code: def test_n_features_to_select_float(
      direction, n_features_to_select, expected
  ):
      # Test passing a float as n_features_to_select
      X, y = make_regression(n_features=10)
      sfs = SequentialFeatureSelector(
          LinearRegression(),
          n_features_to_select=n_features_to_select,
          direction=direction,
          cv=2,
      )
      sfs.fit(X, y)
      assert sfs.n_features_to_select_ == expected
  
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 5 for calling a global function with example usage: test_n_features_to_select_float(direction, n_features_to_select, expected) without return types