code: def is_fair(self):
      if self._is_fair is not None:
          return self._is_fair
      metrics = self.get_additional_metrics()
      fm = metrics.get("fairness_metrics", {})
      for col, m in fm.items():
          if col == "fairness_optimization":
              continue
          if not m.get("is_fair", True):
              self._is_fair = False
              return False
      self._is_fair = True
      return False
  
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 22 for calling the ModelFramework obj's underlying member method code with example usage: obj.is_fair() and returns: False, self, False