code: def test_bad_n_features_to_select():
      n_features = 5
      X, y = make_regression(n_features=n_features)
      sfs = SequentialFeatureSelector(
          LinearRegression(), n_features_to_select=n_features
      )
      with pytest.raises(
          ValueError, match="n_features_to_select must be < n_features"
      ):
          sfs.fit(X, y)
  
imports: import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from sklearn.cluster import KMeans
  from sklearn.datasets import make_blobs, make_classification, make_regression
  from sklearn.ensemble import HistGradientBoostingRegressor
  from sklearn.feature_selection import SequentialFeatureSelector
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 1 for calling a global function with example usage: test_bad_n_features_to_select() without return types