code: def test_predict():
      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=1,
          cv=3,
          step=2,
      )
      fe.fit(X, y)
      assert np.allclose(fe.best_features_, [0, 1, 2])
  
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 4 for calling a global function with example usage: test_predict() without return types