code: def test_multimodel():
      X = np.array([[1, 1, 1], [0, 0, 0], [-1, -1, -1]] * 100)
      y = np.array([0, 0, 1] * 100)
      models = [
          (
              "rf",
              RandomForestClassifier(n_estimators=10),
              {"max_depth": [5, 10], "min_samples_split": range(2, 10)},
          ),
          (
              "lr0",
              LogisticRegression(
                  multi_class="auto", solver="liblinear"
              ),
              {"C": [0.1, 1.0, 10]},
          ),
          (
              "lr1",
              LogisticRegression(
                  multi_class="auto", solver="liblinear"
              ),
              {"fit_intercept": [True, False], "C": expon()},
          ),
          ("nb", GaussianNB(), {}),
      ]
      clf = DistMultiModelSearch(models, n=2)
      clf.fit(X, y)
      preds = clf.predict(X[:3])
      assert np.allclose(preds, np.array([0, 0, 1]))
  
file_path: sk-dist/skdist/distribute/tests/test_search.py
imports: import sys
  import pytest
  import numpy
  from scipy.stats.distributions import expon
  from sklearn.linear_model import LogisticRegression
  from sklearn.naive_bayes import GaussianNB
  from sklearn.ensemble import RandomForestClassifier
label_desc: Use image node_id 4 for calling a global function with example usage: test_multimodel() without return types