contracted_ && !clusters_created_);
  edges_contracted_ = true;

  Output iter =
      ops::VarHandleOp(scope.WithOpName("iter"), DT_INT64, TensorShape({}));
  Output weights_0 = ops::VarHandleOp(scope.WithOpName("weights_0"), DT_FLOAT,
                                      TensorShape({1000}));
  Output weights_1 = ops::VarHandleOp(scope.WithOpName("weights_1"), DT_FLOAT,
                                      TensorShape({1000}));

  // We update the weights by adding delta to them (to "simulate" a

}

TEST(DeadnessAnalysisTest, RefBoolSwitchCondition) {
  Scope root = Scope::NewRootScope().ExitOnError();

  Output condition_ref_var =
      ops::Variable(root.WithOpName("cond_ref"), TensorShape({}), DT_BOOL);
  Output value = ops::Placeholder(root.WithOpName("value"), DT_FLOAT);
  ops::Switch sw(root.WithOpName("switch"), value, condition_ref_var);

    // creating the tf.Pack op. tf.Pack op requires at least one operand.
    if (tensors.empty()) {
      tensorflow::Tensor tensor(list->element_dtype,
                                tensorflow::TensorShape(result_shape));
      auto attr_or = tensorflow::ConvertTensor(tensor, &rewriter);
      if (!attr_or.ok()) return failure();
      rewriter.replaceOpWithNewOp<TF::ConstOp>(op, attr_or.value());
      return success();