code: def forward_word_ins(
      self, normalize, encoder_out, prev_output_tokens
  ):
      features = self.extract_features(
          prev_output_tokens, encoder_out=encoder_out
      )[0]
      features = self.pool_out(
          torch.cat([features[:, :-1, :], features[:, 1:, :]], 2)
      )
      decoder_out = self.output_layer(features)
      return (
          F.log_softmax(decoder_out, -1) if normalize else decoder_out
      )
  
imports: import numpy
  import torch
  import torch.nn.functional
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.nat import FairseqNATModel, LevenshteinTransformerDecoder, LevenshteinTransformerModel, ensemble_decoder
  from fairseq.models.transformer import Linear
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from fairseq.utils import new_arange
label_desc: Use image node_id 2 for calling the InsertionTransformerDecoder obj's underlying member method code with example usage: obj.forward_word_ins(normalize, encoder_out, prev_output_tokens) and returns: unknown