code: def is_invalid_transformation(**kwargs):
      sensitive_feature_transform = kwargs["data_sf"]
      sensitive_features = kwargs["data"].sensitive_features
  
      # Skip combinations where the multi-column sensitive features would have to be compressed
      # into a one-dimensional data structure.
      if (
          sensitive_features == sensitive_features_ex3
      ).all() and sensitive_feature_transform in [
          ensure_list_1d,
          ensure_series,
      ]:
          return True
      return False
  
imports: from collections import defaultdict, namedtuple
  from test.unit.input_convertors import _map_into_single_column, ensure_dataframe, ensure_list_1d, ensure_ndarray, ensure_ndarray_2d, ensure_series
  import numpy
  import pandas
  import pytest
  from sklearn.base import BaseEstimator, ClassifierMixin
  from fairlearn.postprocessing._constants import LABEL_KEY, SCORE_KEY, SENSITIVE_FEATURE_KEY
  from fairlearn.postprocessing._threshold_operation import ThresholdOperation
label_desc: Use image node_id 2 for calling a global function with example usage: is_invalid_transformation() and returns: False, True