docs: def get_unparsed_moves_from_last_n_games(games, games_nr, n,
                                           moves=2**21,
                                           shuffle=True,
                                           column_family=TFEXAMPLE,
                                           column='example',
                                           values_only=True):
      """Get a dataset of serialized TFExamples from the last N games.
  
      Args:
        games, games_nr: GameQueues of the regular selfplay and calibration
          (aka 'no resign') games to sample from.
        n:  an integer indicating how many past games should be sourced.
        moves:  an integer indicating how many moves should be sampled
          from those N games.
        column_family:  name of the column family containing move examples.
        column:  name of the column containing move examples.
        shuffle:  if True, shuffle the selected move examples.
        values_only: if True, return only column values, no row keys.
  
      Returns:
        A dataset containing no more than `moves` examples, sampled
          randomly from the last `n` games in the table.
      """
      # The prefixes and suffixes below have the following meanings:
      #   ct_: count
      #   fr_: fraction
      #    _r: resign (ordinary)
      #   _nr: no-resign
      ct_r, ct_nr = 9, 1
      ct_total = ct_r + ct_nr
      fr_r = ct_r / ct_total
      fr_nr = ct_nr / ct_total
      resign = games.moves_from_last_n_games(
          math.ceil(n * fr_r),
          math.ceil(moves * fr_r),
          shuffle,
          column_family, column)
      no_resign = games_nr.moves_from_last_n_games(
          math.floor(n * fr_nr),
          math.floor(moves * fr_nr),
          shuffle,
          column_family, column)
      selection = np.array([0] * ct_r + [1] * ct_nr, dtype=np.int64)
      choice = tf.data.Dataset.from_tensor_slices(selection).repeat().take(moves)
      ds = tf.contrib.data.choose_from_datasets([resign, no_resign], choice)
      if shuffle:
          ds = ds.shuffle(len(selection) * 2)
      if values_only:
          ds = ds.map(lambda row_name, s: s)
      return ds