import tensorflow as tf

                if self.lesion_omega:
                    setattr(
                        self,
                        'omega_%s' % layer,
                        tf.constant(0.))
                if self.lesion_kappa:
                    setattr(
                        self,
                        'kappa_%s' % layer,
                        tf.constant(0.))
                if self.reuse:
                    # Make the batchnorm variables
                    scopes = ['g1_bn', 'g2_bn', 'c1_bn', 'c2_bn']
                    bn_vars = ['moving_mean', 'moving_variance', 'gamma']
                    for s in scopes:
                        with tf.variable_scope(s):
                            for v in bn_vars:
                                tf.get_variable(
                                    trainable=self.param_trainable[v],
                                    name=v,
                                    dtype=self.dtype,
                                    shape=[self.hgru_k[idx]],
                                    collections=self.param_collections[v],
                                    initializer=self.param_initializer[v])
                    self.param_initializer = None

    def resize_x_to_y(
            self,
            x,
            y,
