model2_decoder.js

class Decoder(tf.keras.Model):
   def __init__(self, vocab_size, embedding_dim, output_length, dec_units,att_units):
      super().__init__()
      self.onestep=One_Step_Decoder(vocab_size, embedding_dim, output_length, dec_units,att_units)
        
    def call(self, input_to_decoder,encoder_output,state_1)
       all_outputs=tf.TensorArray(tf.float32,input_to_decoder.shape[1],name="output_array")
       for step in range(input_to_decoder.shape[1]):
          output,state_1,alpha=self.onestep(input_to_decoder[:,step:step+1],encoder_output,state_1)
          all_outputs=all_outputs.write(step,output)
        
        all_outputs=tf.transpose(all_outputs.stack(),[1,0,2])
        
        return all_outputs