code: def forward(
      self,
      tgt,
      memory,
      tgt_mask: Optional[Tensor] = None,
      memory_mask: Optional[Tensor] = None,
      tgt_key_padding_mask: Optional[Tensor] = None,
      memory_key_padding_mask: Optional[Tensor] = None,
      pos: Optional[Tensor] = None,
      query_pos: Optional[Tensor] = None,
  ):
      output = tgt
  
      for layer in self.layers:
          output = layer(
              output,
              memory,
              tgt_mask=tgt_mask,
              memory_mask=memory_mask,
              tgt_key_padding_mask=tgt_key_padding_mask,
              memory_key_padding_mask=memory_key_padding_mask,
              pos=pos,
              query_pos=query_pos,
          )
  
      if self.norm is not None:
          # nn.LayerNorm(d_model)
          output = self.norm(output)
  
      return output.unsqueeze(0)
  
imports: import copy
  from typing import Dict, List, Optional, Sequence, Union
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
  from mmocr.models.common import Dictionary
  from mmocr.models.textrecog.decoders import BaseDecoder
  from mmocr.registry import MODELS
  from mmocr.utils.typing_utils import TextSpottingDataSample
  from .position_embedding import PositionEmbeddingSine
label_desc: Use image node_id 2 for calling the TransformerDecoder obj's underlying member method code with example usage: obj.forward(tgt, memory, tgt_mask, memory_mask, tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) and returns: output