class_docstr: Samples based on ratios specified as `sampling_ratios` parameter
  in the config. Default to validation/test strategy as in RoundRobin.
  
  `sampling_ratios` defines a dictionary pointing from dataset key to
  a floating ration specifying how much the dataset should be sampled.
  Floats together should sum to one.
  
  `datasets` is a list of datasets that would be sampled. This should
  a subset or same as `sampling_ratios.keys()`.
code: def __call__(self, *args, **kwargs):
      choice = np.random.choice(
          len(self.dataloaders), 1, p=self._dataset_probabilities
      )[0]
      return choice
  
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 RatiosIterationStrategy obj's underlying member method code with example usage: obj.__call__() and returns: choice