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
  
      intermediate = []
  
      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.return_intermediate:
              intermediate.append(self.norm(output))
  
      if self.norm is not None:
          output = self.norm(output)
          if self.return_intermediate:
              intermediate.pop()
              intermediate.append(output)
  
      if self.return_intermediate:
          return torch.stack(intermediate)
  
      return output.unsqueeze(0)
  
imports: import copy
  from typing import Optional, List
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
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, torch