def sample(self, state_below=None, state_above=None, layer_above=None,
               theano_rng=None):

        assert state_below is None

        msg = layer_above.downward_message(state_above, for_sampling=True)

        bias = self.ising_bias(for_sampling=True)

        z = msg + bias

        phi = T.nnet.sigmoid(2. * self.beta * z)

        rval = theano_rng.binomial(size=phi.shape, p=phi, dtype=phi.dtype, n=1)

        return rval * 2. - 1.
Stage-specific configuration linting.
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/models/dbm/ising.py/BoltzmannIsingVisible.sample--crossover with--dataset/ETHPy150Open GoogleCloudPlatform/Data-Pipeline/app/src/pipelines/stages/gcsoutput.py/GcsOutput.Lint