*
 * The task runner is also responsible for releasing held threads when the library is unloaded.
 * This is for the benefit of container environments that implement code unloading.
 *
 * Most applications should share a process-wide [TaskRunner] and use queues for per-client work.
 */
class TaskRunner(
  val backend: Backend,
  internal val logger: Logger = TaskRunner.logger,
) {
  val lock: ReentrantLock = ReentrantLock()

							return errVolumeNotFound
						}
						return verr
					}
				}
			}
			return osErrToFileErr(err)
		}
		if st.Mode().IsDir() {
			return errFileNotFound
		}
		// Check if shard is truncated.
		if st.Size() < fi.Erasure.ShardFileSize(part.Size) {
			return errFileCorrupt
		}
	}

	return nil
}

// deleteFile deletes a file or a directory if its empty unless recursive

      shards to disk. `tf.train.experimental.ShardByTaskPolicy` is the default
      sharding behavior, but `tf.train.experimental.MaxShardSizePolicy` can be
      used to shard the checkpoint with a maximum shard file size. Users with
      advanced use cases can also write their own custom
      `tf.train.experimental.ShardingCallback`s.

* `tf.train.CheckpointOptions`

        * [Better with `Annotated`](https://fastapi.tiangolo.com/tutorial/path-params-numeric-validations/#better-with-annotated)
    * [Dependencies - First Steps - Share `Annotated` dependencies](https://fastapi.tiangolo.com/tutorial/dependencies/#share-annotated-dependencies)