code: def _timm_norm(dls, cfg, pretrained, n_in=3):
      if not pretrained:
          return
      if n_in != len(cfg["mean"]):
          return
      if not dls.after_batch.fs.filter(risinstance(Normalize)):
          tfm = Normalize.from_stats(cfg["mean"], cfg["std"])
          dls.add_tfms([tfm], "after_batch")
  
imports: from __future__ import annotations
  from packaging.version import parse
  from ..basics import *
  from .core import *
  from .data import *
  from .augment import *
  from .None import models
  import torchvision
  from ..callback.hook import num_features_model
label_desc: Use image node_id 20 for calling a global function with example usage: _timm_norm(dls, cfg, pretrained, n_in) without return types