code: def minimize_and_clip(
      optimizer: "torch.optim.Optimizer", clip_val: float = 10.0
  ) -> None:
      """Clips grads found in `optimizer.param_groups` to given value in place.
  
      Ensures the norm of the gradients for each variable is clipped to
      `clip_val`.
  
      Args:
          optimizer: The torch.optim.Optimizer to get the variables from.
          clip_val: The global norm clip value. Will clip around -clip_val and
              +clip_val.
      """
      # Loop through optimizer's variables and norm per variable.
      for param_group in optimizer.param_groups:
          for p in param_group["params"]:
              if p.grad is not None:
                  torch.nn.utils.clip_grad_norm_(p.grad, clip_val)
  
imports: import logging
  import os
  import warnings
  from typing import Dict, List, Optional, TYPE_CHECKING, Union
  import gymnasium
  import numpy
  import tree
  from gymnasium.spaces import Discrete, MultiDiscrete
  from packaging import version
  import ray
  from ray.rllib.models.repeated_values import RepeatedValues
  from ray.rllib.utils.annotations import Deprecated, PublicAPI, DeveloperAPI
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import SMALL_NUMBER
  from ray.rllib.utils.typing import LocalOptimizer, SpaceStruct, TensorStructType, TensorType
label_desc: Use image node_id 14 for calling a global function with example usage: minimize_and_clip(optimizer, clip_val) without return types