code: def _initialize_weights(self):
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              nn.init.kaiming_normal_(m.weight)
          elif isinstance(m, nn.BatchNorm2d):
              nn.init.constant_(m.weight, 1)
              nn.init.constant_(m.bias, 0)
          elif isinstance(m, nn.Linear):
              nn.init.constant_(m.bias, 0)
  
imports: from collections import OrderedDict
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the DenseNet obj's underlying member method code with example usage: obj._initialize_weights() without return types