text: import numpy as np
  import tensorflow as tf
  from dnc.controller import BaseController
  
  """
  A 1-layer LSTM recurrent neural network with 256 hidden units
  Note: the state of the LSTM is not saved in a variable because we want
  the state to reset to zero on every input sequence
  """
  
  
  class RecurrentController(BaseController):
  
      def network_vars(self):
          self.lstm_cell = tf.compat.v1.nn.rnn_cell.BasicLSTMCell(256)
          self.state = self.lstm_cell.zero_state(self.batch_size, tf.float32)
  
      def network_op(self, X, state):
          X = tf.convert_to_tensor(value=X)
          return self.lstm_cell(X, state)
  
      def get_state(self):
          return self.state
  
      @staticmethod
      def update_state(new_state):
          return tf.no_op()
  