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 _get_given_datasets(self):
      config = registry.get("config")
      datasets = None
      if config is not None and "datasets" not in config:
          datasets = dataset_list_from_config(config)
  
      if datasets is None or len(datasets) == 0:
          warnings.warn(
              "Either 'datasets' key not in global config or is a empty list. "
              + "Moving forward with dataset list same as sampling ratios"
          )
          return list(self.config.get("sampling_ratios", {}).keys())
      else:
          return datasets
  
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 3 for calling the RatiosIterationStrategy obj's underlying member method code with example usage: obj._get_given_datasets() and returns: list, datasets