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: Computes required quantile of Absolute Deviations of features.
code: def get_quantiles_from_training_data(
      self, quantile=0.05, normalized=False
  ):
      """Computes required quantile of Absolute Deviations of features."""
  
      quantiles = {}
      if normalized is False:
          for feature in self.continuous_feature_names:
              quantiles[feature] = np.quantile(
                  abs(
                      list(set(self.data_df[feature].tolist()))
                      - np.median(
                          list(set(self.data_df[feature].tolist()))
                      )
                  ),
                  quantile,
              )
      else:
          normalized_train_df = self.normalize_data(self.data_df)
          for feature in self.continuous_feature_names:
              quantiles[feature] = np.quantile(
                  abs(
                      list(set(normalized_train_df[feature].tolist()))
                      - np.median(
                          list(
                              set(normalized_train_df[feature].tolist())
                          )
                      )
                  ),
                  quantile,
              )
      return quantiles
  
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 15 for calling the PublicData obj's underlying member method code with example usage: obj.get_quantiles_from_training_data(quantile, normalized) and returns: quantiles