code: def test_sanity(
      seed, direction, n_features_to_select, expected_selected_features
  ):
      # Basic sanity check: 3 features, only f0 and f2 are correlated with the
      # target, f2 having a stronger correlation than f0. We expect f1 to be
      # dropped, and f2 to always be selected.
  
      rng = np.random.RandomState(seed)
      n_samples = 100
      X = rng.randn(n_samples, 3)
      y = 3 * X[:, 0] - 10 * X[:, 2]
  
      sfs = SequentialFeatureSelector(
          LinearRegression(),
          n_features_to_select=n_features_to_select,
          direction=direction,
          cv=2,
      )
      sfs.fit(X, y)
      assert_array_equal(
          sfs.get_support(indices=True), expected_selected_features
      )
  
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 6 for calling a global function with example usage: test_sanity(seed, direction, n_features_to_select, expected_selected_features) without return types