code: def build_decoder(cls, args, src_dict, dst_dict, embed_tokens):
      return TransformerDecoder(
          args, src_dict, dst_dict, embed_tokens=embed_tokens
      )
  
imports: import math
  import random
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import options, utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, transformer
  from fairseq.modules import AdaptiveSoftmax, LayerNorm, MultiheadAttention, SinusoidalPositionalEmbedding
  from pytorch_translate import utils, vocab_reduction
  from pytorch_translate.average_attention import AverageAttention
  from pytorch_translate.common_layers import TransformerEmbedding, TransformerEncoderGivenEmbeddings, TransformerTokenEmbedding, VariableTracker
  from pytorch_translate.semi_supervised import SemiSupervisedModel
  from pytorch_translate.utils import torch_find
label_desc: Use image node_id 6 for calling the TransformerModel obj's underlying member method code with example usage: obj.build_decoder(cls, args, src_dict, dst_dict, embed_tokens) and returns: TransformerDecoder