docs: def initialize_from_checkpoint(state):
    """Initialize the reinforcement learning loop from a checkpoint."""
  
    # The checkpoint's work_dir should contain the most recently trained model.
    model_paths = glob.glob(os.path.join(FLAGS.checkpoint_dir,
                                         'work_dir/model.ckpt-*.pb'))
    if len(model_paths) != 1:
      raise RuntimeError('Expected exactly one model in the checkpoint work_dir, '
                         'got [{}]'.format(', '.join(model_paths)))
    start_model_path = model_paths[0]
  
    # Copy the latest trained model into the models directory and use it on the
    # first round of selfplay.
    state.best_model_name = 'checkpoint'
    shutil.copy(start_model_path,
                os.path.join(fsdb.models_dir(), state.best_model_name + '.pb'))
  
    # Copy the training chunks.
    golden_chunks_dir = os.path.join(FLAGS.checkpoint_dir, 'golden_chunks')
    for basename in os.listdir(golden_chunks_dir):
      path = os.path.join(golden_chunks_dir, basename)
      shutil.copy(path, fsdb.golden_chunk_dir())
  
    # Copy the training files.
    work_dir = os.path.join(FLAGS.checkpoint_dir, 'work_dir')
    for basename in os.listdir(work_dir):
      path = os.path.join(work_dir, basename)
      shutil.copy(path, fsdb.working_dir())