code: def _add_norm(dls, meta, pretrained, n_in=3):
      if not pretrained:
          return
      stats = meta.get("stats")
      if stats is None:
          return
      if n_in != len(stats[0]):
          return
      if not dls.after_batch.fs.filter(risinstance(Normalize)):
          dls.add_tfms([Normalize.from_stats(*stats)], "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 19 for calling a global function with example usage: _add_norm(dls, meta, pretrained, n_in) without return types