code: def normalization(channels):
      """
      Make a standard normalization layer.
  
      :param channels: number of input channels.
      :return: an nn.Module for normalization.
      """
      groups = 32
      if channels <= 16:
          groups = 8
      elif channels <= 64:
          groups = 16
      while channels % groups != 0:
          groups = int(groups / 2)
      assert groups > 2
      return GroupNorm32(groups, channels)
  
imports: import functools
  import math
  import os
  import fsspec
  import torch
  import torch.nn
  import torch.nn.functional
  import torchaudio
  from transformers import LogitsWarper
  from TTS.tts.layers.tortoise.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
label_desc: Use image node_id 2 for calling a global function with example usage: normalization(channels) and returns: GroupNorm32