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)
        """
        item_size = max(self.args.src_len, self.args.tgt_len)
        if self.args.batch_size is not None:
            bsz = self.args.batch_size
        else:
            bsz = max(1, self.args.max_tokens // item_size)
        tgt = torch.stack([self.dummy_tgt for _ in range(bsz)])
        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.args.src_len, dtype=torch.long
                    ),
                    "prev_output_tokens": tgt.clone(),
                },
                "target": tgt,
                "nsentences": bsz,
                "ntokens": bsz * self.args.tgt_len,
            },
            num_items=self.args.dataset_size,
            item_size=item_size,
        )
Load a given dataset split.
        Args:
            split (str): name of the split (e.g., train, valid, test)
        
structured_prompting/fairseq-version/fairseq/fairseq/benchmark/dummy_mt.py