bool run_tac(const std::string& model_file_path,
             const std::vector<std::string>& device_specs,
             const std::string& model_output_path) {
  mlir::TFL::tac::TacModule::Options options;
  options.hardware_backends = device_specs;
  options.enable_inliner = true;
  options.legalize_to_tflite_ops = true;
  mlir::TFL::tac::TacModule tac_module(options);
  mlir::DialectRegistry registry;

}

absl::Status TacModule::RunTacPasses(mlir::ModuleOp* module, bool debug_mode) {
  mlir::PassManager pm((*module)->getName(),
                       mlir::OpPassManager::Nesting::Implicit);
  AddTACPass(&pm, options_.hardware_backends);
  if (!debug_mode) {
    AddExportTFLPass(&pm, options_.enable_inliner);
  }

  mlir::StatusScopedDiagnosticHandler statusHandler(module->getContext(),

}

absl::Status TargetAwareConversionMain() {
  std::vector<std::string> device_specs_array =
      absl::StrSplit(device_specs, ',', absl::SkipEmpty());
  mlir::TFL::tac::TacModule::Options options;
  options.hardware_backends = device_specs_array;
  options.debug_mode = true;
  if (!output_mlir || inline_subgraphs) {
    options.debug_mode = false;
  }
  options.enable_inliner = true;
  options.legalize_to_tflite_ops = true;

class TacModule {
 public:
  // TAC options. Contains knobs to configure TAC as needed.
  struct Options {
    // List of names for the requested Target hardware.
    std::vector<std::string> hardware_backends;
    // Debug mode.
    // This will output different alternative subgraphs in mlir format for debug
    // purpose.
    bool debug_mode = false;
    // Whether to enable inliner passes or not.

to use TAC to optimize for your model:

```
bazel run -c opt //tensorflow/compiler/mlir/lite/experimental/tac:tac-translate -- <PATH_TO_YOUR_MODEL> -o=<OUTPUT_PATH> -device-specs=<HARDWARE_BACKENDS>
```

The devices_specs is a list of the names of the desired hardware backends,
separated by comma, e.g., "GPU,CPU".

If you're interested in what are the subgraphs being explored for different