code: def initialize(self, input):
      with torch.no_grad():
          flatten = (
              input.permute(1, 0, 2, 3)
              .contiguous()
              .view(input.shape[1], -1)
          )
          mean = (
              flatten.mean(1)
              .unsqueeze(1)
              .unsqueeze(2)
              .unsqueeze(3)
              .permute(1, 0, 2, 3)
          )
          std = (
              flatten.std(1)
              .unsqueeze(1)
              .unsqueeze(2)
              .unsqueeze(3)
              .permute(1, 0, 2, 3)
          )
  
          self.loc.data.copy_(-mean)
          self.scale.data.copy_(1 / (std + 1e-6))
  
file_path: generative-models/sgm/modules/autoencoding/lpips/util.py
imports: import hashlib
  import os
  import requests
  import torch
  import torch.nn
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling the ActNorm obj's underlying member method code with example usage: obj.initialize(input) without return types