code: def test_unsupervised_model_fit(n_features_to_select):
      # Make sure that models without classification labels are not being
      # validated
  
      X, y = make_blobs(n_features=4)
      sfs = SequentialFeatureSelector(
          KMeans(n_init=1),
          n_features_to_select=n_features_to_select,
      )
      sfs.fit(X)
      assert sfs.transform(X).shape[1] == 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 10 for calling a global function with example usage: test_unsupervised_model_fit(n_features_to_select) without return types