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 __call__(self, *args, **kwargs):
      nxt = self._current_idx
      self._current_idx = (self._current_idx + 1) % len(
          self.dataloaders
      )
      return nxt
  
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 2 for calling the RoundRobinIterationStrategy obj's underlying member method code with example usage: obj.__call__() and returns: nxt