docstr: Load a given dataset split.
  Args:
      split (str): name of the split (e.g., train, valid, test)
docstr_tok: ["Load","a","given","dataset","split",".","Args",":","split","(","str",")",":","name","of","the","split","(","e.g.",",","train",",","valid",",","test",")"]
code: 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,
      )
  
file_path: fairseq/fairseq/benchmark/dummy_masked_lm.py
imports: import logging
  from dataclasses import dataclass, field
  from typing import Optional
  import torch
  from omegaconf import II
  from .dummy_dataset import DummyDataset
  from fairseq.data import Dictionary
  from fairseq.dataclass import FairseqDataclass
  from fairseq.tasks import FairseqTask, register_task
label_desc: Use image node_id 2 for calling the DummyMaskedLMTask obj's underlying member method code with example usage: obj.load_dataset(split, epoch, combine) without return types