code: def _initialize_weights(self):
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              if (
                  m.in_channels != m.out_channels
                  or m.out_channels != m.groups
                  or m.bias is not None
              ):
                  # don't want to reinitialize downsample layers, code assuming normal conv layers will not have these characteristics
                  nn.init.kaiming_normal_(
                      m.weight, mode="fan_out", nonlinearity="relu"
                  )
                  if m.bias is not None:
                      nn.init.constant_(m.bias, 0)
              else:
                  print("Not initializing")
          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)
  
file_path: antialiased-cnns/antialiased_cnns/vgg.py
imports: import torch.nn
  import torch.utils.model_zoo
  from antialiased_cnns import *
label_desc: Use image node_id 3 for calling the VGG obj's underlying member method code with example usage: obj._initialize_weights() without return types