code: def __init__(
      self,
      future_seq_len,
      input_feature_num,
      output_feature_num,
      lstm_hidden_dim=128,
      lstm_layer_num=2,
      dropout=0.2,
      teacher_forcing=False,
  ):
      super(LSTMSeq2Seq, self).__init__()
      self.future_seq_len = future_seq_len
      self.input_feature_num = input_feature_num
      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.teacher_forcing = teacher_forcing
      self.decoder_inputs = Reshape(
          (1, output_feature_num), input_shape=(output_feature_num,)
      )
      self.encoder = Encoder(
          input_feature_num, lstm_hidden_dim, lstm_layer_num, dropout
      )
      self.decoder = Decoder(
          output_feature_num, lstm_hidden_dim, lstm_layer_num, dropout
      )
      self.fc = Dense(output_feature_num)
  
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 LSTMSeq2Seq object from inherited base classes: Model with example: obj = LSTMSeq2Seq(future_seq_len, input_feature_num, output_feature_num, lstm_hidden_dim, lstm_layer_num, dropout, teacher_forcing)