print(f"Adding missing translations for {language}")
    add_missing(language=language, max=max)
    print(f"Done updating and adding for {language}")


@app.command()
def make_pr(
    *,
    language: Annotated[str | None, typer.Option(envvar="LANGUAGE")] = None,
    command: Annotated[str | None, typer.Option(envvar="COMMAND")] = None,

      MapMakerInternalMap<K, V, ? extends InternalEntry<K, V, ?>, ? extends Segment<K, V, ?, ?>>
          makeMap(MapMaker maker) {
    return MapMakerInternalMap.create(maker);
  }

  private static MapMaker createMapMaker() {
    MapMaker maker = new MapMaker();
    maker.useCustomMap = true;
    return maker;
  }

  // constructor tests

  public void testDefaults() {

      MapMakerInternalMap<K, V, ? extends InternalEntry<K, V, ?>, ? extends Segment<K, V, ?, ?>>
          makeMap(MapMaker maker) {
    return MapMakerInternalMap.create(maker);
  }

  private static MapMaker createMapMaker() {
    MapMaker maker = new MapMaker();
    maker.useCustomMap = true;
    return maker;
  }

  // constructor tests

  public void testDefaults() {

    *   Add `include_preprocessing` argument to `MobileNetV3` architectures to
        control the inclusion of `Rescaling` layer in the model.
    *   Add optional argument (`force`) to `make_(train|test|predict)_funtion`
        methods to skip the cached function and generate a new one. This is
        useful to regenerate in a single call the compiled training function