{asset_file_defs.begin(), asset_file_defs.end()});
}

absl::StatusOr<ExportedModel> ExportCalibrationModel(
    mlir::ModuleOp module_op, mlir::MLIRContext *context,
    const QuantizationOptions &quantization_options,
    const absl::flat_hash_map<std::string, std::string> &function_aliases,
    absl::string_view calibration_data_dir) {
  // Clone ModuleOp and function aliases so changes in this pipeline won't

from tensorflow.python.util import tf_export

# Type aliases for quant_opts_pb2 messages.
_QuantizationOptions = tf_export.tf_export(
    'quantization.experimental.QuantizationOptions'
)(quant_opts_pb2.QuantizationOptions)

_QuantizationMethod = tf_export.tf_export(
    'quantization.experimental.QuantizationMethod'
)(quant_opts_pb2.QuantizationMethod)

_QuantizationComponentSpec = tf_export.tf_export(

#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"

namespace tensorflow {
namespace quantization {
namespace {

using ::tensorflow::quantization::QuantizationOptions;

void AddConvertTpuToCpuModelPasses(mlir::OpPassManager &pm) {
  pm.addPass(mlir::quant::CreateConvertTpuModelToCpuPass());
  pm.addPass(mlir::createInlinerPass());

using ::tensorflow::quantization::OpSet;
using ::tensorflow::quantization::QuantizationComponentSpec;
using ::tensorflow::quantization::QuantizationMethod;
using ::tensorflow::quantization::QuantizationOptions;
using ::tensorflow::quantization::UnitWiseQuantizationSpec;

class LiftQuantizableSpotsAsFunctionsPass
    : public PassWrapper<LiftQuantizableSpotsAsFunctionsPass,
                         OperationPass<ModuleOp>> {

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

      // The default minimum number of elements a weights array must have to be
      // quantized by this transformation.
      const int kWeightsMinNumElementsDefault = 1024;

      quantization::QuantizationOptions quantization_options;

      quantization_options.mutable_quantization_method()->set_preset_method(
          quantization::QuantizationMethod::METHOD_DYNAMIC_RANGE_INT8);