code: def clip_grad_value_(parameters, clip_value, norm_type=2):
      if isinstance(parameters, torch.Tensor):
          parameters = [parameters]
      parameters = list(
          filter(lambda p: p.grad is not None, parameters)
      )
      norm_type = float(norm_type)
      if clip_value is not None:
          clip_value = float(clip_value)
  
      total_norm = 0
      for p in parameters:
          param_norm = p.grad.data.norm(norm_type)
          total_norm += param_norm.item() ** norm_type
          if clip_value is not None:
              p.grad.data.clamp_(min=-clip_value, max=clip_value)
      total_norm = total_norm ** (1.0 / norm_type)
      return total_norm
  
imports: from enum import Enum
  from typing import Optional, Tuple
  import librosa
  import matplotlib.pylab
  import numpy
  import torch
  from einops import rearrange
  from numba import jit, prange
  from nemo.collections.tts.torch.tts_data_types import DATA_STR2DATA_CLASS, MAIN_DATA_TYPES, WithLens
  from nemo.utils import logging
  from nemo.utils.decorators import deprecated
label_desc: Use image node_id 28 for calling a global function with example usage: clip_grad_value_(parameters, clip_value, norm_type) and returns: total_norm