code: def forward(
      self,
      src_tokens,
      src_lengths,
      prev_output_tokens,
      tgt_tokens,
      **kwargs,
  ):
      # encoding
      encoder_out = self.encoder(
          src_tokens, src_lengths=src_lengths, **kwargs
      )
  
      # length prediction
      length_out = self.decoder.forward_length(
          normalize=False, encoder_out=encoder_out
      )
      length_tgt = self.decoder.forward_length_prediction(
          length_out, encoder_out, tgt_tokens
      )
  
      # decoding
      word_ins_out = self.decoder(
          normalize=False,
          prev_output_tokens=prev_output_tokens,
          encoder_out=encoder_out,
      )
  
      return {
          "word_ins": {
              "out": word_ins_out,
              "tgt": tgt_tokens,
              "mask": tgt_tokens.ne(self.pad),
              "ls": self.args.label_smoothing,
              "nll_loss": True,
          },
          "length": {
              "out": length_out,
              "tgt": length_tgt,
              "factor": self.decoder.length_loss_factor,
          },
      }
  
imports: import torch
  import torch.nn.functional
  from fairseq import utils
  from fairseq.iterative_refinement_generator import DecoderOut
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.nat import FairseqNATDecoder, FairseqNATModel, ensemble_decoder
  from fairseq.models.transformer import Embedding
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
label_desc: Use image node_id 4 for calling the NATransformerModel obj's underlying member method code with example usage: obj.forward(src_tokens, src_lengths, prev_output_tokens, tgt_tokens) and returns: dict