class_docstr: Abstract class defining an interface for training loggers.
docstr: Log the stats for one epoch.
  
  Arguments
  ---------
  stats_meta : dict of str:scalar pairs
      Meta information about the stats (e.g., epoch, learning-rate, etc.).
  train_stats : dict of str:list pairs
      Each loss type is represented with a str : list pair including
      all the values for the training pass.
  valid_stats : dict of str:list pairs
      Each loss type is represented with a str : list pair including
      all the values for the validation pass.
  test_stats : dict of str:list pairs
      Each loss type is represented with a str : list pair including
      all the values for the test pass.
  verbose : bool
      Whether to also put logging information to the standard logger.
docstr_tok: ["Log","the","stats","for","one","epoch",".","Arguments","--","--","--","--","-","stats_meta",":","dict","of","str",":","scalar","pairs","Meta","information","about","the","stats","(","e.g.",",","epoch",",","learning-rate",",","etc.",")",".","train_stats",":","dict","of","str",":","list","pairs","Each","loss","type","is","represented","with","a","str",":","list","pair","including","all","the","values","for","the","training","pass",".","valid_stats",":","dict","of","str",":","list","pairs","Each","loss","type","is","represented","with","a","str",":","list","pair","including","all","the","values","for","the","validation","pass",".","test_stats",":","dict","of","str",":","list","pairs","Each","loss","type","is","represented","with","a","str",":","list","pair","including","all","the","values","for","the","test","pass",".","verbose",":","bool","Whether","to","also","put","logging","information","to","the","standard","logger","."]
code: def log_stats(
      self,
      stats_meta,
      train_stats=None,
      valid_stats=None,
      test_stats=None,
      verbose=False,
  ):
      """Log the stats for one epoch.
  
      Arguments
      ---------
      stats_meta : dict of str:scalar pairs
          Meta information about the stats (e.g., epoch, learning-rate, etc.).
      train_stats : dict of str:list pairs
          Each loss type is represented with a str : list pair including
          all the values for the training pass.
      valid_stats : dict of str:list pairs
          Each loss type is represented with a str : list pair including
          all the values for the validation pass.
      test_stats : dict of str:list pairs
          Each loss type is represented with a str : list pair including
          all the values for the test pass.
      verbose : bool
          Whether to also put logging information to the standard logger.
      """
      raise NotImplementedError
  
imports: import logging
  import torch
  import os
  from speechbrain.utils.distributed import main_process_only, if_main_process
label_desc: Use image node_id 1 for calling the TrainLogger obj's underlying member method code with example usage: obj.log_stats(stats_meta, train_stats, valid_stats, test_stats, verbose) without return types