code: def forward(
      self,
      input_ids,
      attention_mask,
      decoder_input_ids=None,
      encoder_outputs=None,
      labels=None,
      return_dict=True,
      output_attentions=False,
      output_hidden_states=False,
  ):
      if decoder_input_ids is None and labels is not None:
          # decoder_input_ids generate if None
          decoder_input_ids = self.shift_tokens_right(
              labels, self.pad_token_id
          )
  
      decoder_cells = None
      if self.model_name == "lstm":
          # lstm
          masked_lm_loss = None
          if encoder_outputs is None:
              # if encoder outputs has not been computed
              encoder_outputs = self.encoder(input_ids, attention_mask)
          last_hidden_state = encoder_outputs.last_hidden_state
          decoder_hidden_states_before = (
              encoder_outputs.decoder_hidden_states_before
          )
          decoder_cells_before = encoder_outputs.decoder_cells_before
          decoder_hidden_state_last_layer = (
              encoder_outputs.decoder_hidden_state_last_layer
          )
          if labels is not None:
              # train the model
              Y = self.word_embeddings_decoder(
                  decoder_input_ids
              ).transpose(0, 1)
              decoder_hidden_states = decoder_hidden_states_before
              decoder_cells = decoder_cells_before
              decoder_outputs = []
              a_t = self.attention(
                  last_hidden_state,
                  decoder_hidden_state_last_layer,
                  attention_mask,
              )
              for y in Y:
                  y_t = torch.cat((y, a_t), dim=-1)
                  (
                      decoder_hidden_state_last_layer,
                      decoder_hidden_states,
                      decoder_cells,
                  ) = self.decoder(
                      y_t, decoder_hidden_states, decoder_cells
                  )
                  a_t = self.attention(
                      last_hidden_state,
                      decoder_hidden_state_last_layer,
                      attention_mask,
                  )
                  output_decoder = self.output_projection(
                      torch.cat(
                          (a_t, decoder_hidden_state_last_layer), dim=-1
                      )
                  )
                  decoder_outputs.append(output_decoder)
              decoder_outputs = torch.stack(decoder_outputs).transpose(
                  0, 1
              )
              decoder_outputs = torch.cat(
                  (Y[0].unsqueeze(1), decoder_outputs), dim=1
              )
              logits = self.lm_head(decoder_outputs)
              shift_logits = logits.contiguous()
              labels.masked_fill_(labels == -100, self.pad_token_id)
              masked_lm_loss = self.loss_fct(
                  shift_logits.view(-1, self.vocab_size),
                  labels.view(-1),
              )
          else:
              a_t = encoder_outputs.a_t
              if a_t is None:
                  a_t = self.attention(
                      last_hidden_state,
                      decoder_hidden_state_last_layer,
                      attention_mask,
                  )
              y = self.word_embeddings_decoder(decoder_input_ids)
              y_t = torch.cat((y, a_t), dim=-1)
              (
                  decoder_hidden_state_last_layer,
                  decoder_hidden_states,
                  decoder_cells,
              ) = self.decoder(
                  y_t,
                  decoder_hidden_states_before,
                  decoder_cells_before,
              )
              a_t = self.attention(
                  last_hidden_state,
                  decoder_hidden_state_last_layer,
                  attention_mask,
              )
              output_decoder = self.output_projection(
                  torch.cat(
                      (a_t, decoder_hidden_state_last_layer), dim=-1
                  )
              )
              logits = self.lm_head(output_decoder.unsqueeze(1))
      else:
          # gru, rnn
          masked_lm_loss = None
          decoder_hidden_states = None
          if encoder_outputs is None:
              # if encoder outputs has not been computed
              encoder_outputs = self.encoder(input_ids, attention_mask)
          last_hidden_state = encoder_outputs.last_hidden_state
          decoder_hidden_states_before = (
              encoder_outputs.decoder_hidden_states_before
          )
          decoder_hidden_state_last_layer = (
              encoder_outputs.decoder_hidden_state_last_layer
          )
          if labels is not None:
              # train the model
              Y = self.word_embeddings_decoder(
                  decoder_input_ids
              ).transpose(0, 1)
              decoder_hidden_states = decoder_hidden_states_before
              decoder_outputs = []
              a_t = self.attention(
                  last_hidden_state,
                  decoder_hidden_state_last_layer,
                  attention_mask,
              )
              for y in Y:
                  y_t = torch.cat((y, a_t), dim=-1)
                  (
                      decoder_hidden_state_last_layer,
                      decoder_hidden_states,
                      _,
                  ) = self.decoder(y_t, decoder_hidden_states)
                  a_t = self.attention(
                      last_hidden_state,
                      decoder_hidden_state_last_layer,
                      attention_mask,
                  )
                  output_decoder = self.output_projection(
                      torch.cat(
                          (a_t, decoder_hidden_state_last_layer), dim=-1
                      )
                  )
                  decoder_outputs.append(output_decoder)
              decoder_outputs = torch.stack(decoder_outputs).transpose(
                  0, 1
              )
              decoder_outputs = torch.cat(
                  (Y[0].unsqueeze(1), decoder_outputs), dim=1
              )
              logits = self.lm_head(decoder_outputs)
              shift_logits = logits.contiguous()
              labels.masked_fill_(labels == -100, self.pad_token_id)
              masked_lm_loss = self.loss_fct(
                  shift_logits.view(-1, self.vocab_size),
                  labels.view(-1),
              )
          else:
              a_t = encoder_outputs.a_t
              if a_t is None:
                  a_t = self.attention(
                      last_hidden_state,
                      decoder_hidden_state_last_layer,
                      attention_mask,
                  )
              y = self.word_embeddings_decoder(decoder_input_ids)
              y_t = torch.cat((y, a_t), dim=-1)
              (
                  decoder_hidden_state_last_layer,
                  decoder_hidden_states,
                  _,
              ) = self.decoder(y_t, decoder_hidden_states_before)
              a_t = self.attention(
                  last_hidden_state,
                  decoder_hidden_state_last_layer,
                  attention_mask,
              )
              output_decoder = self.output_projection(
                  torch.cat(
                      (a_t, decoder_hidden_state_last_layer), dim=-1
                  )
              )
              logits = self.lm_head(output_decoder.unsqueeze(1))
  
      outputs = RNNOutput(
          logits=logits,
          loss=masked_lm_loss,
          encoder_last_hidden_state=last_hidden_state,
          decoder_hidden_states_before=decoder_hidden_states,
          last_hidden_state=last_hidden_state,
          a_t=a_t,
          decoder_cells_before=decoder_cells,
          decoder_hidden_state_last_layer=decoder_hidden_state_last_layer,
      )
      return outputs
  
imports: import inspect
  import warnings
  from typing import Optional, Tuple, Dict, Any
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.utils import ModelOutput
  from transformers.modeling_utils import PreTrainedModel
  from transformers.configuration_utils import PretrainedConfig
  from torch.nn import CrossEntropyLoss
  from .abstract_model import AbstractModel
label_desc: Use image node_id 9 for calling the RNNSeq2Seq obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, decoder_input_ids, encoder_outputs, labels, return_dict, output_attentions, output_hidden_states) and returns: outputs