class_docstr: A mixing strategy that mix data weightedly and iterate infinitely.
code: def _reset_iter(self, ds_name):
      self._iterators[ds_name] = iter(self.iterables[ds_name])
      self._counts[ds_name] = self._counts.get(ds_name, 0) + 1
      self._logging()
  
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 3 for calling the WeightedMixer obj's underlying member method code with example usage: obj._reset_iter(ds_name) without return types