class_docstr: A data interface for private data with meta information.
class_docstr_tok: ["A","data","interface","for","private","data","with","meta","information","."]
docstr: Gets the min/max value of features in normalized or de-normalized
  form. Assumes that all features are already encoded to numerical form
  such that the number of features remains the same.
  
  # TODO needs work adhere to label encoded max and to support permitted_range for
  both continuous and discrete when provided in _generate_counterfactuals.
docstr_tok: ["Gets","the","min\/max","value","of","features","in","normalized","or","de-normalized","form",".","Assumes","that","all","features","are","already","encoded","to","numerical","form","such","that","the","number","of","features","remains","the","same",".","#","TODO","needs","work","adhere","to","label","encoded","max","and","to","support","permitted_range","for","both","continuous","and","discrete","when","provided","in","_generate_counterfactuals","."]
code: def get_valid_feature_range(
      self, feature_range_input, normalized=True
  ):
      """Gets the min/max value of features in normalized or de-normalized
      form. Assumes that all features are already encoded to numerical form
      such that the number of features remains the same.
  
      # TODO needs work adhere to label encoded max and to support permitted_range for
      both continuous and discrete when provided in _generate_counterfactuals.
      """
      if normalized:
          raise NotImplementedError(
              "Normalized feature range not supported for private data interface"
          )
      return feature_range_input
  
imports: import collections
  import logging
  import sys
  from collections import defaultdict
  import numpy
  import pandas
  from sklearn.preprocessing import LabelEncoder
  from dice_ml.data_interfaces.base_data_interface import _BaseData
label_desc: Use image node_id 26 for calling the PrivateData obj's underlying member method code with example usage: obj.get_valid_feature_range(feature_range_input, normalized) and returns: feature_range_input