def get_last_tid(self):
        try:
            spec = {'tid': {'$gt': 0}}
            sort = [('tid', -1)]
            row = self.db.docs.find_one(spec, sort=sort)
        except Exception as e:
            raise DatabaseException(e)
        if row:
            return row['tid']
        return 0
def _compute_reg_grads(gen_grads, disc_vars):
  """Compute gradients norm (this is an upper-bpund of the full-batch norm)."""
  gen_norm = tf.accumulate_n([tf.reduce_sum(u * u) for u in gen_grads])
  disc_reg_grads = tf.gradients(gen_norm, disc_vars)
  return disc_reg_grads