def get(self, id, pk='id', deleted=False):
        try:
            if pk == 'id':
                id = int(id)
            if deleted:
                spec = {pk: id}
            else:
                spec = {pk: id, 'data': {'$exists': 1}}
            fields = {'_id': 0}
            row = self.db.docs.find_one(spec, fields)
        except Exception as e:
            raise DatabaseException(e)
        if row:
            return Document(**row)
        raise NotFoundError('%s %s not found' % (pk, id))
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