code: def do_KD_init(
      self,
      features_to_vary,
      query_instance,
      cfs,
      desired_class,
      desired_range,
  ):
      cfs = self.label_encode(cfs)
      cfs = cfs.reset_index(drop=True)
  
      self.cfs = np.zeros(
          (self.population_size, self.data_interface.number_of_features)
      )
      for kx in range(self.population_size):
          if kx >= len(cfs):
              break
          one_init = np.zeros(self.data_interface.number_of_features)
          for jx, feature in enumerate(
              self.data_interface.feature_names
          ):
              if feature not in features_to_vary:
                  one_init[jx] = query_instance[jx]
              else:
                  if (
                      feature
                      in self.data_interface.continuous_feature_names
                  ):
                      if (
                          self.feature_range[feature][0]
                          <= cfs.iat[kx, jx]
                          <= self.feature_range[feature][1]
                      ):
                          one_init[jx] = cfs.iat[kx, jx]
                      else:
                          if (
                              self.feature_range[feature][0]
                              <= query_instance[jx]
                              <= self.feature_range[feature][1]
                          ):
                              one_init[jx] = query_instance[jx]
                          else:
                              one_init[jx] = np.random.uniform(
                                  self.feature_range[feature][0],
                                  self.feature_range[feature][1],
                              )
                  else:
                      if cfs.iat[kx, jx] in self.feature_range[feature]:
                          one_init[jx] = cfs.iat[kx, jx]
                      else:
                          if (
                              query_instance[jx]
                              in self.feature_range[feature]
                          ):
                              one_init[jx] = query_instance[jx]
                          else:
                              one_init[jx] = np.random.choice(
                                  self.feature_range[feature]
                              )
          self.cfs[kx] = one_init
          kx += 1
  
      new_array = [tuple(row) for row in self.cfs]
      uniques = np.unique(new_array, axis=0)
  
      if len(uniques) != self.population_size:
          remaining_cfs = self.do_random_init(
              self.population_size - len(uniques),
              features_to_vary,
              query_instance,
              desired_class,
              desired_range,
          )
          self.cfs = np.concatenate([uniques, remaining_cfs])
  
imports: import copy
  import random
  import timeit
  import numpy
  import pandas
  from raiutils.exceptions import UserConfigValidationException
  from dice_ml import diverse_counterfactuals
  from dice_ml.constants import ModelTypes
  from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
label_desc: Use image node_id 5 for calling the DiceGenetic obj's underlying member method code with example usage: obj.do_KD_init(features_to_vary, query_instance, cfs, desired_class, desired_range) without return types