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
      return self.kl_coeff_val
  
imports: import logging
  from typing import Dict, List, Type, Union
  import ray
  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.torch.torch_action_dist import TorchDistributionWrapper
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import ValueNetworkMixin
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.torch_utils import apply_grad_clipping
  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