code: def pareto_front(
      values: np.ndarray, *, is_loss: bool = True
  ) -> np.ndarray:
      """Calculate the pareto front
  
      source from: https://stackoverflow.com/a/40239615
  
      Note
      ----
      Works on the assumption that every value is either something to minimize or
      something to maximize, based on ``is_loss``.
  
      Parameters
      ----------
      values: np.ndarray [n_models, n_metrics]
          The value for each of the metrics
  
      is_loss: bool = True
          Whether the metrics are a loss or a score
  
      Returns
      -------
      np.ndarray
          A boolean mask where true indicates if the model on the pareto front
      """
      op = operator.lt if is_loss else operator.gt
  
      is_efficient = np.ones(values.shape[0], dtype=bool)
      for i, c in enumerate(values):
          if is_efficient[i]:
              # Keep any point with a lower cost
              is_efficient[is_efficient] = np.any(
                  op(values[is_efficient], c), axis=1
              )
  
              # And keep self
              is_efficient[i] = True
  
      return is_efficient
  
label_desc: Use image node_id 1 for calling a global function with example usage: pareto_front(values) and returns: is_efficient