import tensorflow as tf

               dropout_keep_prob=None,
               weights=None):

    super(WordAvgModel, self).__init__(batch_size, max_sequence_len,
                                       out_vocab_size, c2v)

    super(WordAvgModel, self)._DoPredictions(c2v.embedding_dims,
                                            self._inputs)
    self.cost = tf.reduce_mean(self.example_weights * self._xent)


class WordSeqModel(BaseModel):
  """A bag of word embeddings."""
  def __init__(self, out_vocab_size=None,
               batch_size=10,
               model_params=None,
