code: def _initialize_weights(self):
      for m in self.modules():
          if isinstance(m, nn.Conv3d):
              n = (
                  m.kernel_size[0]
                  * m.kernel_size[1]
                  * m.kernel_size[2]
                  * m.out_channels
              )
              m.weight.data.normal_(0, math.sqrt(2.0 / n))
              if m.bias is not None:
                  m.bias.data.zero_()
  
          elif isinstance(m, nn.Conv2d):
              n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
              m.weight.data.normal_(0, math.sqrt(2.0 / n))
              if m.bias is not None:
                  m.bias.data.zero_()
  
          elif isinstance(m, nn.Conv1d):
              n = m.kernel_size[0] * m.out_channels
              m.weight.data.normal_(0, math.sqrt(2.0 / n))
              if m.bias is not None:
                  m.bias.data.zero_()
  
          elif isinstance(m, nn.BatchNorm3d):
              m.weight.data.fill_(1)
              m.bias.data.zero_()
  
          elif isinstance(m, nn.BatchNorm2d):
              m.weight.data.fill_(1)
              m.bias.data.zero_()
  
          elif isinstance(m, nn.BatchNorm1d):
              m.weight.data.fill_(1)
              m.bias.data.zero_()
  
imports: import math
  import numpy
  import torch
  import torch.nn
  from torch.autograd import Variable
label_desc: Use image node_id 3 for calling the Lipreading obj's underlying member method code with example usage: obj._initialize_weights() without return types