class_docstr: Assigns the `update_kl()` and other KL-related methods to a TFPolicy.
  
  This is used in Algorithms to update the KL coefficient after each
  learning step based on `config.kl_target` and the measured KL value
  (from the train_batch).
code: def get_state(self) -> PolicyState:
      state = super().get_state()
      # Add current kl-coeff value.
      state["current_kl_coeff"] = self.kl_coeff_val
      return state
  
imports: import logging
  from typing import Dict, List
  import numpy
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy.eager_tf_policy import EagerTFPolicy
  from ray.rllib.policy.eager_tf_policy_v2 import EagerTFPolicyV2
  from ray.rllib.policy.policy import Policy, PolicyState
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_policy import TFPolicy
  from ray.rllib.utils.annotations import DeveloperAPI, override
  from ray.rllib.utils.framework import get_variable, try_import_tf
  from ray.rllib.utils.schedules import PiecewiseSchedule
  from ray.rllib.utils.tf_utils import make_tf_callable
  from ray.rllib.utils.typing import AlgorithmConfigDict, LocalOptimizer, ModelGradients, TensorType
label_desc: Use image node_id 4 for calling the KLCoeffMixin obj's underlying member method code with example usage: obj.get_state() and returns: state