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:
          # pad_mask is used to ensure padding IDs remain the same regardless
          # of where torch_find() finds it in possible_translation_tokens.
          # This is important since FairseqCriterion has special logic to
          # ignore padding IDs when calculating loss.
          pad_mask = targets == self.decoder.padding_idx
          targets = torch_find(
              possible_translation_tokens,
              targets,
              len(self.task.target_dictionary),
          )
          targets[pad_mask] = self.decoder.padding_idx
      return targets
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.onnx.operators
  from fairseq import options, utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.modules import AdaptiveSoftmax
  from pytorch_translate import attention, rnn_cell, utils, vocab_reduction
  from pytorch_translate.common_layers import ContextEmbedding, DecoderWithOutputProjection, Embedding, Linear, RNNLayer, VariableTracker
  from pytorch_translate.data import data
  from pytorch_translate.multi_model import MultiDecoder, MultiEncoder
  from pytorch_translate.multilingual import MultilingualDecoder, MultilingualEncoder
  from pytorch_translate.ngram import NGramDecoder
  from pytorch_translate.semi_supervised import SemiSupervisedModel
  from pytorch_translate.utils import maybe_cat, maybe_cuda, torch_find
  from torch.nn.utils.rnn import pack_padded_sequence, PackedSequence, pad_packed_sequence
label_desc: Use image node_id 10 for calling the RNNModel obj's underlying member method code with example usage: obj.get_targets(sample, net_output) and returns: targets