class_docstr: Samples datasets one by one in round robin fashion.
  
  Start index can be specified in config as `start_idx`.
  
  Also defaults to size proportional sampling as roundrobin
  doesn't make sense with validation and testing splits
  as they need to finish one complete epoch.
code: def __init__(
      self,
      config: Config,
      dataloaders: Dict[str, DataLoader],
      *args,
      **kwargs,
  ):
      super().__init__(config, dataloaders, *args, **kwargs)
      self._check_not_epoch_training()
  
      if "start_idx" in self.config:
          self._current_idx = self.config.start_idx
  
imports: import logging
  import warnings
  from dataclasses import dataclass
  from typing import Dict
  import numpy
  from mmf.common.registry import registry
  from mmf.utils.configuration import get_global_config
  from mmf.utils.dataset import dataset_list_from_config
  from omegaconf import MISSING, OmegaConf
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 to create a new RoundRobinIterationStrategy object from inherited base classes: IterationStrategy with example: obj = RoundRobinIterationStrategy(config, dataloaders)