code: def apply_gradients_fn(policy, optimizer, grads_and_vars):
      sac_results = sac_apply_gradients(
          policy, optimizer, grads_and_vars
      )
  
      if policy.config["lagrangian"]:
          # Eager mode -> Just apply and return None.
          if policy.config["framework"] == "tf2":
              policy._alpha_prime_optimizer.apply_gradients(
                  policy._alpha_prime_grads_and_vars
              )
              return
          # Tf static graph -> Return grouped op.
          else:
              alpha_prime_apply_op = (
                  policy._alpha_prime_optimizer.apply_gradients(
                      policy._alpha_prime_grads_and_vars,
                      global_step=tf1.train.get_or_create_global_step(),
                  )
              )
              return tf.group([sac_results, alpha_prime_apply_op])
      return sac_results
  
imports: from functools import partial
  import numpy
  import gymnasium
  import logging
  import tree
  from typing import Dict, List, Type, Union
  import ray
  import ray.experimental.tf_utils
  from ray.rllib.algorithms.sac.sac_tf_policy import apply_gradients, compute_and_clip_gradients, get_distribution_inputs_and_class, _get_dist_class, build_sac_model, postprocess_trajectory, setup_late_mixins, stats, validate_spaces, ActorCriticOptimizerMixin, ComputeTDErrorMixin
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
  from ray.rllib.policy.tf_mixins import TargetNetworkMixin
  from ray.rllib.policy.tf_policy_template import build_tf_policy
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.exploration.random import Random
  from ray.rllib.utils.framework import get_variable, try_import_tf, try_import_tfp
  from ray.rllib.utils.typing import LocalOptimizer, ModelGradients, TensorType, AlgorithmConfigDict
label_desc: Use image node_id 8 for calling a global function with example usage: apply_gradients_fn(policy, optimizer, grads_and_vars) and returns: sac_results, tf