code: def batch_adjusted_stddev(imgs):
      # for batch_per_image_standardization
      std = (
          imgs.view(imgs.shape[0], -1)
          .std(dim=1)
          .view(imgs.shape[0], 1, 1, 1)
      )
      std_min = (
          1.0 / imgs.new_tensor(imgs.shape[1:]).prod().float().sqrt()
      )
      return torch.max(std, std_min)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 26 for calling a global function with example usage: batch_adjusted_stddev(imgs) and returns: torch