code: def warn_if_infinite_kl_divergence(
      policy: "TorchPolicy",
      kl_divergence: TensorType,
  ) -> None:
      if policy.loss_initialized() and kl_divergence.isinf():
          logger.warning(
              "KL divergence is non-finite, this will likely destabilize your model and"
              " the training process. Action(s) in a specific state have near-zero"
              " probability. This can happen naturally in deterministic environments"
              " where the optimal policy has zero mass for a specific action. To fix this"
              " issue, consider setting the coefficient for the KL loss term to zero or"
              " increasing policy entropy."
          )
  
imports: import logging
  import os
  import warnings
  from typing import Dict, List, Optional, TYPE_CHECKING, Union
  import gymnasium
  import numpy
  import tree
  from gymnasium.spaces import Discrete, MultiDiscrete
  from packaging import version
  import ray
  from ray.rllib.models.repeated_values import RepeatedValues
  from ray.rllib.utils.annotations import Deprecated, PublicAPI, DeveloperAPI
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import SMALL_NUMBER
  from ray.rllib.utils.typing import LocalOptimizer, SpaceStruct, TensorStructType, TensorType
label_desc: Use image node_id 18 for calling a global function with example usage: warn_if_infinite_kl_divergence(policy, kl_divergence) without return types