TypeError: call () отсутствует 1 обязательный позиционный аргумент: 'state_ c'

Question

Я пытаюсь внимательно реализовать функцию декодера и получаю сообщение об ошибке:

 TypeError: call() missing 1 required positional argument: 'state_c'"  

class Decoder(tf.keras.Model):
     def __init__(self,out_vocab_size, embedding_dim, output_length, dec_units ,score_fun ,att_units):
     #Intialize necessary variables and create an object from the class onestepdecoder
        super(Decoder, self).__init__()
        self.vocab_size    = out_vocab_size
        self.embedding_dim = embedding_dim
        self.out_length    = output_length
        self.dec_units     = dec_units
        self.score_fun     = score_fun
        self.att_units     = att_units
        self.stepdec       = OneStepDecoder(self.vocab_size, self.embedding_dim,self.out_length, self.dec_units,self.score_fun, self.att_units)

    
    def call(self, input_to_decoder,encoder_output,decoder_hidden_state,state_c ):
        all_outputs= tf.TensorArray(tf.float32, size = input_to_decoder.shape[1], name="output_arrays")
        for timestep in range(input_to_decoder.shape[1]):
            output, input_state = self.stepdec(input_to_decoder[:,timestep:timestep+1],state_c, encoder_output)
            all_outputs         = all_outputs.write(timestep, output)
        all_outputs             = tf.transpose(all_outputs.stack(), [1, 0, 2])
        return all_outputs
def grader_decoder(score_fun):
    out_vocab_size = 13 
    embedding_dim  = 12 
    input_length   = 10
    output_length  = 11
    dec_units      = 16 
    att_units      = 16
    batch_size     = 32

    target_sentences = tf.random.uniform(shape=(batch_size,output_length),maxval=10,minval=0,dtype=tf.int32)
    encoder_output   = tf.random.uniform(shape=[batch_size,input_length,dec_units])
    state_h          = tf.random.uniform(shape=[batch_size,dec_units])
    state_c          = tf.random.uniform(shape=[batch_size,dec_units])
    decoder          = Decoder(out_vocab_size, embedding_dim, output_length, dec_units ,score_fun ,att_units)
    output           = decoder(target_sentences,encoder_output, state_h, state_c)
    assert(output.shape==(batch_size,output_length,out_vocab_size))
    return True
print(grader_decoder('dot'))
print(grader_decoder('general'))
print(grader_decoder('concat'))

Мы будем очень признательны за любые предложения или решения.