def load_dataset(self, split, epoch=1, combine=False, **kwargs):
        """Load a given dataset split.
        Args:
            split (str): name of the split (e.g., train, valid, test)
        """
        if self.cfg.batch_size is not None:
            bsz = self.cfg.batch_size
        else:
            bsz = max(1, self.cfg.max_tokens // self.cfg.tokens_per_sample)
        self.datasets[split] = DummyDataset(
            {
                "id": 1,
                "net_input": {
                    "src_tokens": torch.stack([self.dummy_src for _ in range(bsz)]),
                    "src_lengths": torch.full(
                        (bsz,), self.cfg.tokens_per_sample, dtype=torch.long
                    ),
                },
                "target": torch.stack([self.dummy_tgt for _ in range(bsz)]),
                "nsentences": bsz,
                "ntokens": bsz * self.cfg.tokens_per_sample,
            },
            num_items=self.cfg.dataset_size,
            item_size=self.cfg.tokens_per_sample,
        )
Load a given dataset split.
        Args:
            split (str): name of the split (e.g., train, valid, test)
        
understand_icl/fairseq/fairseq/benchmark/dummy_masked_lm.py