code: def _compute(
      self,
      predictions,
      references,
      training,
      periodicity=1,
      sample_weight=None,
      multioutput="uniform_average",
  ):
      y_pred_naive = training[:-periodicity]
      mae_naive = mean_absolute_error(
          training[periodicity:], y_pred_naive, multioutput=multioutput
      )
  
      mae_score = mean_absolute_error(
          references,
          predictions,
          sample_weight=sample_weight,
          multioutput=multioutput,
      )
  
      epsilon = np.finfo(np.float64).eps
      mase_score = mae_score / np.maximum(mae_naive, epsilon)
  
      return {"mase": mase_score}
  
file_path: evaluate/metrics/mase/mase.py
imports: import datasets
  import numpy
  from sklearn.metrics import mean_absolute_error
  import evaluate
label_desc: Use image node_id 3 for calling the Mase obj's underlying member method code with example usage: obj._compute(predictions, references, training, periodicity, sample_weight, multioutput) and returns: dict