class_docstr: Samples index based on size of each dataset. Bigger datasets
  are sampled more and this strategy requires completing
  all iterators before starting new ones. Default in MMF.
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 SizeProportionalIterationStrategy obj's underlying member method code with example usage: obj.__call__() and returns: choice