class_docstr: Abstract class for all nonautoregressive-based models
class_docstr_tok: ["Abstract","class","for","all","nonautoregressive-based","models"]
code: def __init__(self, args, encoder, decoder):
      super().__init__(args, encoder, decoder)
      self.tgt_dict = decoder.dictionary
      self.bos = decoder.dictionary.bos()
      self.eos = decoder.dictionary.eos()
      self.pad = decoder.dictionary.pad()
      self.unk = decoder.dictionary.unk()
  
      self.ensemble_models = None
  
imports: import math
  import torch
  from fairseq.models.transformer import TransformerDecoder, TransformerEncoder, TransformerModel
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
label_desc: Use image node_id 1 to create a new FairseqNATModel object from inherited base classes: TransformerModel with example: obj = FairseqNATModel(args, encoder, decoder)