code: def data_X_y_sf(
      data_X_sf, request
  ):  # sf is an abbreviation for sensitive features
      y_transform = request.param
      data_X_sf._replace(y=y_transform(data_X_sf.y))
      return data_X_sf
  
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 5 for calling a global function with example usage: data_X_y_sf(data_X_sf, request) and returns: data_X_sf