new_contents: import tensorflow as tf
  import numpy as np
  
  #First Option
  
  keep_prob = 0.5
  
  cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)
  cell_drop = tf.contrib.rnn.DropoutWrapper(cell, input_keep_prob=keep_prob)
  multi_layer_cell = tf.contrib.rnn.MultiRNNCell([cell_drop] * n_layers)
  rnn_outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)
  
  # Second option
  import sys
  is_training = (sys.argv[-1] == 'train')
  
  X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
  y = tf.placeholder(tf.float32, [None, n_steps, n_outputs])
  cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)
  if is_training:
      cell = tf.contrib.rnn.DropoutWrapper(cell, input_keep_prob=keep_prob)
  multi_layer_cell = tf.contrib.rnn.MultiRNNCell([cell] * n_layers)
  rnn_outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)
  
  # [....] rest of graph
  
  init = tf.global_variables_initializer()
  saver = tf.train.Saver()
  
  with tf.Session() as sess:
      if is_training:
          init.run()
          for k in range(n_iterations):
              # [.... train model]
          save_path = saver.save(sess, '/tmp/my_model.ckpt')
      else:
          saver.restore(sess, '/tmp/my_model.ckpt')
          # [...] use model 
subject: Add rudimentary code for dropout RNN