def _extract_log_probs(num_states, dist):
  """"""

  states = tf.reshape(tf.range(num_states),
                      tf.concat([[num_states],
                                 tf.ones_like(dist.batch_shape_tensor())],
                                axis=0))
  return distribution_util.move_dimension(dist.log_prob(states), 0, -1)