AddConvertTpuToCpuModelPasses(pm);
  }
  pm.addNestedPass<mlir::func::FuncOp>(
      mlir::quant::CreateConvertTfXlaOpToTfOpPass());
  pm.addNestedPass<mlir::func::FuncOp>(
      mlir::quant::CreatePrepareLiftingPass(quantization_options.op_set()));

  pm.addPass(mlir::quant::CreateLiftQuantizableSpotsAsFunctionsPass(
      quantization_options));
  pm.addPass(mlir::quant::CreateInsertQuantizedFunctionsPass(

    func.emitError() << "quant-prepare-lifting failed.";
    signalPassFailure();
  }
}

}  // namespace

std::unique_ptr<OperationPass<func::FuncOp>> CreatePrepareLiftingPass(
    const OpSet target_opset) {
  return std::make_unique<PrepareLiftingPass>(target_opset);
}

static PassRegistration<PrepareLiftingPass> pass;

}  // namespace quant

    const tensorflow::quantization::QuantizationOptions& quant_options);

// Apply graph optimizations such as fusing and constant folding to prepare
// lifting.
std::unique_ptr<OperationPass<func::FuncOp>> CreatePrepareLiftingPass(
    tensorflow::quantization::OpSet target_opset);

// Lifts the dynamic range quantizable spots as composite functions.
std::unique_ptr<OperationPass<ModuleOp>>
CreateLiftQuantizableSpotsAsFunctionsDRQPass(