code: def _fit(self, model):
      fi = model.explainer.model_parts(
          N=self.get_option("N"), B=self.get_option("B")
      ).permutation
  
      def q1(x):
          return x.quantile(0.25)
  
      def q3(x):
          return x.quantile(0.75)
  
      stats = fi.agg(["mean", "max", "min", q1, q3])
      full_model = stats.loc["mean", "_full_model_"]
      stats = stats.drop(
          ["_baseline_", "_full_model_"], axis=1
      ).sort_values(by="mean", axis=1, ascending=False)
      self.data = {
          "base": full_model,
          "variables": stats.columns.tolist(),
          "dropout_loss": stats.loc["mean"].tolist(),
          "min": stats.loc["min"].tolist(),
          "max": stats.loc["max"].tolist(),
          "q1": stats.loc["q1"].tolist(),
          "q3": stats.loc["q3"].tolist(),
      }
  
label_desc: Use image node_id 1 for calling the FeatureImportanceContainer obj's underlying member method code with example usage: obj._fit(model) and returns: x, x