code: def _initiate_exp_object(
      self, pyt_exp_object, sample_adultincome_query
  ):
      self.exp = pyt_exp_object  # explainer object
      # initialize required params for CF computations
      self.exp.do_cf_initializations(
          total_CFs=4, algorithm="DiverseCF", features_to_vary="all"
      )
  
      # prepare query isntance for CF optimization
      # query_instance = self.exp.data_interface.prepare_query_instance(
      #     query_instance=sample_adultincome_query, encoding='one-hot')
      # self.query_instance = query_instance.iloc[0].values
      self.query_instance = (
          self.exp.data_interface.get_ohe_min_max_normalized_data(
              sample_adultincome_query
          )
          .iloc[0]
          .values
      )
  
      self.exp.initialize_CFs(
          self.query_instance, init_near_query_instance=True
      )  # initialize CFs
      self.exp.target_cf_class = torch.tensor(
          1
      ).float()  # set desired class to 1
  
      # setting random feature weights
      np.random.seed(42)
      weights = np.random.rand(
          len(self.exp.data_interface.ohe_encoded_feature_names)
      )
      self.exp.feature_weights_list = torch.tensor(weights)
  
file_path: DiCE/tests/test_dice_interface/test_dice_pytorch.py
imports: import numpy
  import pytest
  import dice_ml
  from dice_ml.counterfactual_explanations import CounterfactualExplanations
  from dice_ml.utils import helpers
label_desc: Use image node_id 1 for calling the TestDiceTorchMethods obj's underlying member method code with example usage: obj._initiate_exp_object(pyt_exp_object, sample_adultincome_query) without return types