class_docstr: A data interface for public data. This class is an interface to DiCE explainers
  and contains methods to transform user-fed raw data into the format a DiCE explainer
  requires, and vice versa.
code: def create_ohe_params(self, one_hot_encoded_data):
      if len(self.categorical_feature_names) > 0:
          self.ohe_encoded_feature_names = [
              x
              for x in one_hot_encoded_data.columns.tolist()
              if x not in np.array([self.outcome_name])
          ]
      else:
          # one-hot-encoded data is same as original data if there is no categorical features.
          self.ohe_encoded_feature_names = [
              feat for feat in self.feature_names
          ]
  
imports: import logging
  from collections import defaultdict
  import numpy
  import pandas
  from raiutils.exceptions import UserConfigValidationException
  from sklearn.preprocessing import LabelEncoder
  from dice_ml.data_interfaces.base_data_interface import _BaseData
  from dice_ml.utils.exception import SystemException
label_desc: Use image node_id 16 for calling the PublicData obj's underlying member method code with example usage: obj.create_ohe_params(one_hot_encoded_data) without return types