jarOutputStream.addJarEntry(JarFile.MANIFEST_NAME, manifestFile)
                }
                val visited = linkedSetOf<ClassDetails>()
                for (classDetails in classGraph.entryPoints) {
                    visitTree(classDetails, classesDir, jarOutputStream, visited)
                }
            }
        } catch (exception: Exception) {

   */
  static <E extends @Nullable Object> void writeMultiset(
      Multiset<E> multiset, ObjectOutputStream stream) throws IOException {
    int entryCount = multiset.entrySet().size();
    stream.writeInt(entryCount);
    for (Multiset.Entry<E> entry : multiset.entrySet()) {
      stream.writeObject(entry.getElement());
      stream.writeInt(entry.getCount());
    }
  }

  /**

      if (object instanceof Multiset.Entry) {
        Entry<?> entry = (Entry<?>) object;
        Object element = entry.getElement();
        int entryCount = entry.getCount();
        if (entryCount != 0) {
          // Safe as long as we never add a new entry, which we won't.
          // (Presumably it can still throw CCE/NPE but only if the underlying Multiset does.)

      if (object instanceof Multiset.Entry) {
        Entry<?> entry = (Entry<?>) object;
        Object element = entry.getElement();
        int entryCount = entry.getCount();
        if (entryCount != 0) {
          // Safe as long as we never add a new entry, which we won't.
          // (Presumably it can still throw CCE/NPE but only if the underlying Multiset does.)

    accepts a `return_attention_scores` argument. When set to True, the layer
    returns the attention scores as an additional output argument.
*   Adds `tf.metrics.log_cosh` and `tf.metrics.logcosh` API entrypoints with the
    same implementation as their `tf.losses` equivalent.
*   For Keras model, the individual call of `Model.evaluate` uses no cached data
    for evaluation, while `Model.fit` uses cached data when `validation_data`