code: def test_n_features_to_select_auto(direction):
      """Check the behaviour of `n_features_to_select="auto"` with different
      values for the parameter `tol`.
      """
  
      n_features = 10
      tol = 1e-3
      X, y = make_regression(n_features=n_features, random_state=0)
      sfs = SequentialFeatureSelector(
          LinearRegression(),
          n_features_to_select="auto",
          tol=tol,
          direction=direction,
          cv=2,
      )
      sfs.fit(X, y)
  
      max_features_to_select = n_features - 1
  
      assert (
          sfs.get_support(indices=True).shape[0]
          <= max_features_to_select
      )
      assert sfs.n_features_to_select_ <= max_features_to_select
      assert sfs.transform(X).shape[1] <= max_features_to_select
      assert (
          sfs.get_support(indices=True).shape[0]
          == sfs.n_features_to_select_
      )
  
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 3 for calling a global function with example usage: test_n_features_to_select_auto(direction) without return types