code: def __init__(
      self, args, dictionary, embed_tokens, no_encoder_attn=False
  ):
      # use the TransformerDecoder's __init__
      super(LevenshteinTransformerDecoder, self).__init__(
          args,
          dictionary,
          embed_tokens,
          no_encoder_attn=no_encoder_attn,
      )
  
      self.dictionary = dictionary
      self.bos = dictionary.bos()
      self.unk = dictionary.unk()
      self.eos = dictionary.eos()
      self.pool_out = Linear(
          self.output_embed_dim * 2, self.output_embed_dim
      )
  
      self.label_tau = getattr(args, "label_tau", None)
  
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 1 to create a new InsertionTransformerDecoder object from inherited base classes: LevenshteinTransformerDecoder with example: obj = InsertionTransformerDecoder(args, dictionary, embed_tokens, no_encoder_attn)