code: def weights_init(m):
      classname = m.__class__.__name__
      if classname.find("Conv") != -1:
          nn.init.kaiming_normal(m.weight.data)
      elif classname.find("BatchNorm") != -1:
          m.weight.data.fill_(1.0)
          m.bias.data.fill_(1e-4)
      elif classname.find("Linear") != -1:
          m.weight.data.normal_(0.0, 0.0001)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from .None import seg_resnet
  from .None import seg_resnext
label_desc: Use image node_id 1 for calling the ModelBuilder obj's underlying member method code with example usage: obj.weights_init(m) without return types