class_docstr: Helper class for managing experiment-level checkpoints.
  
  This class implements the ``checkpoint()`` method used to checkpoint
  experiment state. When called, this will serialize and write to disk
  the state of the trial runner, trial executor, and search algorithm, to
  a specified checkpoint file.
  
  The checkpoint period is automatically adjusted to
  ``max(10, time_per_checkpoint * 19)``. This means that at most 5% of the
  time (1/20) will be used for writing checkpoints, while 95% of the time
  (19/20) will be used to handle the rest of the training loop.
  
  If ``sync_every_n_trial_checkpoints`` is not None, syncing
  to cloud will be forced if any trial has checkpointed more times than
  ``sync_every_n_trial_checkpoints`` since last sync.
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code: def auto_checkpoint_enabled(self):
      return self._auto_checkpoint_enabled
  
imports: from collections import Counter
  from dataclasses import dataclass
  from pathlib import Path
  from typing import Callable, Dict, Optional, Tuple, Union
  import click
  import logging
  import os
  import time
  import warnings
  from ray.train._internal.storage import StorageContext, get_fs_and_path, _download_from_fs_path, _list_at_fs_path
  from ray.tune.experiment import Trial
  from ray.tune.impl.out_of_band_serialize_dataset import out_of_band_serialize_dataset
label_desc: Use image node_id 2 for calling the _ExperimentCheckpointManager obj's underlying member method code with example usage: obj.auto_checkpoint_enabled() and returns: self