code: def create_categorical_map(data):
      categorical_indices = kBytesIndices
      # Takes form {index : number of unique values}
      categorical_map = {}
      for index in categorical_indices:
          categorical_map[index] = np.unique(data[:, [index]]).shape[0]
      return categorical_map
  
file_path: tcav/tcav/tcav_examples/discrete/kdd99_model.py
imports: from __future__ import division
  from __future__ import print_function
  import os
  import tensorflow
  import argparse
  import numpy
  from sklearn.model_selection import train_test_split
  import sklearn.datasets
  from sklearn.preprocessing import OrdinalEncoder
label_desc: Use image node_id 5 for calling a global function with example usage: create_categorical_map(data) and returns: categorical_map