code: def _relative_error(y_true, y_pred, y_pred_benchmark):
      """Relative error for observations to benchmark method.
  
      Parameters
      ----------
      y_true : pandas Series, pandas DataFrame or NumPy array of
              shape (fh,) or (fh, n_outputs) where fh is the forecasting horizon
          Ground truth (correct) target values.
  
      y_pred : pandas Series, pandas DataFrame or NumPy array of
              shape (fh,) or (fh, n_outputs) where fh is the forecasting horizon
          Forecasted values.
  
      y_pred_benchmark : pd.Series, pd.DataFrame or np.array of shape (fh,) or \
               (fh, n_outputs) where fh is the forecasting horizon, default=None
          Forecasted values from benchmark method.
  
      Returns
      -------
      relative_error : float
          relative error
  
      References
      ----------
      Hyndman, R. J and Koehler, A. B. (2006). "Another look at measures of \
      forecast accuracy", International Journal of Forecasting, Volume 22, Issue 4.
      """
      denominator = np.where(
          y_true - y_pred_benchmark >= 0,
          np.maximum((y_true - y_pred_benchmark), EPS),
          np.minimum((y_true - y_pred_benchmark), -EPS),
      )
      return (y_true - y_pred) / denominator
  
imports: import numpy
  from scipy.stats import gmean
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_error
  from sklearn.metrics import median_absolute_error
  from sklearn.metrics._regression import _check_reg_targets
  from sklearn.utils.stats import _weighted_percentile
  from sklearn.utils.validation import check_consistent_length
  from sktime.utils.stats import _weighted_geometric_mean
label_desc: Use image node_id 25 for calling a global function with example usage: _relative_error(y_true, y_pred, y_pred_benchmark) and returns: unknown