tensorflow2.3.0において、ModelのSaveができない

Question

TensorflowのTutorialの画像キャプション生成のタスクで、encoder及びdecoderのモデル全体の保存をしようとしたら、encoderは難なく保存できましたが、decoderの保存ができなくて行き詰っています。

それぞれバージョンです

Python：3.8.10
Tensorflow : 2.3.0

Tensorflow tutorialの当該ぺージ

decoderのプログラムがこちらです。

class BahdanauAttention(tf.keras.Model):#アテンションモデル
    def __init__(self, units):
        super(BahdanauAttention, self).__init__()
        self.W1 = tf.keras.layers.Dense(units)
        self.W2 = tf.keras.layers.Dense(units)
        self.V = tf.keras.layers.Dense(1)

    def call(self, features, hidden):
        # features(CNN_encoder output) shape == (batch_size, 64, embedding_dim)

        # hidden shape == (batch_size, hidden_size)
        # hidden_with_time_axis shape == (batch_size, 1, hidden_size)
        hidden_with_time_axis = tf.expand_dims(hidden, 1)

        # score shape == (batch_size, 64, hidden_size)
        score = tf.nn.tanh(self.W1(features) + self.W2(hidden_with_time_axis))

        # attention_weights shape == (batch_size, 64, 1)
        # score を self.V に適用するので、最後の軸は 1 となる
        attention_weights = tf.nn.softmax(self.V(score), axis=1)

        # 合計をとったあとの　context_vector の shpae == (batch_size, hidden_size)
        context_vector = attention_weights * features
        context_vector = tf.reduce_sum(context_vector, axis=1)

        return context_vector, attention_weights

class RNN_Decoder(tf.keras.Model):
  def __init__(self, embedding_dim, units, vocab_size):
    super(RNN_Decoder, self).__init__()
    self.units = units

    self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
    self.gru = tf.keras.layers.GRU(self.units,
                                   return_sequences=True,
                                   return_state=True,
                                   recurrent_initializer='glorot_uniform')
    self.fc1 = tf.keras.layers.Dense(self.units)
    self.fc2 = tf.keras.layers.Dense(vocab_size)

    self.attention = BahdanauAttention(self.units)

  def call(self, x, features, hidden):
    # アテンションを別のモデルとして定義
    context_vector, attention_weights = self.attention(features, hidden)

    # embedding 層を通過したあとの x の shape == (batch_size, 1, embedding_dim)
    x = self.embedding(x)

    # 結合後の x の shape == (batch_size, 1, embedding_dim + hidden_size)
    x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)

    # 結合したベクトルを GRU に渡す
    output, state = self.gru(x)

    # shape == (batch_size, max_length, hidden_size)
    x = self.fc1(output)

    # x shape == (batch_size * max_length, hidden_size)
    x = tf.reshape(x, (-1, x.shape[2]))

    # output shape == (batch_size * max_length, vocab)
    x = self.fc2(x)

    return x, state, attention_weights

  def reset_state(self, batch_size):
    return tf.zeros((batch_size, self.units))

保存しようとしたプログラムがこちらです

decoder.save("saved_model/decoder")

実行結果です

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-50-3e1e31b58585> in <module>
----> 1 decoder.save("saved_model/decoder")

~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py in save(self, filepath, overwrite, include_optimizer, save_format, signatures, options)
   1976     ```
   1977     """
-> 1978     save.save_model(self, filepath, overwrite, include_optimizer, save_format,
   1979                     signatures, options)
   1980 

.
.
.


~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\autograph\impl\api.py in wrapper(*args, **kwargs)
    300   def wrapper(*args, **kwargs):
    301     with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 302       return func(*args, **kwargs)
    303 
    304   if inspect.isfunction(func) or inspect.ismethod(func):

TypeError: call() missing 2 required positional arguments: 'features' and 'hidden'

エラーが多すぎるので途中省略しています。よろしくお願いします