code: def __init__(
      self, input_dim, hidden_dim, layer_num, dropout, output_dim
  ):
      super(LSTMModel, self).__init__()
      self.input_dim = input_dim
      self.hidden_dim = hidden_dim
      self.layer_num = layer_num
      self.output_dim = output_dim
      self.dropout = dropout
      self.lstm_sequential = Sequential(
          [Input(shape=(None, self.input_dim))]
      )
      for layer in range(self.layer_num - 1):
          self.lstm_sequential.add(
              LSTM(
                  self.hidden_dim[layer],
                  return_sequences=True,
                  dropout=self.dropout[layer],
                  name="lstm_" + str(layer + 1),
              )
          )
      self.lstm_sequential.add(
          LSTM(
              self.hidden_dim[-1],
              dropout=dropout[-1],
              name="lstm_" + str(layer_num),
          )
      )
      self.lstm_sequential.add(Dense(self.output_dim))
      self.lstm_sequential.add(
          Reshape((1, self.output_dim), input_shape=(self.output_dim,))
      )
  
file_path: BigDL/python/chronos/src/bigdl/chronos/model/tf2/VanillaLSTM_keras.py
imports: import tensorflow
  from bigdl.nano.tf.keras import Sequential, Model
  from tensorflow.keras.layers import LSTM, Reshape, Dense, Input
label_desc: Use image node_id 1 to create a new LSTMModel object from inherited base classes: Model with example: obj = LSTMModel(input_dim, hidden_dim, layer_num, dropout, output_dim)