code: def maybe_add_gradient_clipping(cfg, optimizer):
      """
      If gradient clipping is enabled through config options, wraps the existing
      optimizer instance of some type OptimizerType to become an instance
      of the new dynamically created class OptimizerTypeWithGradientClip
      that inherits OptimizerType and overrides the `step` method to
      include gradient clipping.
  
      Args:
          cfg: CfgNode
              configuration options
          optimizer: torch.optim.Optimizer
              existing optimizer instance
  
      Return:
          optimizer: torch.optim.Optimizer
              either the unmodified optimizer instance (if gradient clipping is
              disabled), or the same instance with adjusted __class__ to override
              the `step` method and include gradient clipping
      """
      if not cfg.CONFIG.TRAIN.CLIP_GRADIENTS.ENABLED:
          return optimizer
      grad_clipper = _create_gradient_clipper(
          cfg.CONFIG.TRAIN.CLIP_GRADIENTS
      )
      OptimizerWithGradientClip = (
          _generate_optimizer_class_with_gradient_clipping(
              type(optimizer), grad_clipper
          )
      )
      optimizer.__class__ = OptimizerWithGradientClip
      return optimizer
  
file_path: gluon-cv/gluoncv/torch/utils/optimizer.py
imports: import math
  from enum import Enum
  import torch
  from torch.optim.optimizer import Optimizer
label_desc: Use image node_id 3 for calling a global function with example usage: maybe_add_gradient_clipping(cfg, optimizer) and returns: optimizer, optimizer