code: def __init__(self, config):
      self.kl_coeff_val = [config["kl_coeff"]] * config[
          "inner_adaptation_steps"
      ]
      self.kl_target = self.config["kl_target"]
      self.kl_coeff = tf1.get_variable(
          initializer=tf.keras.initializers.Constant(self.kl_coeff_val),
          name="kl_coeff",
          shape=(config["inner_adaptation_steps"]),
          trainable=False,
          dtype=tf.float32,
      )
  
imports: import logging
  from typing import Dict, List, Type, Union
  from ray.rllib.algorithms.ppo.ppo_tf_policy import validate_config
  from ray.rllib.evaluation.postprocessing import Postprocessing, compute_gae_for_sample_batch
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
  from ray.rllib.models.utils import get_activation_fn
  from ray.rllib.policy.dynamic_tf_policy_v2 import DynamicTFPolicyV2
  from ray.rllib.policy.eager_tf_policy_v2 import EagerTFPolicyV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import LocalOptimizer, ModelGradients, ValueNetworkMixin, compute_gradients
  from ray.rllib.utils import try_import_tf
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 1 to create a new KLCoeffMixin object  with example: obj = KLCoeffMixin(config)