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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _adjust_penalty(self, observ, old_policy_params, length):
 """Adjust the KL policy between the behavioral and current policy. Compute how much the policy actually changed during the multiple update steps. Adjust the penalty strength for the next training phase if we overshot or undershot the target divergence too much. Args: observ: Sequences of observations. old_policy_params: Parameters of the behavioral policy. length: Batch of sequence lengths. Returns: Summary tensor. """

    old_policy = self._policy_type(**old_policy_params)
    with tf.name_scope('adjust_penalty'):
      network = self._network(observ, length)
      print_penalty = tf.Print(0, [self._penalty], 'current penalty: ')
      with tf.control_dependencies([print_penalty]):
        kl_change = tf.reduce_mean(self._mask(
            tf.contrib.distributions.kl_divergence(old_policy, network.policy),
            length))
        kl_change = tf.Print(kl_change, [kl_change], 'kl change: ')
        maybe_increase = tf.cond(
            kl_change > 1.3 * self._config.kl_target,
            # pylint: disable=g-long-lambda
            lambda: tf.Print(self._penalty.assign(
                self._penalty * 1.5), [0], 'increase penalty '),
            float)
        maybe_decrease = tf.cond(
            kl_change < 0.7 * self._config.kl_target,
            # pylint: disable=g-long-lambda
            lambda: tf.Print(self._penalty.assign(
                self._penalty / 1.5), [0], 'decrease penalty '),
            float)
      with tf.control_dependencies([maybe_increase, maybe_decrease]):
        return tf.summary.merge([
            tf.summary.scalar('kl_change', kl_change),
            tf.summary.scalar('penalty', self._penalty)])