code: def update_kls(self, sampled_kls):
      for i, kl in enumerate(sampled_kls):
          if kl < self.kl_target / 1.5:
              self.kl_coeff_val[i] *= 0.5
          elif kl > 1.5 * self.kl_target:
              self.kl_coeff_val[i] *= 2.0
      print(self.kl_coeff_val)
      self.kl_coeff.load(self.kl_coeff_val, session=self.get_session())
      return self.kl_coeff_val
  
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 2 for calling the KLCoeffMixin obj's underlying member method code with example usage: obj.update_kls(sampled_kls) and returns: self