text: import tensorflow as tf
  from tensorflow.examples.tutorials.mnist import input_data
  from tensorflow.contrib import rnn
  
  mnist = input_data.read_data_sets("temp/data/", one_hot=True)
  
  hm_epochs = 10
  n_classes = 10
  batch_size = 128
  chunk_size = 28
  n_chunks = 28
  rnn_size = 128
  
  X = tf.placeholder('float', [None, n_chunks, chunk_size])
  y = tf.placeholder('float')
  
  
  def recurrent_neural_network(x):
      layer = {'weights': tf.Variable(tf.random_normal([rnn_size, n_classes])),
               'biases': tf.Variable(tf.random_normal([n_classes]))}
      x = tf.transpose(x, [1, 0, 2])
      x = tf.reshape(x, [-1, chunk_size])
      x = tf.split(x, n_chunks, 0)
      lstm_cell = rnn.BasicLSTMCell(rnn_size)
      outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
      output = tf.matmul(outputs[-1], layer['weights']) + layer['biases']
      return output
  
  
  def train_neural_network(x):
      prediction = recurrent_neural_network(x)
      cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
      optimizer = tf.train.AdamOptimizer().minimize(cost)
      with tf.Session() as sess:
          sess.run(tf.global_variables_initializer())
          for epoch in range(hm_epochs):
              epoch_loss = 0
              for _ in range(int(mnist.train.num_examples / batch_size)):
                  epoch_x, epoch_y = mnist.train.next_batch(batch_size)
                  epoch_x = epoch_x.reshape((batch_size, n_chunks, chunk_size))
                  _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
                  epoch_loss += c
              print('Epoch', epoch + 1, 'completed out of', hm_epochs, 'loss: ', epoch_loss)
          correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
          accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
          print('Accuracy', accuracy.eval({x: mnist.test.images.reshape((-1, n_chunks, chunk_size)),
                                           y: mnist.test.labels}))
  
  train_neural_network(X)