code: def validate_config(config: AlgorithmConfigDict) -> None:
      """Executed before Policy is "initialized" (at beginning of constructor).
      Args:
          config: The Policy's config.
      """
      # If vf_share_layers is True, inform about the need to tune vf_loss_coeff.
      if config.get("model", {}).get("vf_share_layers") is True:
          logger.info(
              "`vf_share_layers=True` in your model. "
              "Therefore, remember to tune the value of `vf_loss_coeff`!"
          )
  
file_path: ray/python/ray/rllib/algorithms/ppo/ppo_tf_policy.py
imports: import logging
  from typing import Dict, List, Type, Union
  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.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 EntropyCoeffSchedule, KLCoeffMixin, LearningRateSchedule, ValueNetworkMixin
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.tf_utils import explained_variance, warn_if_infinite_kl_divergence
  from ray.rllib.utils.typing import AlgorithmConfigDict, TensorType, TFPolicyV2Type
label_desc: Use image node_id 1 for calling a global function with example usage: validate_config(config) without return types