code: def save(self, results_path, model_subpath):
      start_time = time.time()
      model_path = os.path.join(results_path, model_subpath)
      logger.info(f"Save the model {model_path}")
  
      type_of_predictions = (
          "validation"
          if "k_folds" not in self.validation_params
          else "out_of_folds"
      )
      predictions_fname = os.path.join(
          model_subpath, f"predictions_{type_of_predictions}.csv"
      )
      self._oof_predictions_fname = os.path.join(
          results_path, predictions_fname
      )
      predictions = self.get_out_of_folds()
      predictions.to_csv(self._oof_predictions_fname, index=False)
  
      saved = [
          os.path.join(model_subpath, l.get_fname())
          for l in self.learners
      ]
  
      with open(
          os.path.join(model_path, "framework.json"), "w"
      ) as fout:
          preprocessing = [p.to_json() for p in self.preprocessings]
          learners_params = [
              learner.get_params() for learner in self.learners
          ]
  
          desc = {
              "uid": self.uid,
              "name": self._name,
              "preprocessing": preprocessing,
              "learners": learners_params,
              "params": self.params,
              "saved": saved,
              "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,
              "joblib_version": joblib.__version__,
          }
          desc["final_loss"] = str(desc["final_loss"])
          if self._threshold is not None:
              desc["threshold"] = self._threshold
          if self._single_prediction_time is not None:
              desc[
                  "single_prediction_time"
              ] = self._single_prediction_time
          fout.write(json.dumps(desc, indent=4, cls=MLJSONEncoder))
  
      learning_curve_metric = self.learners[0].get_metric_name()
      if learning_curve_metric is None:
          learning_curve_metric = self.get_metric_name()
  
      LearningCurves.plot(
          [l.name for l in self.learners],
          learning_curve_metric,
          model_path,
          trees_in_iteration=self.additional_params.get(
              "trees_in_step"
          ),
      )
  
      # 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!")
      # I'm adding save time to total train time
      # there is always save after the training
      self.train_time += time.time() - start_time
  
imports: import copy
  import gc
  import json
  import logging
  import os
  import sys
  import time
  import uuid
  import zipfile
  import numpy
  import pandas
  from supervised.algorithms.factory import AlgorithmFactory
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, AlgorithmsRegistry
  from supervised.callbacks.callback_list import CallbackList
  from supervised.exceptions import AutoMLException
  from supervised.preprocessing.exclude_missing_target import ExcludeRowsMissingTarget
  from supervised.preprocessing.preprocessing import Preprocessing
  from supervised.utils.additional_metrics import AdditionalMetrics
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.metric import Metric
  from supervised.validation.validation_step import ValidationStep
  from supervised.utils.jsonencoder import MLJSONEncoder
  import joblib
  import optuna
  from supervised.tuner.optuna.tuner import OptunaTuner
  from supervised.utils.config import mem
  from supervised.utils.learning_curves import LearningCurves
label_desc: Use image node_id 23 for calling the ModelFramework obj's underlying member method code with example usage: obj.save(results_path, model_subpath) without return types