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() {

    (*var_to_shape_map)[key] = TensorShape(entry.shape());
    (*var_to_data_type_map)[key] = DataType(entry.dtype());
  }
  // The returned pointers are owned by the caller.
  return std::make_pair(std::move(var_to_shape_map),
                        std::move(var_to_data_type_map));
}

}  // namespace checkpoint

    return Arrays.copyOfRange(source, from, to, (Class<? extends T[]>) arrayOfType.getClass());
  }

  /**
   * Configures the given map maker to use weak keys, if possible; does nothing otherwise (i.e., in
   * GWT). This is sometimes acceptable, when only server-side code could generate enough volume
   * that reclamation becomes important.
   */

    return Arrays.copyOfRange(source, from, to, (Class<? extends T[]>) arrayOfType.getClass());
  }

  /**
   * Configures the given map maker to use weak keys, if possible; does nothing otherwise (i.e., in
   * GWT). This is sometimes acceptable, when only server-side code could generate enough volume
   * that reclamation becomes important.
   */

    int64_t target_id = target_tensor_ids[i];
    if (sources_set.find(target_id) != sources_set.end()) {
      auto tensor = targets[i];
      sources_that_are_targets.insert(
          std::make_pair(target_id, TapeTensor(tensor)));
    }
  }

  TF_RETURN_IF_ERROR(GradientTape::ComputeGradient(
      vspace, target_tensor_ids, source_tensor_ids, sources_that_are_targets,

    *   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