class_docstr: A data interface for private data with meta information.
class_docstr_tok: ["A","data","interface","for","private","data","with","meta","information","."]
code: def fit_label_encoders(self):
      labelencoders = {}
      for column in self.categorical_feature_names:
          labelencoders[column] = LabelEncoder()
          labelencoders[column].fit(self.permitted_range[column])
      return labelencoders
  
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 15 for calling the PrivateData obj's underlying member method code with example usage: obj.fit_label_encoders() and returns: labelencoders