code: def compute_gradients_fn(
      policy: Policy, optimizer: LocalOptimizer, loss: TensorType
  ) -> ModelGradients:
      grads_and_vars = sac_compute_and_clip_gradients(
          policy, optimizer, loss
      )
  
      if policy.config["lagrangian"]:
          # 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
              log_alpha_prime = [policy.model.log_alpha_prime]
              alpha_prime_grads_and_vars = list(
                  zip(
                      tape.gradient(
                          policy.alpha_prime_loss, log_alpha_prime
                      ),
                      log_alpha_prime,
                  )
              )
          # Tf1.x: Use optimizer.compute_gradients()
          else:
              alpha_prime_grads_and_vars = (
                  policy._alpha_prime_optimizer.compute_gradients(
                      policy.alpha_prime_loss,
                      var_list=[policy.model.log_alpha_prime],
                  )
              )
  
          # 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._alpha_prime_grads_and_vars = [
              (clip_func(g), v)
              for (g, v) in alpha_prime_grads_and_vars
              if g is not None
          ]
  
          grads_and_vars += policy._alpha_prime_grads_and_vars
      return grads_and_vars
  
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 7 for calling a global function with example usage: compute_gradients_fn(policy, optimizer, loss) and returns: grads_and_vars