class_docstr: A PPO Agent.
class_docstr_tok: ["A","PPO","Agent","."]
code: def adaptive_kl_loss(
      self, kl_divergence: types.Tensor, debug_summaries: bool = False
  ) -> types.Tensor:
      if self._adaptive_kl_beta is None:
          return tf.constant(
              0.0, dtype=tf.float32, name="zero_adaptive_kl_loss"
          )
  
      # Define the loss computation, which depends on the update computation.
      mean_kl = tf.reduce_mean(input_tensor=kl_divergence)
      adaptive_kl_loss = self._adaptive_kl_beta * mean_kl
  
      if debug_summaries:
          tf.compat.v2.summary.scalar(
              name="adaptive_kl_loss",
              data=adaptive_kl_loss,
              step=self.train_step_counter,
          )
  
      return adaptive_kl_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 17 for calling the PPOAgent obj's underlying member method code with example usage: obj.adaptive_kl_loss(kl_divergence, debug_summaries) and returns: adaptive_kl_loss, tf