code: def forward(
      self,
      src_tokens: torch.Tensor,
      additional_features: List[List[torch.Tensor]],
      src_lengths,
      prev_output_tokens,
      incremental_state: Optional[Dict[str, torch.Tensor]] = None,
  ):
      # embed tokens
      embeddings = self.source_embeddings(
          [[src_tokens]] + additional_features
      )
  
      # n.b. tensorized_features[0][0] must be src_tokens
      encoder_out = self.encoder(
          src_tokens, embeddings, src_lengths=src_lengths
      )
      decoder_out = self.decoder(
          prev_output_tokens, encoder_out, incremental_state
      )
      return decoder_out
  
file_path: pytext/pytext/models/seq_models/rnn_encoder_decoder.py
imports: from typing import Dict, List, Optional
  import torch.jit
  from pytext.config import ConfigBase
  from pytext.models.module import create_module
  from pytext.utils.usage import log_class_usage
  from .base import PyTextSeq2SeqModule
  from .rnn_decoder import RNNDecoder
  from .rnn_encoder import LSTMSequenceEncoder
label_desc: Use image node_id 2 for calling the RNNModel obj's underlying member method code with example usage: obj.forward(src_tokens, additional_features, src_lengths, prev_output_tokens, incremental_state) and returns: decoder_out