class_docstr: A PPO Agent.
class_docstr_tok: ["A","PPO","Agent","."]
code: def kl_cutoff_loss(
      self, kl_divergence: types.Tensor, debug_summaries: bool = False
  ) -> types.Tensor:
      # Squared penalization for mean KL divergence above some threshold.
      if self._kl_cutoff_factor <= 0.0:
          return tf.constant(
              0.0, dtype=tf.float32, name="zero_kl_cutoff_loss"
          )
      kl_cutoff = self._kl_cutoff_factor * self._adaptive_kl_target
      mean_kl = tf.reduce_mean(input_tensor=kl_divergence)
      kl_over_cutoff = tf.maximum(mean_kl - kl_cutoff, 0.0)
      kl_cutoff_loss = self._kl_cutoff_coef * tf.square(kl_over_cutoff)
  
      if debug_summaries:
          tf.compat.v2.summary.scalar(
              name="kl_cutoff_count",
              data=tf.reduce_sum(
                  input_tensor=tf.cast(
                      kl_divergence > kl_cutoff, dtype=tf.int64
                  )
              ),
              step=self.train_step_counter,
          )
          tf.compat.v2.summary.scalar(
              name="kl_cutoff_loss",
              data=kl_cutoff_loss,
              step=self.train_step_counter,
          )
  
      return tf.identity(kl_cutoff_loss, name="kl_cutoff_loss")
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from typing import Optional, Text, Tuple
  from absl import logging
  import gin
  from six.moves import range
  from six.moves import zip
  import tensorflow
  from tf_agents.agents import data_converter
  from tf_agents.agents import tf_agent
  from tf_agents.agents.ppo import ppo_policy
  from tf_agents.agents.ppo import ppo_utils
  from tf_agents.networks import network
  from tf_agents.policies import greedy_policy
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import eager_utils
  from tf_agents.utils import nest_utils
  from tf_agents.utils import object_identity
  from tf_agents.utils import tensor_normalizer
  from tf_agents.utils import value_ops
label_desc: Use image node_id 16 for calling the PPOAgent obj's underlying member method code with example usage: obj.kl_cutoff_loss(kl_divergence, debug_summaries) and returns: tf, tf