def get_stats_result(self, request):
        """Return the StatsResult object for this statistic"""
        stats_datasets = StatsMakerDataverses(**request.GET.dict())

        pub_state = self.get_pub_state(request)

        if pub_state == self.PUB_STATE_ALL:
            stats_result = stats_datasets.get_dataverse_affiliation_counts()
        elif pub_state == self.PUB_STATE_UNPUBLISHED:
            stats_result = stats_datasets.get_dataverse_affiliation_counts_unpublished()
        else:
            stats_result = stats_datasets.get_dataverse_affiliation_counts_published()

        return stats_result
def update_model(model, disc_grads, gen_grads, disc_opt, gen_opt,
                 global_step, update_scale):
  """Update model with gradients."""

  disc_vars, gen_vars = model.get_variables()

  with tf.control_dependencies(gen_grads + disc_grads):
    disc_update_op = disc_opt.apply_gradients(
        zip(_scale_vars(update_scale, disc_grads),
            disc_vars))

    gen_update_op = gen_opt.apply_gradients(
        zip(_scale_vars(update_scale, gen_grads),
            gen_vars),
        global_step=global_step)

    update_op = tf.group([disc_update_op, gen_update_op])

  return update_op