class_docstr: Abstract class for all nonautoregressive-based models
class_docstr_tok: ["Abstract","class","for","all","nonautoregressive-based","models"]
code: def build_decoder(cls, args, tgt_dict, embed_tokens):
      decoder = FairseqNATDecoder(args, tgt_dict, embed_tokens)
      if getattr(args, "apply_bert_init", False):
          decoder.apply(init_bert_params)
      return decoder
  
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 6 for calling the FairseqNATModel obj's underlying member method code with example usage: obj.build_decoder(cls, args, tgt_dict, embed_tokens) and returns: decoder