mlir::OpPassManager::Nesting::Implicit);
  AddTACPass(&pm, options_.hardware_backends);
  if (!debug_mode) {
    AddExportTFLPass(&pm, options_.enable_inliner);
  }

  mlir::StatusScopedDiagnosticHandler statusHandler(module->getContext(),
                                                    /*propagate=*/true);
  if (failed(pm.run(*module))) {
    return absl::InternalError("conversion error");
  }

  print_flags.elideLargeElementsAttrs();

  if (VLOG_IS_ON(1)) {
    VLOG(1) << "Input TF Executor dialect:";
    DumpMlirOpToFile("tf_to_tfrt_tf_executor_dialect", module);
  }

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

  // Lower MLIR TF Dialect to MLIR TFRT CoreRT dialect.
  mlir::PassManager pm(module.getContext());
  tensorflow::applyTensorflowAndCLOptions(pm);

namespace mlir {
namespace lite {

absl::Status SparsifyModel(const tflite::ModelT& input_model,
                           flatbuffers::FlatBufferBuilder* builder) {
  MLIRContext context;
  StatusScopedDiagnosticHandler statusHandler(&context,
                                              /*propagate=*/true);

  // Import input_model to a MLIR module
  flatbuffers::FlatBufferBuilder input_builder;

    std::unique_ptr<tensorflow::SavedModelBundle>* saved_model_bundle);

Status ConvertTFExecutorToStablehloFlatbuffer(
    mlir::PassManager& pass_manager, mlir::ModuleOp module, bool export_to_mlir,
    mlir::StatusScopedDiagnosticHandler& statusHandler,
    const toco::TocoFlags& toco_flags, const mlir::TFL::PassConfig& pass_config,
    std::optional<tensorflow::Session*> session, std::string* result);

  PassManager tf_to_executor(module.getContext());
  ::tensorflow::applyTensorflowAndCLOptions(tf_to_executor);
  tf_to_executor.enableVerifier();

  AddTfDialectToExecutorPasses(tf_to_executor);

  mlir::StatusScopedDiagnosticHandler diag_handler(
      module.getContext(), /*propagate=*/false,
      /*filter_stack=*/!VLOG_IS_ON(1));

  if (VLOG_IS_ON(1) ||
      DEBUG_DATA_DUMPER()->ShouldDump(module_name.str(), kDebugGroupMain)) {

    QuantizationConfig* quantization_config,
    const PyFunctionLibrary* quantization_py_function_lib,
    const SavedModelBundle* saved_model_bundle, mlir::PassManager& pass_manager,
    mlir::StatusScopedDiagnosticHandler& status_handler, ModuleOp& module) {
  // TODO: b/194747383 - We need to valid that indeed the "main" func is
  // presented.
  AddPreQuantizationStableHloToTfPasses(/*entry_function_name=*/"main",

  for (auto& entry : denylisted_ops) {
    denylisted_mlir_op_names.insert(TfLiteToMlir(entry));
  }

  DialectRegistry registry;
  MLIRContext context(registry);
  StatusScopedDiagnosticHandler statusHandler(&context,
                                              /*propagate=*/true);

  // Import input_model to a MLIR module
  flatbuffers::FlatBufferBuilder input_builder;

    denylisted_mlir_op_names.insert(TfLiteToMlir(entry));
  }

  DialectRegistry registry;
  registry.insert<mlir::TFL::TensorFlowLiteDialect>();
  MLIRContext context(registry);
  StatusScopedDiagnosticHandler statusHandler(&context,
                                              /*propagate=*/true);

  OwningOpRef<mlir::ModuleOp> module = tflite::FlatBufferToMlir(
      model_buffer, &context, UnknownLoc::get(&context));

  bridge.enableVerifier();
  ::tensorflow::applyTensorflowAndCLOptions(bridge);

  // Populate a passmanager with the list of passes that implement the bridge.
  pipeline_builder(bridge);

  mlir::StatusScopedDiagnosticHandler diag_handler(
      module.getContext(), /*propagate=*/false,
      /*filter_stack=*/!VLOG_IS_ON(1));

  if (VLOG_IS_ON(1) ||
      DEBUG_DATA_DUMPER()->ShouldDump(module_name.str(), kDebugGroupMain)) {

  bridge.enableVerifier();
  ::tensorflow::applyTensorflowAndCLOptions(bridge);

  // Populate a passmanager with the list of passes that implement the bridge.
  pipeline_builder(bridge);

  mlir::StatusScopedDiagnosticHandler diag_handler(
      module.getContext(), /*propagate=*/false,
      /*filter_stack=*/!VLOG_IS_ON(1));

  if (VLOG_IS_ON(1) ||
      DEBUG_DATA_DUMPER()->ShouldDump(module_name.str(), kDebugGroupMain)) {