class_docstr: Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017)
  <https://arxiv.org/abs/1706.03762>`_.
class_docstr_tok: ["Transformer","model","from","`","``","Attention","Is","All","You","Need","''","(","Vaswani",",","et","al",",","2017",")","<","https",":","\/\/arxiv.org\/abs\/1706.03762",">","`","_","."]
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 torch
  import torch.nn
  import torch.nn.functional
  from fairseq import options, utils
  from fairseq.models import FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, transformer
  from fairseq.modules import AdaptiveSoftmax, LearnedPositionalEmbedding, MultiheadAttention, SinusoidalPositionalEmbedding
  from pytorch_translate import transformer, vocab_reduction
  from pytorch_translate.average_attention import AverageAttention, AverageWindowAttention
  from pytorch_translate.utils import torch_find
label_desc: Use image node_id 4 for calling the TransformerAANModel obj's underlying member method code with example usage: obj.get_targets(sample, net_output) and returns: targets