    def _get_noising_dataset_batch(
        self,
        src_tokens_no_pad,
        src_dict,
        append_eos_to_tgt=False,
    ):
        """
        Constructs a NoisingDataset and the corresponding
        ``LanguagePairDataset(NoisingDataset(src), src)``. If
        *append_eos_to_tgt* is True, wrap the source dataset in
        :class:`TransformEosDataset` to append EOS to the clean source when
        using it as the target.
        """
        src_dataset = test_utils.TestDataset(data=src_tokens_no_pad)

        noising_dataset = noising.NoisingDataset(
            src_dataset=src_dataset,
            src_dict=src_dict,
            seed=1234,
            max_word_shuffle_distance=3,
            word_dropout_prob=0.2,
            word_blanking_prob=0.2,
            noising_class=noising.UnsupervisedMTNoising,
        )
        tgt = src_dataset
        language_pair_dataset = LanguagePairDataset(
            src=noising_dataset, tgt=tgt, src_sizes=None, src_dict=src_dict
        )
        language_pair_dataset = TransformEosDataset(
            language_pair_dataset,
            src_dict.eos(),
            append_eos_to_tgt=append_eos_to_tgt,
        )

        dataloader = torch.utils.data.DataLoader(
            dataset=language_pair_dataset,
            batch_size=2,
            collate_fn=language_pair_dataset.collater,
        )
        denoising_batch_result = next(iter(dataloader))
        return denoising_batch_result
Constructs a NoisingDataset and the corresponding
``LanguagePairDataset(NoisingDataset(src), src)``. If
*append_eos_to_tgt* is True, wrap the source dataset in
:class:`TransformEosDataset` to append EOS to the clean source when
using it as the target.
tests/test_noising.py