code: def __init__(
      self,
      output_feature_num,
      lstm_hidden_dim=128,
      lstm_layer_num=2,
      dropout=0.2,
  ):
      self.output_feature_num = output_feature_num
      self.lstm_hidden_dim = lstm_hidden_dim
      self.lstm_layer_num = lstm_layer_num
      self.dropout = dropout
      self.decoder_lstm = []
      for i in range(lstm_layer_num):
          self.decoder_lstm.append(
              LSTM(
                  self.lstm_hidden_dim,
                  return_sequences=True,
                  return_state=True,
                  dropout=dropout,
                  name="lstm_decoder_" + str(i),
              )
          )
      self.fc = Dense(self.output_feature_num)
      super(Decoder, self).__init__()
  
imports: import numpy
  import tensorflow
  from tensorflow.keras import backend
  from bigdl.nano.tf.keras import Model
  from tensorflow.keras.layers import LSTM, Dense, Lambda, Reshape, Layer, Input
label_desc: Use image node_id 1 to create a new Decoder object from inherited base classes: Layer with example: obj = Decoder(output_feature_num, lstm_hidden_dim, lstm_layer_num, dropout)