code: def init_weights(modules):
      for m in modules:
          if isinstance(m, nn.Conv2d):
              init.xavier_uniform_(m.weight.data)
              if m.bias is not None:
                  m.bias.data.zero_()
          elif isinstance(m, nn.BatchNorm2d):
              m.weight.data.fill_(1)
              m.bias.data.zero_()
          elif isinstance(m, nn.Linear):
              m.weight.data.normal_(0, 0.01)
              m.bias.data.zero_()
  
imports: import torch
  import torch.nn
  import torch.nn.init
  import torchvision
  from torchvision import models
  from collections import namedtuple
  from packaging import version
label_desc: Use image node_id 1 for calling a global function with example usage: init_weights(modules) without return types