code: def test_transform_protecteddataset(lfrfitmodel, ad):
      """After transformation - it should not change protected attributes - it should be same as input."""
      lftransformeddataset = lfrfitmodel.transform(ad, threshold=0.3)
      # print( ad.protected_attributes)
      print(type(lftransformeddataset.protected_attributes))
      # print("transformeddataset:" + lfttransformeddataset.protected_attributes)
      expected = True
      res = np.array_equal(
          lftransformeddataset.protected_attributes,
          ad.protected_attributes,
      )
      assert res == expected
  
file_path: AIF360/tests/test_lfr.py
imports: import os
  import sys
  import numpy
  import pytest
  from aif360.algorithms.preprocessing import LFR
  from aif360.algorithms.preprocessing.optim_preproc_helpers.data_preproc_functions import load_preproc_data_adult
label_desc: Use image node_id 8 for calling a global function with example usage: test_transform_protecteddataset(lfrfitmodel, ad) without return types