code: def get_targets(self, sample, net_output):
      targets = sample["target"].view(-1)
      possible_translation_tokens = net_output[-1]
      if possible_translation_tokens is not None:
          targets = torch_find(
              possible_translation_tokens,
              targets,
              len(self.task.target_dictionary),
          )
      return targets
  
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 4 for calling the TransformerModel obj's underlying member method code with example usage: obj.get_targets(sample, net_output) and returns: targets