code: def log_training_progress(
      stats_dict,
      params_dict,
      epoch,
      steps_completed,
      total_steps,
      log_freq,
      loggers,
  ):
      """Log information about the training progress, and the distribution of the weight tensors.
  
      Args:
          stats_dict: A tuple of (group_name, dict(var_to_log)).  Grouping statistics variables is useful for logger
            backends such as TensorBoard.  The dictionary of var_to_log has string key, and float values.
            For example:
                stats = ('Peformance/Validation/',
                         OrderedDict([('Loss', vloss),
                                      ('Top1', top1),
                                      ('Top5', top5)]))
          params_dict: A parameter dictionary, such as the one returned by model.named_parameters()
          epoch: The current epoch
          steps_completed: The current step in the epoch
          total_steps: The total number of training steps taken so far
          log_freq: The number of steps between logging records
          loggers: A list of loggers to send the log info to
      """
      if loggers is None:
          return
      if not isinstance(loggers, list):
          loggers = [loggers]
      for logger in loggers:
          logger.log_training_progress(
              stats_dict,
              epoch,
              steps_completed,
              total_steps,
              freq=log_freq,
          )
          logger.log_weights_distribution(params_dict, steps_completed)
  
imports: import argparse
  from collections import OrderedDict
  from copy import deepcopy
  import logging
  import operator
  import random
  import numpy
  import torch
  import torch.nn
  import torch.backends.cudnn
  import yaml
  import inspect
label_desc: Use image node_id 35 for calling a global function with example usage: log_training_progress(stats_dict, params_dict, epoch, steps_completed, total_steps, log_freq, loggers) without return types