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());

  // on TPU.

  // Optimizes TF graph via cleanups, merges, rewrites, constant folding,
  // and edge case handling where possible.
  pm.addNestedPass<func::FuncOp>(TF::CreateDropWhileShapeInvariantPass());
  pm.addNestedPass<func::FuncOp>(
      tf_executor::CreateTFExecutorGraphPruningPass());
  pm.addNestedPass<func::FuncOp>(
      tf_executor::CreateTFExecutorIslandCoarseningPass());
  pm.addPass(TF::CreateTFFunctionalControlFlowToRegions());

    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<func::FuncOp>(mhlo::createMhloQuantLegalizeToIntPass());
  pm.addNestedPass<func::FuncOp>(createCanonicalizerPass());
  // Integer graph optimization relies on chlo broadcast ops for easier handling
  // of dynamic shapes. Therefore we lower chlo ops after optimization.
  pm.addNestedPass<func::FuncOp>(CreateOptimizeIntGraphPass());
  pm.addNestedPass<func::FuncOp>(mhlo::createChloLegalizeToHloPass());

  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.addNestedPass<mlir::func::FuncOp>(CreateConvertTFQuantOpsToMHLOPass());
  pm.addNestedPass<mlir::func::FuncOp>(mhlo::createChloLegalizeToHloPass());
  pm.addNestedPass<mlir::func::FuncOp>(mlir::createCanonicalizerPass());
  pm.addNestedPass<mlir::func::FuncOp>(
      mhlo::createMhloQuantLegalizeToIntPass());
  pm.addNestedPass<mlir::func::FuncOp>(mlir::createCanonicalizerPass());

  // First, we need to convert from functional, to executor dialect.
  pm.addNestedPass<FuncOp>(
      mlir::CreateFunctionalToExecutorDialectConversionPass());

  // Do a single pass to split the graph's single island op into an island per
  // op as expected by the following passes.
  pm.addNestedPass<FuncOp>(mlir::TF::CreateSplitIntoIslandPerOpPass());

  pm.addNestedPass<FuncOp>(mlir::TFDevice::CreateReplicateToIslandPass(

      /*legacy_graph_export=*/true));
  add_pass(mlir::TFDevice::CreateLaunchToDeviceAttributePass(
      /*legacy_graph_export=*/true));
  pm.addNestedPass<FuncOp>(mlir::TFTPU::CreateTPUDevicePropagationPass());
  pm.addNestedPass<FuncOp>(mlir::TFTPU::CreateTPUColocateSplitsPass());
  pm.addPass(mlir::createSymbolDCEPass());
  if (tensorflow::GetMlirCommonFlags()

  if (enable_inliner) pass_manager->addPass(mlir::createInlinerPass());
  pass_manager->addPass(mlir::createSymbolDCEPass());
  pass_manager->addNestedPass<mlir::func::FuncOp>(
      mlir::createCanonicalizerPass());
  pass_manager->addNestedPass<mlir::func::FuncOp>(mlir::createCSEPass());
}
}  // namespace

// TODO(b/177376459): We should make this configureable.