code: def forward_pre(
      self,
      tgt,
      tgt_mask: Optional[Tensor] = None,
      tgt_key_padding_mask: Optional[Tensor] = None,
      query_pos: Optional[Tensor] = None,
  ):
      tgt2 = self.norm(tgt)
      q = k = self.with_pos_embed(tgt2, query_pos)
      tgt2 = self.self_attn(
          q,
          k,
          value=tgt2,
          attn_mask=tgt_mask,
          key_padding_mask=tgt_key_padding_mask,
      )[0]
      tgt = tgt + self.dropout(tgt2)
  
      return tgt
  
imports: import copy
  import math
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
label_desc: Use image node_id 5 for calling the SelfAttentionLayer obj's underlying member method code with example usage: obj.forward_pre(tgt, tgt_mask, tgt_key_padding_mask, query_pos) and returns: tgt