&mlir_module_with_add));

  std::unique_ptr<mlir::Pass> partitioning_pass =
      mlir::TFTPU::CreateTPUResourceReadsWritesPartitioningPass();
  std::unique_ptr<mlir::Pass> shape_inference_pass =
      mlir::TF::CreateTFShapeInferencePass();
  std::unique_ptr<mlir::Pass> inliner_pass = mlir::createInlinerPass();

  EXPECT_EQ(0, clusters.size());
}

TEST(XlaCompilationTest, DeterministicClusterNames) {
  auto create_graph =
      [](absl::string_view output_name) -> std::unique_ptr<Graph> {
    std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));
    GraphDefBuilder builder(GraphDefBuilder::kFailImmediately);
    Tensor t(DT_FLOAT, TensorShape());
    t.scalar<float>()() = 0.0f;

//  se::DeviceMemoryBase gpu_dst{device_tensor.data(), 4 * sizeof(float)};
//  xla::Shape shape(xla::F32, {2, 2}, {}, {})
//  tsl::AsyncValueRef<std::unique_ptr<se::Event>> done_event =
//      tsl::MakeConstructedAsyncValueRef<std::unique_ptr<se::Event>>(stream.parent());
//  done_event->Init();
//
//  XlaHostSendDeviceContext device_context(&stream, &gpu_dst,
//    shape, done_event);

  Device* GetDevice(const string& device_name);
  FunctionLibraryRuntime* flr() { return flr_; }

 private:
  FunctionLibraryRuntime* flr_;
  std::unique_ptr<DeviceMgr> device_mgr_;
  std::unique_ptr<FunctionLibraryDefinition> lib_def_;
  std::unique_ptr<ProcessFunctionLibraryRuntime> pflr_;
};

}  // namespace tensorflow

  std::unique_ptr<ParallelTensor> CopyToParallelDevice(TFE_Context* context,
                                                       TFE_TensorHandle* tensor,
                                                       TF_Status* status) const;

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(

  // future.
  static Status New(const QueueRunnerDef& queue_runner_def,
                    std::unique_ptr<QueueRunner>* result);

  /// Creates a new QueueRunner with a coordinator, see coordinator.h for usage.
  static Status New(const QueueRunnerDef& queue_runner_def, Coordinator* coord,
                    std::unique_ptr<QueueRunner>* result);

  /// Adds a callback that the queue runner will call when it detects an error.

Variable* Variable::Create(TFE_Context* context, TF_DataType type,
                           const int64_t* dims, const int num_dims,
                           const char* device, TF_Status* status) {
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(
      TFE_NewOp(context, "VarHandleOp", status), TFE_DeleteOp);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op.get(), "dtype", type);

      if (!status.ok()) out_tensor->reset();
    }
  }
  if (!status.ok()) {
    tsl::Set_TF_Status_from_Status(out_status, status);
  }
}

std::pair<std::unique_ptr<TensorSliceReader::VarToShapeMap>,
          std::unique_ptr<TensorSliceReader::VarToDataTypeMap>>
CheckpointReader::BuildV2VarMaps() {
  CHECK(v2_reader_ != nullptr);
  CHECK(v2_reader_->status().ok());

  }

  return false;
}

using deadness_analysis_internal::ComputePredicates;
using deadness_analysis_internal::PredicateMapTy;

Status AnalyzeDeadness(Graph* graph,
                       std::unique_ptr<DeadnessAnalysis>* result) {
  FixupSourceAndSinkEdges(graph);
  return DeadnessAnalysis::Run(*graph, result);
}

ops::Switch CreateSwitch(const Scope& root, const string& prefix) {

absl::StatusOr<std::string> ConvertSerializedStableHloModuleToBfloat16(
    StringRef serialized_stablehlo_module);

std::unique_ptr<OperationPass<ModuleOp>>
CreateLiftQuantizableSpotsAsFunctionsPass(
    const ::stablehlo::quantization::QuantizationSpecs& quantization_specs);

// Creates a pass that inserts CalibrationStatisticsSaverOp.
std::unique_ptr<OperationPass<ModuleOp>>
CreateInsertCalibrationStatisticsSaverPass(
    StringRef calibration_data_dir,