import tensorflow as tf

        cell_fw = GetCell()
      with tf.variable_scope('bw'):
        cell_bw = GetCell()

      rnnout, _, _ = tf.nn.bidirectional_rnn(cell_fw, cell_bw, self._inputs,
                                             dtype=tf.float32,
                                             sequence_length=self.seq_lens)
      if proj_size:
        out_size = 2 * proj_size
      else:
        out_size = 2 * hidden_size
      super(TweetSeqModel, self)._DoPredictions(out_size, rnnout, class_weights=weights)

      self.cost = tf.reduce_mean(self.example_weights * self._xent)


class CharSeqModel(object): #formerly TweetSeqModel
  """
  Treats each document (tweet) as a single "word," which is fed through c2v,
  and the output "embedding" sized to be a vector of language predictions.
  """
  def __init__(self, out_vocab_size=None,
               batch_size=10, model_params=None, c2v=None,
               max_sequence_len=None,
               dropout_keep_prob=None,
               weights=None):
