code: def initialize(model, init_type="pytorch"):
      """Initialize Transformer module.
  
      :param torch.nn.Module model: transformer instance
      :param str init_type: initialization type
      """
      if init_type == "pytorch":
          return
  
      # weight init
      for p in model.parameters():
          if p.dim() > 1:
              if init_type == "xavier_uniform":
                  torch.nn.init.xavier_uniform_(p.data)
              elif init_type == "xavier_normal":
                  torch.nn.init.xavier_normal_(p.data)
              elif init_type == "kaiming_uniform":
                  torch.nn.init.kaiming_uniform_(
                      p.data, nonlinearity="relu"
                  )
              elif init_type == "kaiming_normal":
                  torch.nn.init.kaiming_normal_(
                      p.data, nonlinearity="relu"
                  )
              else:
                  raise ValueError(
                      "Unknown initialization: " + init_type
                  )
      # bias init
      for p in model.parameters():
          if p.dim() == 1:
              p.data.zero_()
  
      # reset some modules with default init
      for m in model.modules():
          if isinstance(m, (torch.nn.Embedding, LayerNorm)):
              m.reset_parameters()
  
label_desc: Use image node_id 1 for calling a global function with example usage: initialize(model, init_type) without return types