applyTensorflowAndCLOptions(manager);
  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());

    output_mlir_type = input_mlir_type;
  }

  tensorflow::AddQuantizationPasses(mlir::TFL::PassConfig(quant_specs), pm);
  pm.addPass(TFL::CreateModifyIONodesPass(input_mlir_type, output_mlir_type));
  // If the first or final ops are not quantized, remove QDQ.
  pm.addPass(TFL::CreatePostQuantizeRemoveQDQPass());
  if (failed(pm.run(module.get()))) {
    const std::string err(statusHandler.ConsumeStatus().message());

    LOG(ERROR) << "Couldn't import flatbuffer to MLIR.";
    return absl::InternalError("Couldn't import flatbuffer to MLIR.");
  }

  PassManager pm((*module)->getName(), OpPassManager::Nesting::Implicit);
  pm.addPass(TFL::CreateDenseToSparsePass());

  if (failed(pm.run(module.get()))) {
    LOG(ERROR) << "Failed to sparsify: "
               << statusHandler.ConsumeStatus().message();
    return absl::InternalError(absl::StrCat(