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 __init__(self, config: AlgorithmConfigDict):
      # The current KL value (as python float).
      self.kl_coeff_val = config["kl_coeff"]
      # The current KL value (as tf Variable for in-graph operations).
      self.kl_coeff = get_variable(
          float(self.kl_coeff_val),
          tf_name="kl_coeff",
          trainable=False,
          framework=config["framework"],
      )
      # Constant target value.
      self.kl_target = config["kl_target"]
      if self.framework == "tf":
          self._kl_coeff_placeholder = tf1.placeholder(
              dtype=tf.float32, name="kl_coeff"
          )
          self._kl_coeff_update = self.kl_coeff.assign(
              self._kl_coeff_placeholder, read_value=False
          )
  
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 1 to create a new KLCoeffMixin object  with example: obj = KLCoeffMixin(config)