code: def compute_and_clip_gradients(
      policy: Policy, optimizer: LocalOptimizer, loss: TensorType
  ) -> ModelGradients:
      """Gradients computing function (from loss tensor, using local optimizer).
  
      Note: For SAC, optimizer and loss are ignored b/c we have 3
      losses and 3 local optimizers (all stored in policy).
      `optimizer` will be used, though, in the tf-eager case b/c it is then a
      fake optimizer (OptimizerWrapper) object with a `tape` property to
      generate a GradientTape object for gradient recording.
  
      Args:
          policy: The Policy object that generated the loss tensor and
              that holds the given local optimizer.
          optimizer: The tf (local) optimizer object to
              calculate the gradients with.
          loss: The loss tensor for which gradients should be
              calculated.
  
      Returns:
          ModelGradients: List of the possibly clipped gradients- and variable
              tuples.
      """
      # Eager: Use GradientTape (which is a property of the `optimizer` object
      # (an OptimizerWrapper): see rllib/policy/eager_tf_policy.py).
      if policy.config["framework"] == "tf2":
          tape = optimizer.tape
          pol_weights = policy.model.policy_variables()
          actor_grads_and_vars = list(
              zip(
                  tape.gradient(policy.actor_loss, pol_weights),
                  pol_weights,
              )
          )
          q_weights = policy.model.q_variables()
          if policy.config["twin_q"]:
              half_cutoff = len(q_weights) // 2
              grads_1 = tape.gradient(
                  policy.critic_loss[0], q_weights[:half_cutoff]
              )
              grads_2 = tape.gradient(
                  policy.critic_loss[1], q_weights[half_cutoff:]
              )
              critic_grads_and_vars = list(
                  zip(grads_1, q_weights[:half_cutoff])
              ) + list(zip(grads_2, q_weights[half_cutoff:]))
          else:
              critic_grads_and_vars = list(
                  zip(
                      tape.gradient(policy.critic_loss[0], q_weights),
                      q_weights,
                  )
              )
  
          alpha_vars = [policy.model.log_alpha]
          alpha_grads_and_vars = list(
              zip(
                  tape.gradient(policy.alpha_loss, alpha_vars),
                  alpha_vars,
              )
          )
      # Tf1.x: Use optimizer.compute_gradients()
      else:
          actor_grads_and_vars = (
              policy._actor_optimizer.compute_gradients(
                  policy.actor_loss,
                  var_list=policy.model.policy_variables(),
              )
          )
  
          q_weights = policy.model.q_variables()
          if policy.config["twin_q"]:
              half_cutoff = len(q_weights) // 2
              (
                  base_q_optimizer,
                  twin_q_optimizer,
              ) = policy._critic_optimizer
              critic_grads_and_vars = (
                  base_q_optimizer.compute_gradients(
                      policy.critic_loss[0],
                      var_list=q_weights[:half_cutoff],
                  )
                  + twin_q_optimizer.compute_gradients(
                      policy.critic_loss[1],
                      var_list=q_weights[half_cutoff:],
                  )
              )
          else:
              critic_grads_and_vars = policy._critic_optimizer[
                  0
              ].compute_gradients(
                  policy.critic_loss[0], var_list=q_weights
              )
          alpha_grads_and_vars = (
              policy._alpha_optimizer.compute_gradients(
                  policy.alpha_loss, var_list=[policy.model.log_alpha]
              )
          )
  
      # Clip if necessary.
      if policy.config["grad_clip"]:
          clip_func = partial(
              tf.clip_by_norm, clip_norm=policy.config["grad_clip"]
          )
      else:
          clip_func = tf.identity
  
      # Save grads and vars for later use in `build_apply_op`.
      policy._actor_grads_and_vars = [
          (clip_func(g), v)
          for (g, v) in actor_grads_and_vars
          if g is not None
      ]
      policy._critic_grads_and_vars = [
          (clip_func(g), v)
          for (g, v) in critic_grads_and_vars
          if g is not None
      ]
      policy._alpha_grads_and_vars = [
          (clip_func(g), v)
          for (g, v) in alpha_grads_and_vars
          if g is not None
      ]
  
      grads_and_vars = (
          policy._actor_grads_and_vars
          + policy._critic_grads_and_vars
          + policy._alpha_grads_and_vars
      )
      return grads_and_vars
  
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 6 for calling a global function with example usage: compute_and_clip_gradients(policy, optimizer, loss) and returns: grads_and_vars