code: def compute_video_metrics_from_predictions(predictions, decode_hparams):
    
    all_results = {}
  
  
    ssim_all_decodes, psnr_all_decodes = [], []
    for single_decode in predictions:
      args = get_zipped_dataset_from_predictions(single_decode)
      psnr_single, ssim_single = compute_one_decoding_video_metrics(*args)
      psnr_all_decodes.append(psnr_single)
      ssim_all_decodes.append(ssim_single)
    psnr_all_decodes = np.array(psnr_all_decodes)
    ssim_all_decodes = np.array(ssim_all_decodes)
    all_results.update({"PSNR": psnr_all_decodes, "SSIM": ssim_all_decodes})
    return compute_all_metrics_statistics(all_results)
docs: Computes metrics from predictions.
  
    Args:
      predictions: list of list of dicts.
                   outer length: num_decodes, inner_length: num_samples
      decode_hparams: Decode hparams. instance of HParams.
    Returns:
      statistics: dict of Tensors, key being the metric with each Tensor
                  having the shape (num_samples, num_frames).