class_docstr: A mixing strategy that mix data weightedly and iterate infinitely.
docstr: Report corpora loading statistics.
code: def _logging(self):
      """Report corpora loading statistics."""
      msgs = []
      # patch to log stdout spawned processes of dataloader
      logger = init_logger()
      for ds_name, ds_count in self._counts.items():
          msgs.append(f"\t\t\t* {ds_name}: {ds_count}")
      logger.info("Weighted corpora loaded so far:\n" + "\n".join(msgs))
  
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 2 for calling the WeightedMixer obj's underlying member method code with example usage: obj._logging() without return types