def load_dataset(self, split, epoch=0, combine=False, **kwargs):
        """Load a given dataset split.

        Args:
            split (str): name of the split (e.g., train, valid, test)
        """

        paths = self.args.data.split(':')
        assert len(paths) > 0
        data_path = paths[epoch % len(paths)]
        split_path = os.path.join(data_path, split)

        dataset = data_utils.load_indexed_dataset(
            split_path,
            self.dictionary,
            self.args.dataset_impl,
            combine=combine,
        )
        if dataset is None:
            raise FileNotFoundError('Dataset not found: {} ({})'.format(split, split_path))

        dataset = StripTokenDataset(dataset, self.dictionary.eos())

        # create continuous blocks of tokens
        dataset = TokenBlockDataset(
                dataset,
                dataset.sizes,
                self.args.tokens_per_sample - 2,  # one less for <s> and one for </s>
                pad=self.dictionary.pad(),
                eos=self.dictionary.eos(),
                break_mode=self.args.sample_break_mode,
                document_sep_len=0
        )

        # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT)
        dataset = PrependTokenDataset(dataset, self.source_dictionary.bos())
        dataset = AppendTokenDataset(dataset, self.source_dictionary.eos())

        mask_whole_words = get_whole_word_mask(self.args, self.source_dictionary) \
            if self.args.mask_length != 'subword' else None

        self.datasets[split] = DenoisingDataset(
            dataset, dataset.sizes, self.dictionary, self.mask_idx,
            mask_whole_words, shuffle=self.args.shuffle_instance,
            seed=self.seed, args=self.args
        )
        print(
            "| Split: {0}, Loaded {1} samples of denoising_dataset".format(
                split,
                len(self.datasets[split]),
            )
        )
Load a given dataset split.

        Args:
            split (str): name of the split (e.g., train, valid, test)
        
infoxlm/fairseq/fairseq/tasks/denoising.py