def test_iadd(self, evt):
        rval = event.ReturnValue(append_events=[evt])
        rval2 = event.ReturnValue(eat=True, append_events=[evt])
        rset = event.ResultSet()

        rset += rval
        rset += rval2
        rset += None
        assert rset.eat
        assert rset.append_events == [evt, evt]
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