pass_manager.addPass(mlir::TFL::CreatePrepareQuantizeVariablesPass());
    pass_manager.addNestedPass<mlir::func::FuncOp>(
        mlir::TFL::CreateQuantizePass(quant_specs));
    pass_manager.addNestedPass<mlir::func::FuncOp>(
        mlir::TFL::CreatePostQuantizePass(emit_quant_adaptor_ops));
  }
  pass_manager.addNestedPass<mlir::func::FuncOp>(
      mlir::TFL::CreateOptimizeOpOrderPass());

    pm.addPass(mlir::TF::CreateTFShapeInferencePass());
    pm.addNestedPass<mlir::func::FuncOp>(mlir::createCanonicalizerPass());
    if (quantization_options.op_set() == OpSet::XLA) {
      pm.addNestedPass<mlir::func::FuncOp>(
          mlir::quant::CreateReplaceCastHacksWithTFXLAOpsPass());
    }
    pm.addNestedPass<mlir::func::FuncOp>(mlir::createCSEPass());
  }
  pm.addNestedPass<mlir::func::FuncOp>(mlir::quant::CreateOptimizePass());
}

  pm.addNestedPass<FuncOp>(
      mlir::TFDevice::CreateVerifyNoOutsideCompilationMarkersPass());

  pm.addNestedPass<FuncOp>(mlir::TFDevice::CreateClusterConstantSinkingPass());
  pm.addPass(mlir::TF::CreateResourceDeviceInferencePass());
  pm.addNestedPass<FuncOp>(
      tensorflow::tf2xla::internal::CreateHoistBroadcastReadPass());
  pm.addNestedPass<FuncOp>(

  pm.addNestedPass<mlir::func::FuncOp>(
      mlir::mhlo::createLegalizeEinsumToDotGeneralPass());
  pm.addNestedPass<mlir::func::FuncOp>(
      mlir::mhlo::createLegalizeDotToDotGeneralPass());
  // Unfuse mhlo BatchNorm to primitive ops.
  pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createUnfuseBatchNormPass());
  // Fuse Conv + Mul to Conv.

    pm.addPass(mlir::mhlo::createStablehloLegalizeToHloPass());
  }

  pm.addNestedPass<mlir::func::FuncOp>(mlir::TF::CreateLowerQuantizedPass());
  pm.addNestedPass<mlir::func::FuncOp>(
      mlir::quant::stablehlo::CreateConvertTFQuantTypesPass());

  if (lower_to_xla_hlo) {
    for (auto& target_pass : custom_legalization_passes) {
      pm.addNestedPass<mlir::func::FuncOp>(std::move(target_pass));
    }

  AddCallModuleSerializationPasses(pm);
  if (duplicate_shape_determining_constants) {
    pm.addNestedPass<mlir::func::FuncOp>(
        mlir::quant::CreateDuplicateShapeDeterminingConstantsPass());
  }

  pm.addPass(mlir::quant::CreateInsertMainFunctionPass());
  pm.addPass(mlir::quant::CreateLiftHashTableOpsAsArgsPass());
  pm.addNestedPass<mlir::func::FuncOp>(
      mlir::CreateFunctionalToExecutorDialectConversionPass());

namespace {

void CreateReplicatedBridgePipelineV1(OpPassManager &pm) {
  pm.addPass(mlir::tf2xla::internal::CreateInferenceMetricsPass());

  // Convert to unified compilation and replication attributes.
  pm.addNestedPass<FuncOp>(
      mlir::TF::CreateCanonicalizeCompileAndReplicateAttributesPass());
  // Guarantee all functions have one use, which enables more exact shape
  // inference.

  manager.addPass(mlir::TF::CreatePrepareTpuComputationForTfExportPass());
  manager.addPass(mlir::TF::CreateTFRegionControlFlowToFunctional());
  manager.addPass(mlir::TF::CreateTFShapeInferencePass());
  manager.addNestedPass<mlir::func::FuncOp>(
      mlir::CreateFunctionalToExecutorDialectConversionPass());
  manager.addPass(mlir::CreateBreakUpIslandsPass());

  mlir::StatusScopedDiagnosticHandler diag_handler(module.getContext());

MlirFunction::GetFunctionRecord() {
  if (func_record_) {
    return func_record_.GetNewRef();
  }
  PassManager pm(func_.getContext());
  ::tensorflow::applyTensorflowAndCLOptions(pm);
  pm.addNestedPass<func::FuncOp>(
      CreateFunctionalToExecutorDialectConversionPass());
  pm.addPass(CreateBreakUpIslandsPass());

  // In case of failure, the `diag_handler` converts MLIR errors emitted to

    // Run the Convert TF Quant Types, TF Quant -> MHLO Quant and MHLO Quant ->
    // MHLO int passes.
    PassManager pm(module_op->getContext());
    pm.addNestedPass<func::FuncOp>(CreateConvertTFQuantTypesPass());
    AddQuantizationLoweringPasses(pm);
    CHECK(succeeded(pm.run(module_op.get())));
    // Compile the program.