def expected_energy_term(self, state, average, state_below=None,
                             average_below=None):

        # state = Print('v_state', attrs=['min', 'max'])(state)

        assert state_below is None
        assert average_below is None
        assert average in [True, False]
        self.space.validate(state)

        # Energy function is linear so it doesn't matter if we're averaging
        # or not
        rval = -(self.beta * T.dot(state, self.ising_bias()))

        assert rval.ndim == 1

        return rval
.. todo::
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/models/dbm/ising.py/BoltzmannIsingVisible.expected_energy_term