code: def do_param_initializations(
      self,
      total_CFs,
      initialization,
      desired_range,
      desired_class,
      query_instance,
      query_instance_df_dummies,
      algorithm,
      features_to_vary,
      permitted_range,
      yloss_type,
      diversity_loss_type,
      feature_weights,
      proximity_weight,
      sparsity_weight,
      diversity_weight,
      categorical_penalty,
      verbose,
  ):
      if verbose:
          print(
              "Initializing initial parameters to the genetic algorithm..."
          )
  
      self.feature_range = self.get_valid_feature_range(
          normalized=False
      )
      if len(self.cfs) != total_CFs:
          self.do_cf_initializations(
              total_CFs,
              initialization,
              algorithm,
              features_to_vary,
              desired_range,
              desired_class,
              query_instance,
              query_instance_df_dummies,
              verbose,
          )
      else:
          self.total_CFs = total_CFs
      self.do_loss_initializations(
          yloss_type,
          diversity_loss_type,
          feature_weights,
          encoding="label",
      )
      self.update_hyperparameters(
          proximity_weight,
          sparsity_weight,
          diversity_weight,
          categorical_penalty,
      )
  
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 7 for calling the DiceGenetic obj's underlying member method code with example usage: obj.do_param_initializations(total_CFs, initialization, desired_range, desired_class, query_instance, query_instance_df_dummies, algorithm, features_to_vary, permitted_range, yloss_type, diversity_loss_type, feature_weights, proximity_weight, sparsity_weight, diversity_weight, categorical_penalty, verbose) without return types