code: def setup(
      self,
      features_to_vary,
      permitted_range,
      query_instance,
      feature_weights,
  ):
      self.data_interface.check_features_to_vary(
          features_to_vary=features_to_vary
      )
      self.data_interface.check_permitted_range(permitted_range)
  
      if features_to_vary == "all":
          features_to_vary = self.data_interface.feature_names
  
      if permitted_range is None:  # use the precomputed default
          self.feature_range = self.data_interface.permitted_range
          feature_ranges_orig = self.feature_range
      else:  # compute the new ranges based on user input
          (
              self.feature_range,
              feature_ranges_orig,
          ) = self.data_interface.get_features_range(permitted_range)
  
      self.check_query_instance_validity(
          features_to_vary,
          permitted_range,
          query_instance,
          feature_ranges_orig,
      )
  
      return features_to_vary
  
imports: import pickle
  from abc import ABC, abstractmethod
  from collections.abc import Iterable
  from typing import Any, List
  import numpy
  import pandas
  from raiutils.exceptions import UserConfigValidationException
  from sklearn.neighbors import KDTree
  from tqdm import tqdm
  from dice_ml.constants import ModelTypes, _PostHocSparsityTypes
  from dice_ml.counterfactual_explanations import CounterfactualExplanations
label_desc: Use image node_id 6 for calling the ExplainerBase obj's underlying member method code with example usage: obj.setup(features_to_vary, permitted_range, query_instance, feature_weights) and returns: features_to_vary