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.
docstr: Gets the min/max value of features in normalized or de-normalized form.
code: def get_minx_maxx(self, normalized=True):
      """Gets the min/max value of features in normalized or de-normalized form."""
      minx = np.array([[0.0] * len(self.ohe_encoded_feature_names)])
      maxx = np.array([[1.0] * len(self.ohe_encoded_feature_names)])
  
      for idx, feature_name in enumerate(self.continuous_feature_names):
          max_value = self.data_df[feature_name].max()
          min_value = self.data_df[feature_name].min()
  
          if normalized:
              minx[0][idx] = (
                  self.permitted_range[feature_name][0] - min_value
              ) / (max_value - min_value)
              maxx[0][idx] = (
                  self.permitted_range[feature_name][1] - min_value
              ) / (max_value - min_value)
          else:
              minx[0][idx] = self.permitted_range[feature_name][0]
              maxx[0][idx] = self.permitted_range[feature_name][1]
      return minx, maxx
  
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 12 for calling the PublicData obj's underlying member method code with example usage: obj.get_minx_maxx(normalized) and returns: minx,  maxx