code: def initialize_weights(module):
      if isinstance(module, nn.Conv2d):
          n = (
              module.kernel_size[0]
              * module.kernel_size[1]
              * module.out_channels
          )
          module.weight.data.normal_(0, math.sqrt(2.0 / n))
          if module.bias is not None:
              module.bias.data.zero_()
      elif isinstance(module, nn.BatchNorm2d):
          module.weight.data.fill_(1)
          module.bias.data.zero_()
      elif isinstance(module, nn.Linear):
          module.bias.data.zero_()
  
imports: import math
  from PIL import Image
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torchvision.transforms
  from torch.utils.data import Dataset, DataLoader
  from art.estimators.classification import PyTorchClassifier
  from art.data_generators import PyTorchDataGenerator
  from art.defences.trainer import AdversarialTrainerFBFPyTorch
  from art.utils import load_cifar10
  from art.attacks.evasion import ProjectedGradientDescent
label_desc: Use image node_id 2 for calling a global function with example usage: initialize_weights(module) without return types