code: def save(self, results_path, model_subpath):
      model_path = os.path.join(results_path, model_subpath)
      logger.info(f"Save the ensemble to {model_path}")
  
      predictions = self.get_out_of_folds()
      predictions_fname = os.path.join(
          model_subpath, f"predictions_ensemble.csv"
      )
      self._oof_predictions_fname = os.path.join(
          results_path, predictions_fname
      )
      predictions.to_csv(self._oof_predictions_fname, index=False)
  
      with open(os.path.join(model_path, "ensemble.json"), "w") as fout:
          ms = []
          for selected in self.selected_models:
              ms += [
                  {
                      "model": selected["model"]._name,
                      "repeat": selected["repeat"],
                  }
              ]
  
          desc = {
              "name": self._name,
              "ml_task": self._ml_task,
              "optimize_metric": self._optimize_metric,
              "selected_models": ms,
              "predictions_fname": predictions_fname,
              "metric_name": self.get_metric_name(),
              "final_loss": self.get_final_loss(),
              "train_time": self.get_train_time(),
              "is_stacked": self._is_stacked,
          }
  
          if self._threshold is not None:
              desc["threshold"] = self._threshold
          fout.write(json.dumps(desc, indent=4, cls=MLJSONEncoder))
  
      LearningCurves.plot_for_ensemble(
          self._scores, self.metric.name, model_path
      )
  
      # call additional metics just to be sure they are computed
      self._additional_metrics = self.get_additional_metrics()
  
      AdditionalMetrics.save(
          self._additional_metrics,
          self._ml_task,
          self.model_markdown(),
          model_path,
      )
  
      with open(os.path.join(model_path, "status.txt"), "w") as fout:
          fout.write("ALL OK!")
  
imports: import copy
  import json
  import logging
  import operator
  import os
  import time
  import uuid
  import numpy
  import pandas
  from supervised.algorithms.algorithm import BaseAlgorithm
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
  from supervised.exceptions import NotTrainedException
  from supervised.model_framework import ModelFramework
  from supervised.utils.additional_metrics import AdditionalMetrics
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.metric import Metric
  from supervised.utils.jsonencoder import MLJSONEncoder
  import matplotlib.pyplot
  from tabulate import tabulate
  from supervised.utils.learning_curves import LearningCurves
label_desc: Use image node_id 25 for calling the Ensemble obj's underlying member method code with example usage: obj.save(results_path, model_subpath) without return types