// This is a brief description of the pass.
    return "Import quantization stats to the model";
  }

  void runOnOperation() override;

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

  // Parses the serialized quant stats protobuf and initialize the internal

    }
    return success();
  }
};

class LiftTfliteFlexOpsPass
    : public impl::LiftTfliteFlexOpsPassBase<LiftTfliteFlexOpsPass> {
  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<TF::TensorFlowDialect>();
  }

 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(LiftTfliteFlexOpsPass)

  void runOnOperation() override {

  StringRef getDescription() const final {
    return "Unfuses MHLO batch norm inference op into arithmetic ops";
  }
  void getDependentDialects(::mlir::DialectRegistry &registry) const override {
    // TODO(b/299514833): Remove TensorFlowDialect usage.
    registry.insert<shape::ShapeDialect, mlir::TF::TensorFlowDialect>();
  }

  void runOnOperation() override {
    RewritePatternSet patterns(&getContext());

  StringRef getDescription() const final {
    // This is a brief description of the pass.
    return "Quantize weights used by quantizable ops.";
  }

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

 private:
  void runOnOperation() override;

  bool test_mode_;
  tensorflow::quantization::QuantizationOptions quant_options_;

            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();

  StringRef getDescription() const final {
    // This is a brief description of the pass.
    return "Apply converting Tensorflow Xla ops to non-xla ops.";
  }

  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<TF::TensorFlowDialect, arith::ArithDialect>();
  }

  void runOnOperation() override;
};

// Generate an einsum equation from the given DotDimensionNumber.
std::string CreateEinsumEquation(

class LegalizeTFGToTFE
    : public impl::LegalizeTFGToTFPassBase<LegalizeTFGToTFE> {
  void getDependentDialects(DialectRegistry &registry) const override {
    RegisterAllTensorFlowDialects(registry);
  }

  void runOnOperation() override;
};

}  // namespace

void LegalizeTFGToTFE::runOnOperation() {
  MLIRContext &context = getContext();

                   "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());

    MlirToFlatBufferFileTranslateFunction, [](DialectRegistry& registry) {
      registry.insert<quant::QuantizationDialect,
                      quantfork::QuantizationForkDialect>();
      mlir::RegisterAllTensorFlowDialects(registry);
      registry.insert<TFL::TensorFlowLiteDialect>();
      registry.insert<arith::ArithDialect>();
      registry.insert<func::FuncDialect>();
      registry.insert<mlir::vhlo::VhloDialect>();