message: Update checkpoint_management.py
diff: @@ -76,7 +76,7 @@ def generate_checkpoint_state_proto(save_dir,
  last_preserved_timestamp: A float, indicating the number of seconds since
  the Epoch when the last preserved checkpoint was written, e.g. due to a
  `keep_checkpoint_every_n_hours` parameter (see
  - `tf.contrib.checkpoint.CheckpointManager` for an implementation).
  + `tf.train.CheckpointManager` for an implementation).
  Returns:
  CheckpointState proto with model_checkpoint_path and
  all_model_checkpoint_paths updated to either absolute paths or
  @@ -152,7 +152,7 @@ def update_checkpoint_state(save_dir,
  last_preserved_timestamp: A float, indicating the number of seconds since
  the Epoch when the last preserved checkpoint was written, e.g. due to a
  `keep_checkpoint_every_n_hours` parameter (see
  - `tf.contrib.checkpoint.CheckpointManager` for an implementation).
  + `tf.train.CheckpointManager` for an implementation).
  Raises:
  RuntimeError: If any of the model checkpoint paths conflict with the file
  containing CheckpointSate.
  @@ -196,7 +196,7 @@ def update_checkpoint_state_internal(save_dir,
  last_preserved_timestamp: A float, indicating the number of seconds since
  the Epoch when the last preserved checkpoint was written, e.g. due to a
  `keep_checkpoint_every_n_hours` parameter (see
  - `tf.contrib.checkpoint.CheckpointManager` for an implementation).
  + `tf.train.CheckpointManager` for an implementation).
  Raises:
  RuntimeError: If any of the model checkpoint paths conflict with the file
  @@ -501,7 +501,7 @@ class CheckpointManager(object):
  ```python
  import tensorflow as tf
  checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)
  - manager = tf.contrib.checkpoint.CheckpointManager(
  + manager = tf.train.CheckpointManager(
  checkpoint, directory="/tmp/model", max_to_keep=5)
  status = checkpoint.restore(manager.latest_checkpoint)
  while True:
  