class_docstr: A mixing strategy that mix data weightedly and iterate infinitely.
code: def __init__(self, iterables, weights):
      super().__init__(iterables, weights)
      self._iterators = {}
      self._counts = {}
      for ds_name in self.iterables.keys():
          self._reset_iter(ds_name)
  
imports: import torch
  from itertools import cycle
  from onmt.constants import CorpusTask, ModelTask
  from onmt.inputters.text_corpus import get_corpora, build_corpora_iters
  from onmt.inputters.text_utils import text_sort_key, process, numericalize, tensorify, _addcopykeys
  from onmt.transforms import make_transforms
  from onmt.utils.logging import init_logger, logger
  from onmt.utils.misc import RandomShuffler
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 to create a new WeightedMixer object from inherited base classes: MixingStrategy with example: obj = WeightedMixer(iterables, weights)