class_docstr: Samples random number each time when sampled.
  
  Follows test/validation strategy similar to RoundRobin.
code: def __init__(
      self,
      config: Config,
      dataloaders: Dict[str, DataLoader],
      *args,
      **kwargs,
  ):
      super().__init__(config, dataloaders, *args, **kwargs)
      self._check_not_epoch_training()
  
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 RandomIterationStrategy object from inherited base classes: IterationStrategy with example: obj = RandomIterationStrategy(config, dataloaders)