'Create the deterministic transformation between stochastic layers.
If self.hparam.nonlinear:
2 x tanh layers
Else:
1 x linear layer'

def _create_transformation(self, input, n_output, reuse, scope_prefix):

    if self.hparams.nonlinear:
        h = slim.fully_connected(input, self.hparams.n_hidden, reuse=reuse, activation_fn=tf.nn.tanh, scope=('%s_nonlinear_1' % scope_prefix))
        h = slim.fully_connected(h, self.hparams.n_hidden, reuse=reuse, activation_fn=tf.nn.tanh, scope=('%s_nonlinear_2' % scope_prefix))
        h = slim.fully_connected(h, n_output, reuse=reuse, activation_fn=None, scope=('%s' % scope_prefix))
    else:
        h = slim.fully_connected(input, n_output, reuse=reuse, activation_fn=None, scope=('%s' % scope_prefix))
    return h
