code: def test_sparse():
      X, y = datasets.load_iris(return_X_y=True)
      X = np.hstack([X, np.reshape(range(len(X)), (len(X), 1))])
      fe = DistFeatureEliminator(
          LogisticRegression(solver="liblinear", multi_class="auto"),
          scoring="f1_weighted",
          min_features_to_select=3,
          cv=3,
      )
      fe.fit(csr_matrix(X), y)
      assert fe.predict(X)[0] == 0
  
file_path: sk-dist/skdist/distribute/tests/test_eliminate.py
imports: import numpy
  from scipy.sparse import csr_matrix
  from sklearn.linear_model import LogisticRegression
  from sklearn import datasets
label_desc: Use image node_id 5 for calling a global function with example usage: test_sparse() without return types