code: def initialize_weights(*models):
      for model in models:
          for m in model.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(
                      m.weight.data, nonlinearity="relu"
                  )
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1.0)
                  m.bias.data.fill_(1e-4)
              elif isinstance(m, nn.Linear):
                  m.weight.data.normal_(0.0, 0.0001)
                  m.bias.data.zero_()
  
imports: import torch
  import logging
  import torch.nn
  import torch.nn.functional
  from torchvision import models
  from itertools import chain
  import numpy
  from collections import OrderedDict
label_desc: Use image node_id 1 for calling a global function with example usage: initialize_weights() without return types