bool include_extensions = true) {
  registry
      .insert<mlir::arith::ArithDialect, mlir::func::FuncDialect,
              mlir::ml_program::MLProgramDialect, mlir::TF::TensorFlowDialect,
              mlir::tf_type::TFTypeDialect, mlir::cf::ControlFlowDialect,
              mlir::tf_device::TensorFlowDeviceDialect,
              mlir::tf_executor::TensorFlowExecutorDialect,

    return "Convert Fake Quant op to quant.qcast and quant.dcast pairs";
  }

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

  void runOnOperation() override;
};

  tensorflow::InitMlir y(&argc, &argv);

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

  registry.addExtension(
      +[](mlir::MLIRContext *ctx, mlir::TF::TensorFlowDialect *dialect) {
        dialect->addInterfaces<TFReductionPatternInterface>();
      });

  mlir::MLIRContext context(registry);

  return failed(mlirReduceMain(argc, argv, 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));

int main(int argc, char **argv) {
  tensorflow::InitMlir y(&argc, &argv);

  mlir::registerAllPasses();

  mlir::DialectRegistry registry;
  registry.insert<mlir::scf::SCFDialect, mlir::TF::TensorFlowDialect,
                  mlir::arith::ArithDialect, mlir::func::FuncDialect,
                  mlir::shape::ShapeDialect, mlir::quant::QuantizationDialect,
                  mlir::quantfork::QuantizationForkDialect,

TEST_F(LegalizationOpConfigTest, CountLoweringsSet) {
  int mlir_lowering_count = 0;
  int tf2xla_fallback_count = 0;
  int non_categorized_count = 0;

  DialectRegistry dialect_registry;
  dialect_registry.insert<mlir::TF::TensorFlowDialect>();

  MLIRContext context(dialect_registry);
  context.loadAllAvailableDialects();

  for (auto operation : context.getRegisteredOperations()) {
    if (IsTypeLegalizedWithMlir(operation.getTypeID())) {

  registry.insert<mlir::tensor::TensorDialect>();
  registry.insert<mlir::tosa::TosaDialect>();
  registry.insert<mlir::xla_framework::XLAFrameworkDialect,
                  mlir::TF::TensorFlowDialect, mlir::tf_type::TFTypeDialect>();
}

  tensorflow::InitMlir y(&argc, &argv);

  mlir::registerAllPasses();
  mlir::registerTensorFlowPasses();

  mlir::DialectRegistry registry;
  registry.insert<mlir::scf::SCFDialect, mlir::TF::TensorFlowDialect,
                  mlir::tf_saved_model::TensorFlowSavedModelDialect,
                  mlir::func::FuncDialect, mlir::shape::ShapeDialect,
                  mlir::arith::ArithDialect, mlir::tf_type::TFTypeDialect,

  }
  llvm::StringRef getDescription() const final {
    return "Suitable transformation for nnapi only.";
  }
  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<TF::TensorFlowDialect>();
  }
  void runOnOperation() override;
};

void DeviceTransformNNAPIPass::runOnOperation() {
  auto func = getOperation();
  auto* ctx = &getContext();
  NNAPIHardware nnapi_hardware;

  MLIRContext context;
  OwningOpRef<ModuleOp> module(ModuleOp::create(UnknownLoc::get(&context)));
  OpBuilder builder(&module->getBodyRegion());
  context.loadDialect<TF::TensorFlowDialect>();

  Value value = CreateConstValue<int8_t>(builder, module->getLoc(),
                                         unpacked_shape, unpacked_values);