class_docstr: Manages multiple checkpoints by keeping some and deleting unneeded ones.
  
  Example usage:
  
  ```python
  import tensorflow as tf
  checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)
  manager = tf.train.CheckpointManager(
      checkpoint, directory="/tmp/model", max_to_keep=5)
  status = checkpoint.restore(manager.latest_checkpoint)
  while True:
    # train
    manager.save()
  ```
  
  `CheckpointManager` preserves its own state across instantiations (see the
  `__init__` documentation for details). Only one should be active in a
  particular directory at a time.
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docstr: A common prefix for all checkpoints saved with this manager.
  
  For example, if `directory` (a constructor argument) were `"/tmp/tf-model"`,
  `prefix` would be `"/tmp/tf-model/ckpt"` and checkpoints would generally be
  numbered `"/tmp/tf-model/ckpt-1"`, `"/tmp/tf-model/ckpt-2"`, and so on. Each
  checkpoint has several associated files
  (e.g. `"/tmp/tf-model/ckpt-2.index"`).
  
  Returns:
    A string prefix.
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code: def _prefix(self):
      """A common prefix for all checkpoints saved with this manager.
  
      For example, if `directory` (a constructor argument) were `"/tmp/tf-model"`,
      `prefix` would be `"/tmp/tf-model/ckpt"` and checkpoints would generally be
      numbered `"/tmp/tf-model/ckpt-1"`, `"/tmp/tf-model/ckpt-2"`, and so on. Each
      checkpoint has several associated files
      (e.g. `"/tmp/tf-model/ckpt-2.index"`).
  
      Returns:
        A string prefix.
      """
      return self._checkpoint_prefix
  
imports: import collections
  import copy
  import os.path
  import re
  import time
  from google.protobuf import text_format
  from tensorflow.core.protobuf import saver_pb2
  from tensorflow.python.checkpoint import checkpoint_options
  from tensorflow.python.eager import context
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.lib.io import file_io
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.training import training_util
  from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState
  from tensorflow.python.util import compat
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 8 for calling the CheckpointManager obj's underlying member method code with example usage: obj._prefix() and returns: self