code: def forward(
      self,
      tgt,
      tgt_mask: Optional[Tensor] = None,
      tgt_key_padding_mask: Optional[Tensor] = None,
      query_pos: Optional[Tensor] = None,
  ):
      # self attention
      tgt2 = self.norm1(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.dropout1(tgt2)
  
      # ffn
      tgt2 = self.norm2(tgt)
      tgt2 = self.linear2(
          self.dropout(self.activation(self.linear1(tgt2)))
      )
      tgt = tgt + self.dropout2(tgt2)
      return tgt
  
imports: import math
  import torch
  from torch import nn, Tensor
  import torch.nn.functional
  from typing import Optional
  from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
  from basicsr.utils.registry import ARCH_REGISTRY
label_desc: Use image node_id 3 for calling the TransformerSALayer obj's underlying member method code with example usage: obj.forward(tgt, tgt_mask, tgt_key_padding_mask, query_pos) and returns: tgt