}

 private:
  void runOnOperation() override;

  void getDependentDialects(DialectRegistry &registry) const override {
    mlir::mhlo::registerAllMhloDialects(registry);
    mlir::stablehlo::registerAllDialects(registry);
    registry.insert<mlir::func::FuncDialect, mlir::arith::ArithDialect>();
    registry.insert<shape::ShapeDialect>();
  }

 public:
  StringRef getArgument() const final { return "tf-mhlo"; }

  options.enable_inliner = true;
  options.legalize_to_tflite_ops = true;
  mlir::TFL::tac::TacModule tac_module(options);
  mlir::DialectRegistry registry;
  mlir::RegisterAllTensorFlowDialects(registry);
  tac_module.RegisterExtraDialects(registry);
  tac_module.SetImporter(CreateTfLiteImporter(model_file_path));
  tac_module.SetExporter(
      CreateTfLiteExporter(model_output_path, options.hardware_backends));

TPUEmbeddingOpsRegistry::GetOpsTypeIds() {
  return ops_type_ids_;
}

// static
TPUEmbeddingOpsRegistry& TPUEmbeddingOpsRegistry::Global() {
  static TPUEmbeddingOpsRegistry* registry = new TPUEmbeddingOpsRegistry;
  return *registry;
}
}  // namespace TF

namespace mlir {
namespace tf_saved_model {

// Create a pass that removes function arguments that map to global tensors.
std::unique_ptr<Pass> CreateLowerGlobalsToMlProgramPass();

// Register this pass in the global registry of MLIR.
void RegisterLowerGlobalsToMlProgramPass();

}  // namespace tf_saved_model
}  // namespace mlir

namespace mlir {
namespace TF {

// Create an instance of a pass that reorders ops so ops of the same dialect are
// next to each other.
std::unique_ptr<Pass> CreateOrderByDialectPass();

// Register this pass in the global registry of MLIR.
void RegisterOrderByDialectPass();

}  // namespace TF
}  // namespace mlir

using TFInitProfilerFn = void (*)(TF_ProfilerRegistrationParams* const,
                                  TF_Status* const);

// Registers plugin's profiler to TensorFlow's profiler registry.
Status InitPluginProfiler(TFInitProfilerFn init_fn);

}  // namespace profiler
}  // namespace tensorflow

  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();

                   "allowlist) as select Tensorflow ops"),
    llvm::cl::init(false));

// The following approach allows injecting opdefs in addition
// to those that are already part of the global TF registry  to be linked in
// prior to importing the graph. The primary goal is for support of custom ops.
// This is not intended to be a general solution for custom ops for the future

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());