code: def clip_gradients(
      parameters,
      max_norm=1.0,
      global_grad_norm=None,
      mpu=None,
      eps=1e-6,
  ):
      """Clip the gradient of a list of parameters.
      Args:
          parameters: List of parameters whose .grad will be clipped.
          global_grad_norm (float, optional): Precomputed gradient norm. Defaults to None.
          mpu (optional): model parallelism unit. Defaults to None.
          eps (float, optional): epsilon value added to grad norm. Defaults to 1e-6
      Returns:
          float: the global gradient norm
      """
      if global_grad_norm is None:
          global_grad_norm = get_grad_norm(parameters, mpu=mpu)
      clip_coef = max_norm / (global_grad_norm + eps)
      if clip_coef < 1:
          for p in parameters:
              p.grad.detach().mul_(clip_coef)
      return global_grad_norm
  
imports: from collections.abc import Iterable
  from deepspeed.moe.utils import is_moe_param
  import os
  import psutil
  import gc
  from math import sqrt
  from math import floor
  from bisect import bisect_left
  import torch
  from torch import inf
  from deepspeed import comm
  from deepspeed.utils import groups, logger
  from deepspeed.runtime.constants import PIPE_REPLICATED
  from numpy import prod
label_desc: Use image node_id 24 for calling a global function with example usage: clip_gradients(parameters, max_norm, global_grad_norm, mpu, eps) and returns: global_grad_norm