code: def norm(self, images):
      if not self.normalization is not None:
          return images
  
      means, stds = map(
          lambda t: torch.as_tensor(t).to(images), self.normalization
      )
      arrange = (
          "c -> () c () ()"
          if self.positional_dims == 2
          else "c -> () c ()"
      )
      means, stds = map(lambda t: rearrange(t, arrange), (means, stds))
      images = images.clone()
      images.sub_(means).div_(stds)
      return images
  
imports: import functools
  from math import sqrt
  import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
  import torchaudio
  from einops import rearrange
label_desc: Use image node_id 2 for calling the DiscreteVAE obj's underlying member method code with example usage: obj.norm(images) and returns: images, images