code: def compute_conformity_score(
      y_val_pred: np.ndarray, y_val: np.ndarray, quantile_levels: list
  ):
      """
      Compute conformity scores based on validation set. This is only applicable to quantile regression problems.
      The scores are used to conformalize new quantile predictions.
      This is based on the paper 'Conformalized Quantile Regression (https://arxiv.org/abs/1905.03222)',
      and its implementation at 'https://github.com/yromano/cqr'.
  
      Parameters:
      -----------
      y_val_preds: numpy ndarray
          [num_samples x num_quantiles]
          Quantile estimates by model on validation set
      y_val: numpy ndarray
          [num_samples x 1]
          The target values to the validation set
      quantile_levels: list
          List of quantile levels
      Return:
      numpy ndarray: values to conformalize the new quantile predictions
      """
  
      num_samples = y_val.shape[0]
      y_val = y_val.reshape(-1)
      assert y_val_pred.shape[0] == num_samples
      assert y_val_pred.shape[1] == len(quantile_levels)
  
      conformalize_list = []
      for i, q in enumerate(quantile_levels):
          if q > 0.5:
              error_high = y_val - y_val_pred[:, i]
              error_high = np.sort(error_high, 0)
              index_high = int(np.ceil(q * (num_samples + 1))) - 1
              index_high = min(max(index_high, 0), num_samples - 1)
              conformalize = error_high[index_high]
          elif q < 0.5:
              error_low = y_val_pred[:, i] - y_val
              error_low = np.sort(error_low, 0)
              index_low = int(np.ceil((1 - q) * (num_samples + 1))) - 1
              index_low = min(max(index_low, 0), num_samples - 1)
              conformalize = -error_low[index_low]
          else:
              conformalize = 0.0
          conformalize_list.append(conformalize)
      return np.array(conformalize_list)
  
label_desc: Use image node_id 1 for calling a global function with example usage: compute_conformity_score(y_val_pred, y_val, quantile_levels) and returns: np