code: def do_random_init(
      self,
      num_inits,
      features_to_vary,
      query_instance,
      desired_class,
      desired_range,
  ):
      remaining_cfs = np.zeros(
          (num_inits, self.data_interface.number_of_features)
      )
      # kx is the number of valid inits found so far
      kx = 0
      precisions = self.data_interface.get_decimal_precisions()
      while kx < num_inits:
          one_init = np.zeros(self.data_interface.number_of_features)
          for jx, feature in enumerate(
              self.data_interface.feature_names
          ):
              if feature in features_to_vary:
                  if (
                      feature
                      in self.data_interface.continuous_feature_names
                  ):
                      one_init[jx] = np.round(
                          np.random.uniform(
                              self.feature_range[feature][0],
                              self.feature_range[feature][1],
                          ),
                          precisions[jx],
                      )
                  else:
                      one_init[jx] = np.random.choice(
                          self.feature_range[feature]
                      )
              else:
                  one_init[jx] = query_instance[jx]
          if self.is_cf_valid(self.predict_fn_scores(one_init)):
              remaining_cfs[kx] = one_init
              kx += 1
      return 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 4 for calling the DiceGenetic obj's underlying member method code with example usage: obj.do_random_init(num_inits, features_to_vary, query_instance, desired_class, desired_range) and returns: remaining_cfs