code: def get_timestamped_export_dir(export_dir_base):
      """Builds a path to a new subdirectory within the base directory.
  
      Each export is written into a new subdirectory named using the
      current time.  This guarantees monotonically increasing version
      numbers even across multiple runs of the pipeline.
      The timestamp used is the number of seconds since epoch UTC.
  
      Args:
        export_dir_base: A string containing a directory to write the exported
            graph and checkpoints.
      Returns:
        The full path of the new subdirectory (which is not actually created yet).
  
      Raises:
        RuntimeError: if repeated attempts fail to obtain a unique timestamped
          directory name.
      """
      attempts = 0
      while attempts < MAX_DIRECTORY_CREATION_ATTEMPTS:
          export_timestamp = int(time.time())
  
          export_dir = os.path.join(
              tf.compat.as_bytes(export_dir_base),
              tf.compat.as_bytes(str(export_timestamp)),
          )
          if not tf.compat.v1.gfile.Exists(export_dir):
              # Collisions are still possible (though extremely unlikely): this
              # directory is not actually created yet, but it will be almost
              # instantly on return from this function.
              return export_dir
          time.sleep(1)
          attempts += 1
          logging.warn(
              "Export directory %s already exists; retrying (attempt %d/%d)",
              export_dir,
              attempts,
              MAX_DIRECTORY_CREATION_ATTEMPTS,
          )
      raise RuntimeError(
          "Failed to obtain a unique export directory name after "
          "%d attempts.".MAX_DIRECTORY_CREATION_ATTEMPTS
      )
  
file_path: hub/tensorflow_hub/tf_utils.py
imports: import os
  import time
  import uuid
  from absl import logging
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: get_timestamped_export_dir(export_dir_base) and returns: export_dir