code: def get_fairness_sample_weight(self, current_models, model_type):
      try:
          df_models, algorithms = self.df_models_algorithms(
              current_models
          )
          # df_models are sorted based on fairness metric
          df_models = df_models[df_models.model_type == model_type]
  
          if df_models.shape[0]:
              m = df_models["model"].iloc[0]
              swp = m.params["validation_strategy"][
                  "sample_weight_path"
              ]
  
              name_suffix = "_SampleWeigthing"
              name = m.params["name"]
              if "Update" in name:
                  words = name.split("_")
                  i = words.index("Update")
                  name_suffix += f"_Update_{words[i+1]}"
  
              return swp, name_suffix
      except Exception:
          pass
      return None, None
  
imports: import copy
  import json
  import logging
  import os
  import numpy
  import pandas
  from sklearn.preprocessing import OneHotEncoder
  from supervised.algorithms.catboost import catboost_eval_metric
  from supervised.algorithms.lightgbm import lightgbm_eval_metric
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, AlgorithmsRegistry
  from supervised.algorithms.xgboost import xgboost_eval_metric
  from supervised.preprocessing.label_encoder import LabelEncoder
  from supervised.preprocessing.preprocessing_categorical import PreprocessingCategorical
  from supervised.tuner.hill_climbing import HillClimbing
  from supervised.tuner.preprocessing_tuner import PreprocessingTuner
  from supervised.tuner.random_parameters import RandomParameters
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.utils import dump_data, load_data
label_desc: Use image node_id 18 for calling the MljarTuner obj's underlying member method code with example usage: obj.get_fairness_sample_weight(current_models, model_type) and returns: None,  None, swp,  name_suffix