code: def _validate_counterfactual_configuration(
      self,
      query_instances,
      total_CFs,
      desired_class="opposite",
      desired_range=None,
      permitted_range=None,
      features_to_vary="all",
      stopping_threshold=0.5,
      posthoc_sparsity_param=0.1,
      posthoc_sparsity_algorithm="linear",
      verbose=False,
      **kwargs,
  ):
      if (
          len(
              self._find_features_having_missing_values(query_instances)
          )
          > 0
      ):
          raise UserConfigValidationException(
              "The query instance(s) should not have any missing values. "
              "Please impute the missing values and try again."
          )
  
      if total_CFs <= 0:
          raise UserConfigValidationException(
              "The number of counterfactuals generated per query instance (total_CFs) should be a positive integer."
          )
  
      if features_to_vary != "all":
          if len(features_to_vary) == 0:
              raise UserConfigValidationException(
                  "Some features need to be varied for generating counterfactuals."
              )
  
      if posthoc_sparsity_algorithm not in _PostHocSparsityTypes.ALL:
          raise UserConfigValidationException(
              "The posthoc_sparsity_algorithm should be {0} and not {1}".format(
                  " or ".join(_PostHocSparsityTypes.ALL),
                  posthoc_sparsity_algorithm,
              )
          )
  
      if stopping_threshold < 0.0 or stopping_threshold > 1.0:
          raise UserConfigValidationException(
              "The stopping_threshold should lie between {0} and {1}".format(
                  str(0.0), str(1.0)
              )
          )
  
      if posthoc_sparsity_param is not None and (
          posthoc_sparsity_param < 0.0 or posthoc_sparsity_param > 1.0
      ):
          raise UserConfigValidationException(
              "The posthoc_sparsity_param should lie between {0} and {1}".format(
                  str(0.0), str(1.0)
              )
          )
  
      if (
          self.model is not None
          and self.model.model_type == ModelTypes.Classifier
      ):
          if desired_range is not None:
              raise UserConfigValidationException(
                  "The desired_range parameter should not be set for classification task"
              )
  
      if (
          self.model is not None
          and self.model.model_type == ModelTypes.Regressor
      ):
          if desired_range is None:
              raise UserConfigValidationException(
                  "The desired_range parameter should be set for regression task"
              )
  
      if desired_range is not None:
          if len(desired_range) != 2:
              raise UserConfigValidationException(
                  "The parameter desired_range needs to have two numbers in ascending order."
              )
          if desired_range[0] > desired_range[1]:
              raise UserConfigValidationException(
                  "The range provided in desired_range should be in ascending order."
              )
  
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 3 for calling the ExplainerBase obj's underlying member method code with example usage: obj._validate_counterfactual_configuration(query_instances, total_CFs, desired_class, desired_range, permitted_range, features_to_vary, stopping_threshold, posthoc_sparsity_param, posthoc_sparsity_algorithm, verbose) without return types