code: def log_model_buffers(
      model,
      buffer_names,
      tag_prefix,
      epoch,
      steps_completed,
      total_steps,
      log_freq,
      loggers=(),
  ):
      """
      Log values of model buffers. 'buffer_names' is a list of buffers to be logged (which not necessarily exist
      in all layers in the model).
  
      USE WITH CARE:
          * This logger logs each value within the buffers. As such, while any buffer can be passed
            it is not really intended for big buffers such as model weights.
          * Special attention is needed when using this using this functionality in TensorBoardLogger, as it could
            significantly slow down the load time of TensorBard. Please see the documentation of 'log_model_buffers'
            in that class.
  
      Args:
          model: Model containing buffers to be logged
          buffer_names: Names of buffers to be logged. Expected to be
          tag_prefix: Prefix to be used before buffer name by logger
          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: An iterable of loggers to send the log info to
      """
      for logger in loggers:
          logger.log_model_buffers(
              model,
              buffer_names,
              tag_prefix,
              epoch,
              steps_completed,
              total_steps,
              log_freq,
          )
  
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 38 for calling a global function with example usage: log_model_buffers(model, buffer_names, tag_prefix, epoch, steps_completed, total_steps, log_freq, loggers) without return types