code: def reduce_to_best_decode(metrics, reduce_func):
    
    num_videos = metrics.shape[1]
    # Take mean of the metric across the frames to approximate the video
    # closest to the ground truth.
    mean_across_frames = np.mean(metrics, axis=-1)
  
    # For every sample, use the decode that has a maximum mean-metric.
    best_decode_ind = reduce_func(mean_across_frames, axis=0)
    best_metrics = metrics[best_decode_ind, np.arange(num_videos), :]
    return best_metrics, best_decode_ind
docs: Extracts the best-decode from the metrics according to reduce_func.
  
    Args:
      metrics: 3-D numpy array, shape=(num_decodes, num_samples, num_frames)
      reduce_func: callable, np.argmax or np.argmin.
    Returns:
      best_metrics: 2-D numpy array, shape=(num_samples, num_frames).
      best_decode_ind: 1-D numpy array, shape=(num_samples,)