code: def apply_gradients(
      policy: Policy,
      optimizer: LocalOptimizer,
      grads_and_vars: ModelGradients,
  ) -> Union["tf.Operation", None]:
      """Gradients applying function (from list of "grad_and_var" tuples).
  
      Note: For SAC, optimizer and grads_and_vars are ignored b/c we have 3
      losses and optimizers (stored in policy).
  
      Args:
          policy: The Policy object whose Model(s) the given gradients
              should be applied to.
          optimizer: The tf (local) optimizer object through
              which to apply the gradients.
          grads_and_vars: The list of grad_and_var tuples to
              apply via the given optimizer.
  
      Returns:
          Union[tf.Operation, None]: The tf op to be used to run the apply
              operation. None for eager mode.
      """
      actor_apply_ops = policy._actor_optimizer.apply_gradients(
          policy._actor_grads_and_vars
      )
  
      cgrads = policy._critic_grads_and_vars
      half_cutoff = len(cgrads) // 2
      if policy.config["twin_q"]:
          critic_apply_ops = [
              policy._critic_optimizer[0].apply_gradients(
                  cgrads[:half_cutoff]
              ),
              policy._critic_optimizer[1].apply_gradients(
                  cgrads[half_cutoff:]
              ),
          ]
      else:
          critic_apply_ops = [
              policy._critic_optimizer[0].apply_gradients(cgrads)
          ]
  
      # Eager mode -> Just apply and return None.
      if policy.config["framework"] == "tf2":
          policy._alpha_optimizer.apply_gradients(
              policy._alpha_grads_and_vars
          )
          return
      # Tf static graph -> Return op.
      else:
          alpha_apply_ops = policy._alpha_optimizer.apply_gradients(
              policy._alpha_grads_and_vars,
              global_step=tf1.train.get_or_create_global_step(),
          )
          return tf.group(
              [actor_apply_ops, alpha_apply_ops] + critic_apply_ops
          )
  
imports: import copy
  import gymnasium
  from gymnasium.spaces import Box, Discrete
  from functools import partial
  import logging
  from typing import Dict, List, Optional, Tuple, Type, Union
  import ray
  import ray.experimental.tf_utils
  from ray.rllib.algorithms.dqn.dqn_tf_policy import postprocess_nstep_and_prio, PRIO_WEIGHTS
  from ray.rllib.algorithms.sac.sac_tf_model import SACTFModel
  from ray.rllib.algorithms.sac.sac_torch_model import SACTorchModel
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.models import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import Beta, Categorical, DiagGaussian, Dirichlet, SquashedGaussian, TFActionDistribution
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import TargetNetworkMixin
  from ray.rllib.policy.tf_policy_template import build_tf_policy
  from ray.rllib.utils.error import UnsupportedSpaceException
  from ray.rllib.utils.framework import get_variable, try_import_tf
  from ray.rllib.utils.spaces.simplex import Simplex
  from ray.rllib.utils.tf_utils import huber_loss, make_tf_callable
  from ray.rllib.utils.typing import AgentID, LocalOptimizer, ModelGradients, TensorType, AlgorithmConfigDict
label_desc: Use image node_id 7 for calling a global function with example usage: apply_gradients(policy, optimizer, grads_and_vars) and returns: tf