code: def clip_gradients(
      policy: Policy,
      optimizer: "tf.keras.optimizers.Optimizer",
      loss: TensorType,
  ) -> ModelGradients:
      if not hasattr(policy, "q_func_vars"):
          policy.q_func_vars = policy.model.variables()
  
      return minimize_and_clip(
          optimizer,
          loss,
          var_list=policy.q_func_vars,
          clip_val=policy.config["grad_clip"],
      )
  
imports: from typing import Dict
  import gymnasium
  import numpy
  import ray
  from ray.rllib.algorithms.dqn.distributional_q_tf_model import DistributionalQTFModel
  from ray.rllib.algorithms.simple_q.utils import Q_SCOPE, Q_TARGET_SCOPE
  from ray.rllib.evaluation.postprocessing import adjust_nstep
  from ray.rllib.models import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import get_categorical_class_with_temperature
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import LearningRateSchedule, TargetNetworkMixin
  from ray.rllib.policy.tf_policy_template import build_tf_policy
  from ray.rllib.utils.error import UnsupportedSpaceException
  from ray.rllib.utils.exploration import ParameterNoise
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.tf_utils import huber_loss, l2_loss, make_tf_callable, minimize_and_clip, reduce_mean_ignore_inf
  from ray.rllib.utils.typing import AlgorithmConfigDict, ModelGradients, TensorType
label_desc: Use image node_id 5 for calling a global function with example usage: clip_gradients(policy, optimizer, loss) and returns: minimize_and_clip