class_docstr: Helper class for async checkpoint.
class_docstr_tok: ["Helper","class","for","async","checkpoint","."]
docstr: An integer variable numbering the checkpoint events.
  
  This is maintained by the underlying tf.train.Checkpoing object employed by
  AsyncCheckpoint class. The number starts at 0 and gets incremented for each
  checkpoint event.
  
  Returns:
    The save counter variable.
docstr_tok: ["An","integer","variable","numbering","the","checkpoint","events",".","This","is","maintained","by","the","underlying","tf.train.Checkpoing","object","employed","by","AsyncCheckpoint","class",".","The","number","starts","at","0","and","gets","incremented","for","each","checkpoint","event",".","Returns",":","The","save","counter","variable","."]
code: def save_counter(self):
      """An integer variable numbering the checkpoint events.
  
      This is maintained by the underlying tf.train.Checkpoing object employed by
      AsyncCheckpoint class. The number starts at 0 and gets incremented for each
      checkpoint event.
  
      Returns:
        The save counter variable.
      """
      return self.checkpointer().save_counter
  
imports: import atexit
  import copy
  import queue
  import threading
  import time
  import weakref
  from absl import logging
  from tensorflow.python.checkpoint import checkpoint_context
  from tensorflow.python.checkpoint import trackable_view
  from tensorflow.python.distribute import device_util
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.eager import executor
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import variables
  from tensorflow.python.saved_model.pywrap_saved_model import metrics
  from tensorflow.python.trackable import base
  from tensorflow.python.util import object_identity
label_desc: Use image node_id 9 for calling the AsyncCheckpointHelper obj's underlying member method code with example usage: obj.save_counter() and returns: self