code: def weights_init(m):
      # custom weights initialization called on netG and netD
      classname = m.__class__.__name__
      if classname.find("Conv") != -1:
          nn.init.normal_(m.weight.data, 0.0, 0.02)
      elif classname.find("BatchNorm") != -1:
          nn.init.normal_(m.weight.data, 1.0, 0.02)
          nn.init.constant_(m.bias.data, 0)
  
imports: import os
  import random
  import time
  from pathlib import Path
  import torch
  import torch.nn
  import torch.nn.parallel
  import torch.optim
  import torch.utils.data
  import torchvision.transforms
  import torchvision.utils
  from torchvision.datasets import CelebA
label_desc: Use image node_id 2 for calling a global function with example usage: weights_init(m) without return types