def resample_bias_noise(self, batch_size_changed=False):
        if batch_size_changed:
            self.resample_fn = None

        if self.resample_fn is None:
            updates = OrderedDict()

            if self.sampling_b_stdev is not None:
                self.noisy_sampling_b = \
                    sharedX(np.zeros((self.dbm.batch_size, self.dim)))

            if self.noisy_sampling_b is not None:
                theano_rng = make_theano_rng(
                    None,
                    self.dbm.rng.randint(2**16),
                    which_method="normal"
                )

                b = self.boltzmann_b
                if self.layer_above is not None:
                    W_above = self.layer_above.W
                    ising_b = 0.5 * b + 0.25 * self.W.sum(axis=0) \
                                      + 0.25 * W_above.sum(axis=1)
                else:
                    ising_b = 0.5 * b + 0.25 * self.W.sum(axis=0)

                noisy_sampling_b = \
                    theano_rng.normal(avg=ising_b.dimshuffle('x', 0),
                                      std=self.sampling_b_stdev,
                                      size=self.noisy_sampling_b.shape,
                                      dtype=ising_b.dtype)
                updates[self.noisy_sampling_b] = noisy_sampling_b

            self.resample_fn = function([], updates=updates)

        self.resample_fn()
Return token type
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/models/dbm/ising.py/BoltzmannIsingHidden.resample_bias_noise--crossover with--dataset/ETHPy150Open rohe/pyoidc/src/oic/extension/token.py/JWTToken.get_type