code: def _initialize_weights(self) -> None:
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              nn.init.kaiming_normal_(
                  m.weight, mode="fan_out", nonlinearity="relu"
              )
              if m.bias is not None:
                  nn.init.constant_(m.bias, 0)
          elif isinstance(m, nn.BatchNorm2d):
              nn.init.constant_(m.weight, 1)
              nn.init.constant_(m.bias, 0)
          elif isinstance(m, nn.Linear):
              nn.init.normal_(m.weight, 0, 0.01)
              nn.init.constant_(m.bias, 0)
  
imports: from typing import Union, List, Dict, Any, cast
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import ClassifierHead
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_module
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 9 for calling the VGG obj's underlying member method code with example usage: obj._initialize_weights() without return types