code: def _initialize_weights(self):
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
              nn.init.xavier_uniform_(m.weight)
              if m.bias is not None:
                  nn.init.constant_(m.bias, 0)
          elif isinstance(m, nn.Linear):
              nn.init.xavier_uniform_(m.weight)
              # nn.init.normal_(m.weight, 0, 0.01)
              nn.init.constant_(m.bias, 0)
  
file_path: deep-learning-for-image-processing/pytorch_classification/Test3_vggnet/model.py
imports: import torch.nn
  import torch
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