code: def test_invalid_k_features():
      iris = load_iris()
      X = iris.data
      y = iris.target
      lr = SoftmaxRegression(random_seed=1)
  
      sfs1 = SFS(lr, k_features=(1, 2, 3), scoring="accuracy")
      expect = "k_features tuple must consist of 2 elements, a min and a max value."
      assert_raises(AttributeError, expect, sfs1.fit, X, y)
  
      sfs1 = SFS(lr, k_features="something", scoring="accuracy")
      expect = 'If a string argument is provided, it must be "best" or "parsimonious"'
      assert_raises(AttributeError, expect, sfs1.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 43 for calling a global function with example usage: test_invalid_k_features() without return types