code: def forward_length_prediction(
      self, length_out, encoder_out, tgt_tokens=None
  ):
      enc_feats = encoder_out["encoder_out"][0]  # T x B x C
      if len(encoder_out["encoder_padding_mask"]) > 0:
          src_masks = encoder_out["encoder_padding_mask"][0]  # B x T
      else:
          src_masks = None
      if self.pred_length_offset:
          if src_masks is None:
              src_lengs = enc_feats.new_ones(enc_feats.size(1)).fill_(
                  enc_feats.size(0)
              )
          else:
              src_lengs = (
                  (~src_masks).transpose(0, 1).type_as(enc_feats).sum(0)
              )
          src_lengs = src_lengs.long()
  
      if tgt_tokens is not None:
          # obtain the length target
          tgt_lengs = tgt_tokens.ne(self.padding_idx).sum(1).long()
          if self.pred_length_offset:
              length_tgt = tgt_lengs - src_lengs + 128
          else:
              length_tgt = tgt_lengs
          length_tgt = length_tgt.clamp(min=0, max=255)
  
      else:
          # predict the length target (greedy for now)
          # TODO: implementing length-beam
          pred_lengs = length_out.max(-1)[1]
          if self.pred_length_offset:
              length_tgt = pred_lengs - 128 + src_lengs
          else:
              length_tgt = pred_lengs
  
      return length_tgt
  
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 7 for calling the NATransformerDecoder obj's underlying member method code with example usage: obj.forward_length_prediction(length_out, encoder_out, tgt_tokens) and returns: length_tgt