LegalizeTFToQuant(const LegalizeTFToQuant &) {}

  /// Performs the lowering to Quant ops dialect.
  void runOnOperation() override;

  void getDependentDialects(DialectRegistry &registry) const override {
    registry.insert<quant::QuantizationDialect,
                    quantfork::QuantizationForkDialect>();
  }

  StringRef getArgument() const final {
    // This is the argument used to refer to the pass in

            tflite::RegisterCustomOpdefs(custom_opdefs_txt_raw.ptr()));
      },
      py::arg("custom_opdefs_txt_raw"),
      R"pbdoc(
      Registers the given custom opdefs to the TensorFlow global op registry.
    )pbdoc");
  m.def(
      "RetrieveCollectedErrors",
      []() {
        std::vector<std::string> collected_errors =
            tflite::RetrieveCollectedErrors();

void Register(const std::string& op_name, OpRegistry* registry) {
  registry->Register([op_name](OpRegistrationData* op_reg_data) -> Status {
    op_reg_data->op_def.set_name(op_name);
    return absl::OkStatus();
  });
}

}  // namespace

TEST(TfTextUtilsTest, TestTfTextRegistered) {
  std::unique_ptr<OpRegistry> registry(new OpRegistry);
  Register("WhitespaceTokenizeWithOffsets", registry.get());

  tensorflow::tfrt_compiler::
      RegisterNonTPULowerClusterToRuntimeOpsPassPipeline();

  mlir::DialectRegistry registry;
  mlir::RegisterCommonToolingDialects(registry);

  return failed(
      mlir::MlirOptMain(argc, argv, "TensorFlow pass driver\n", registry));

}

static TranslateFromMLIRRegistration mlir_to_graph_translate(
    /*name=*/"mlir-to-graph", /*description=*/"convert mlir to graph",
    MlirToGraphTranslateFunction, [](DialectRegistry& registry) {
      mlir::RegisterAllTensorFlowDialects(registry);
    });

static LogicalResult MlirToGraphdefTranslateFunction(
    ModuleOp module, llvm::raw_ostream& output) {
  if (!module) return failure();

  func.return %3 : tensor<2x1xf32>
})";
  const std::string kExpectedFB = CreateRuntimeMetadata();
  mlir::DialectRegistry registry;
  registry.insert<mlir::TFL::TensorFlowLiteDialect, mlir::arith::ArithDialect,
                  mlir::func::FuncDialect>();
  mlir::MLIRContext context(registry);
  auto module = mlir::OwningOpRef<mlir::ModuleOp>(
      mlir::parseSourceString<mlir::ModuleOp>(kMLIR, &context));

namespace mlir {

void RegisterCommonToolingDialects(mlir::DialectRegistry& registry) {
  mlir::RegisterAllTensorFlowDialects(registry);
  mlir::mhlo::registerAllMhloDialects(registry);
  mlir::registerAllDialects(registry);
  mlir::registerAllExtensions(registry);
  mlir::stablehlo::registerAllDialects(registry);

  registry.insert<mlir::TFL::TensorFlowLiteDialect>();
  registry.insert<mlir::kernel_gen::tf_framework::TFFrameworkDialect>();

}

void RuntimeFallbackExecutor::Prepare(llvm::StringRef mlir_input) {
  // We only support IR written in the Tensorflow dialect.
  mlir::DialectRegistry registry;
  mlir::RegisterAllTensorFlowDialects(registry);
  mlir::MLIRContext context(registry);

  llvm::SourceMgr source_mgr;
  source_mgr.AddNewSourceBuffer(
      llvm::MemoryBuffer::getMemBuffer(mlir_input, "test_ir"), llvm::SMLoc());

#include "tensorflow/core/tfrt/runtime/runtime.h"

namespace tensorflow {

class BackendCompiler {
 public:
  virtual ~BackendCompiler();

  virtual void GetDependentDialects(mlir::DialectRegistry& registry) const {}

  // Compile the `module` in TF dialect. The result module should be also in TF
  // dialect.
  virtual absl::Status CompileTensorflow(
      tfrt_stub::ModelRuntimeContext& model_context,

  mlir::DialectRegistry registry;
  registry.insert<mlir::func::FuncDialect, mlir::stablehlo::StablehloDialect,
                  mlir::TFL::TFLDialect, mlir::arith::ArithDialect,
                  mlir::TF::TensorFlowDialect, mlir::chlo::ChloDialect>();

  return failed(
      mlir::MlirOptMain(argc, argv, "ODML Converter Driver\n", registry));