code: def test_sparse_support(csr_container):
      # Make sure sparse data is supported
  
      X, y = make_regression(n_features=10)
      X = csr_container(X)
      sfs = SequentialFeatureSelector(
          LinearRegression(), n_features_to_select="auto", cv=2
      )
      sfs.fit(X, y)
      sfs.transform(X)
  
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 7 for calling a global function with example usage: test_sparse_support(csr_container) without return types