code: def forward_mask_ins(
      self, normalize, encoder_out, prev_output_tokens, **unused
  ):
      features, extra = self.extract_features(
          prev_output_tokens,
          encoder_out=encoder_out,
          early_exit=self.early_exit[1],
          layers=self.layers_msk,
          **unused,
      )
      features_cat = torch.cat(
          [features[:, :-1, :], features[:, 1:, :]], 2
      )
      decoder_out = F.linear(features_cat, self.embed_mask_ins.weight)
      if normalize:
          return F.log_softmax(decoder_out, -1), extra["attn"]
      return decoder_out, extra["attn"]
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  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 import TransformerDecoderLayer
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from .levenshtein_utils import _apply_del_words, _apply_ins_masks, _apply_ins_words, _fill, _get_del_targets, _get_ins_targets, _skip, _skip_encoder_out
label_desc: Use image node_id 3 for calling the LevenshteinTransformerDecoder obj's underlying member method code with example usage: obj.forward_mask_ins(normalize, encoder_out, prev_output_tokens) and returns: decoder_out,  extra, F,  extra