text: import torch.nn as nn
  
  
  def initialize_decoder(module):
      for m in module.modules():
  
          if isinstance(m, nn.Conv2d):
              nn.init.kaiming_uniform_(m.weight, mode="fan_in", nonlinearity="relu")
              if m.bias is not None:
                  nn.init.constant_(m.bias, 0)
  
          elif isinstance(m, nn.BatchNorm2d):
              nn.init.constant_(m.weight, 1)
              nn.init.constant_(m.bias, 0)
  
          elif isinstance(m, nn.Linear):
              nn.init.xavier_uniform_(m.weight)
              if m.bias is not None:
                  nn.init.constant_(m.bias, 0)
  
  
  def initialize_head(module):
      for m in module.modules():
          if isinstance(m, (nn.Linear, nn.Conv2d)):
              nn.init.xavier_uniform_(m.weight)
              if m.bias is not None:
                  nn.init.constant_(m.bias, 0)
  