code: def forward(
      self,
      src_tokens: torch.Tensor,
      dict_feats: Optional[
          Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
      ],
      src_lengths: torch.Tensor,
      trg_tokens: torch.Tensor,
      src_subword_begin_indices: Optional[torch.Tensor] = None,
  ):
      additional_features: List[List[torch.Tensor]] = []
  
      if dict_feats is not None:
          additional_features.append(list(dict_feats))
  
      logits, output_dict = self.model(
          src_tokens=src_tokens,
          additional_features=additional_features,
          src_lengths=src_lengths,
          prev_output_tokens=trg_tokens,
          src_subword_begin_indices=src_subword_begin_indices,
      )
  
      output_dict = nar_model_forward_aux(
          self.length_prediction_model,
          output_dict,
          dict_feats=dict_feats,
      )
  
      return logits, output_dict
  
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  from pytext.common.constants import Stage
  from pytext.data.masked_tensorizer import MaskedTokenTensorizer
  from pytext.data.tensorizers import GazetteerTensorizer, Tensorizer, TokenTensorizer
  from pytext.data.utils import Vocabulary
  from pytext.models.embeddings.scriptable_embedding_list import ScriptableEmbeddingList
  from pytext.models.model import Model
  from pytext.models.module import create_module
  from .conv_model import CNNModel, DecoupledCNNModel
  from .mask_generator import MaskedSequenceGenerator
  from .nar_length import ConvLengthPredictionModule, MaskedLengthPredictionModule
  from .nar_output_layer import NARSeq2SeqOutputLayer
  from .seq2seq_model import Seq2SeqModel
label_desc: Use image node_id 3 for calling the NARSeq2SeqModel obj's underlying member method code with example usage: obj.forward(src_tokens, dict_feats, src_lengths, trg_tokens, src_subword_begin_indices) and returns: logits,  output_dict