code: def test_kfeatures_type_2():
      iris = load_iris()
      X = iris.data
      y = iris.target
      knn = KNeighborsClassifier()
      expect = "k_features must be a positive integer, tuple, or string"
      sfs = SFS(estimator=knn, verbose=0, k_features=set())
      assert_raises(AttributeError, expect, sfs.fit, X, y)
  
imports: import numpy
  import pandas
  from numpy import nan
  from numpy.testing import assert_almost_equal
  from packaging.version import Version
  from sklearn import __version__
  from sklearn.datasets import load_iris
  from sklearn.decomposition import PCA
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import accuracy_score, make_scorer, roc_auc_score
  from sklearn.model_selection import GridSearchCV, GroupKFold
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.pipeline import Pipeline
  from mlxtend.classifier import SoftmaxRegression
  from mlxtend.data import boston_housing_data
  from mlxtend.feature_selection import SequentialFeatureSelector
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 6 for calling a global function with example usage: test_kfeatures_type_2() without return types