code: def get_norm(norm, out_channels):
      """
      Args:
          norm (str or callable): either one of BN, SyncBN, FrozenBN, GN;
              or a callable that takes a channel number and returns
              the normalization layer as a nn.Module.
  
      Returns:
          nn.Module or None: the normalization layer
      """
      if norm is None:
          return None
      if isinstance(norm, str):
          if len(norm) == 0:
              return None
          norm = {
              "BN": nn.BatchNorm2d,
              "GN": lambda channels: nn.GroupNorm(32, channels),
          }[norm]
      return norm(out_channels)
  
imports: import copy
  import math
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
label_desc: Use image node_id 1 for calling a global function with example usage: get_norm(norm, out_channels) and returns: norm, None, None