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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def compute_all_metrics_statistics(all_results):
 """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. """

  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