code: def compute_all_metrics_statistics(all_results):
    
    statistics = {}
    decode_inds = {}
    all_metrics = all_results.keys()
  
    for key in all_metrics:
      values = all_results[key]
      statistics[key + "_MEAN"] = np.mean(values, axis=0)
      statistics[key + "_STD"] = np.std(values, axis=0)
      min_stats, min_decode_ind = reduce_to_best_decode(values, np.argmin)
      statistics[key + "_MIN"] = min_stats
      decode_inds[key + "_MIN_DECODE"] = min_decode_ind
      max_stats, max_decode_ind = reduce_to_best_decode(values, np.argmax)
      statistics[key + "_MAX"] = max_stats
      decode_inds[key + "_MAX_DECODE"] = max_decode_ind
  
    # Computes mean of each statistic across the dataset.
    for key in statistics:
      statistics[key] = np.mean(statistics[key], axis=0)
    return statistics, decode_inds
docs: Computes statistics of metrics across multiple decodings.
  
    Args:
      all_results: dict of 3-D numpy arrays.
                   Each array has shape=(num_decodes, num_samples, num_frames).
    Returns:
      statistics: dict of 1-D numpy arrays, shape=(num_frames).
                  First the statistic (max/mean/std) is computed across the
                  decodes, then the mean is taken across num_samples.
      decode_inds: dict of 1-D numpy arrays, shape=(num_samples,)
                   Each element represents the index of the decode corresponding
                   to the best statistic.