message: Migrate text_classification.py from .contrib utils to .core.
  Some usages are left untouched: datasets, VocabularyProcessor.  A tracking bug is filed for embed_sequence.
  Tested by re-running and the loss numbers look similar to the ones before the change.
diff: @@ -91,11 +91,11 @@ def rnn_model(features, labels, mode):
  word_list = tf.unstack(word_vectors, axis=1)
  # Create a Gated Recurrent Unit cell with hidden size of EMBEDDING_SIZE.
  - cell = tf.contrib.rnn.GRUCell(EMBEDDING_SIZE)
  + cell = tf.nn.rnn_cell.GRUCell(EMBEDDING_SIZE)
  # Create an unrolled Recurrent Neural Networks to length of
  # MAX_DOCUMENT_LENGTH and passes word_list as inputs for each unit.
  - _, encoding = tf.contrib.rnn.static_rnn(cell, word_list, dtype=tf.float32)
  + _, encoding = tf.nn.static_rnn(cell, word_list, dtype=tf.float32)
  # Given encoding of RNN, take encoding of last step (e.g hidden size of the
  # neural network of last step) and pass it as features for softmax
  @@ -107,6 +107,8 @@ def rnn_model(features, labels, mode):
  def main(unused_argv):
  global n_words
  + tf.logging.set_verbosity(tf.logging.INFO)
  +
  # Prepare training and testing data
  dbpedia = tf.contrib.learn.datasets.load_dataset(
  'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data)
  