new_contents: import tensorflow as tf
  import numpy as np
  
  tf.set_random_seed(5)
  
  n_inputs = 28
  n_neurons = 150
  n_layers = 3
  n_steps = 28
  n_outputs = 10
  
  learning_rate = 0.001
  
  from tensorflow.examples.tutorials.mnist import input_data
  mnist = input_data.read_data_sets("/tmp/data/")
  X_test = mnist.test.images.reshape((-1, n_steps, n_inputs))
  y_test = mnist.test.labels
  
  X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
  y = tf.placeholder(tf.int32, [None])
  
  multi_cell = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons) for _ in range(3)])
  outputs, states = tf.nn.dynamic_rnn(multi_cell, X, dtype=tf.float32)
  top_layer_h_state = states[-1][1]
  logits = tf.layers.dense(top_layer_h_state, n_outputs, name='softmax')
  x_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
  loss = tf.reduce_mean(x_entropy, name='loss')
  optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
  training_op = optimizer.minimize(loss)
  correct = tf.nn.in_top_k(logits, y, 1)
  accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
  
  init = tf.global_variables_initializer()
  
  states
  
  top_layer_h_state
  
  n_epochs = 25
  batch_size = 150
  
  with tf.Session() as sess:
      init.run()
      for epoch in range(n_epochs):
          for k in range(mnist.train.num_examples // batch_size):
              X_batch, y_batch = mnist.train.next_batch(batch_size)
              X_batch = X_batch.reshape((batch_size, n_steps, n_inputs))
              sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
          acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})
          acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})
          print("Epoch", epoch, 'Train acc: ', acc_train, "Test acc: ", acc_test)
subject: Add code for LSTM based MNIST