code: def _backtranslation_dataset_helper(
      self,
      remove_eos_from_input_src,
      remove_eos_from_output_src,
  ):
      tgt_dataset = LanguagePairDataset(
          src=self.tgt_dataset,
          src_sizes=self.tgt_dataset.sizes,
          src_dict=self.tgt_dict,
          tgt=None,
          tgt_sizes=None,
          tgt_dict=None,
      )
  
      generator = SequenceGenerator(
          [self.model],
          tgt_dict=self.tgt_dict,
          max_len_a=0,
          max_len_b=200,
          beam_size=2,
          unk_penalty=0,
      )
  
      backtranslation_dataset = BacktranslationDataset(
          tgt_dataset=TransformEosDataset(
              dataset=tgt_dataset,
              eos=self.tgt_dict.eos(),
              # remove eos from the input src
              remove_eos_from_src=remove_eos_from_input_src,
          ),
          src_dict=self.tgt_dict,
          backtranslation_fn=(
              lambda sample: generator.generate([self.model], sample)
          ),
          output_collater=TransformEosDataset(
              dataset=tgt_dataset,
              eos=self.tgt_dict.eos(),
              # if we remove eos from the input src, then we need to add it
              # back to the output tgt
              append_eos_to_tgt=remove_eos_from_input_src,
              remove_eos_from_src=remove_eos_from_output_src,
          ).collater,
          cuda=self.cuda,
      )
      dataloader = torch.utils.data.DataLoader(
          backtranslation_dataset,
          batch_size=2,
          collate_fn=backtranslation_dataset.collater,
      )
      backtranslation_batch_result = next(iter(dataloader))
  
      eos, pad, w1, w2 = (
          self.tgt_dict.eos(),
          self.tgt_dict.pad(),
          self.w1,
          self.w2,
      )
  
      # Note that we sort by src_lengths and add left padding, so actually
      # ids will look like: [1, 0]
      expected_src = torch.LongTensor(
          [[w1, w2, w1, eos], [pad, pad, w1, eos]]
      )
      if remove_eos_from_output_src:
          expected_src = expected_src[:, :-1]
      expected_tgt = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]])
      generated_src = backtranslation_batch_result["net_input"][
          "src_tokens"
      ]
      tgt_tokens = backtranslation_batch_result["target"]
  
      self.assertTensorEqual(expected_src, generated_src)
      self.assertTensorEqual(expected_tgt, tgt_tokens)
  
file_path: fairseq/tests/test_backtranslation_dataset.py
imports: import unittest
  import tests.utils
  import torch
  from fairseq.data import BacktranslationDataset, LanguagePairDataset, TransformEosDataset
  from fairseq.sequence_generator import SequenceGenerator
label_desc: Use image node_id 2 for calling the TestBacktranslationDataset obj's underlying member method code with example usage: obj._backtranslation_dataset_helper(remove_eos_from_input_src, remove_eos_from_output_src) without return types