*
 *
 */
@Deprecated
public class MetadataGraph {
    public static final int DEFAULT_VERTICES = 32;
    public static final int DEFAULT_EDGES = 64;

    // flags to indicate the granularity of vertices
    private boolean versionedVertices = false;
    private boolean scopedVertices = false;
    /**
     * the entry point we started building the graph from
     */
    MetadataGraphVertex entry;

 * to that of {@code ConcurrentHashMap} in a reusable form, and extends it for semaphores and
 * read-write locks. Conceptually, lock striping is the technique of dividing a lock into many
 * <i>stripes</i>, increasing the granularity of a single lock and allowing independent operations
 * to lock different stripes and proceed concurrently, instead of creating contention for a single
 * lock.
 *

   * write, and twice for each cache loading computation (once prior to loading the new value, and
   * once after loading completes). Much internal cache management is performed at the segment
   * granularity. For example, access queues and write queues are kept per segment when they are
   * required by the selected eviction algorithm. As such, when writing unit tests it is not

        as a tuple.
    *   Added `tf.keras.utils.split_dataset` utility to split a `Dataset` object
        or a list/tuple of arrays into two `Dataset` objects (e.g. train/test).
    *   Added step granularity to `BackupAndRestore` callback for handling
        distributed training failures & restarts. The training state can now be
        restored at the exact epoch and step at which it was previously saved
        before failing.