class_docstr: Separately save the model with maximum value of some statistics.
  
  See docs of :class:`MinSaver` for details.
class_docstr_tok: ["Separately","save","the","model","with","maximum","value","of","some","statistics",".","See","docs","of",":","class",":","`","MinSaver","`","for","details","."]
docstr: Args:
      monitor_stat(str): the name of the statistics.
      filename (str): the name for the saved model.
          Defaults to ``max-{monitor_stat}.tfmodel``.
docstr_tok: ["Args",":","monitor_stat","(","str",")",":","the","name","of","the","statistics",".","filename","(","str",")",":","the","name","for","the","saved","model",".","Defaults","to","``","max-","{","monitor_stat","}",".tfmodel","``","."]
code: def __init__(self, monitor_stat, filename=None, checkpoint_dir=None):
      """
      Args:
          monitor_stat(str): the name of the statistics.
          filename (str): the name for the saved model.
              Defaults to ``max-{monitor_stat}.tfmodel``.
      """
      super(MaxSaver, self).__init__(
          monitor_stat,
          True,
          filename=filename,
          checkpoint_dir=checkpoint_dir,
      )
  
file_path: tensorpack/tensorpack/callbacks/saver.py
imports: import os
  from datetime import datetime
  from ..compat import tfv1
  from ..utils import fs, logger
  from .base import Callback
label_desc: Use image node_id 1 to create a new MaxSaver object from inherited base classes: MinSaver with example: obj = MaxSaver(monitor_stat, filename, checkpoint_dir)