code: def test_skope_rules():
      """Check various parameter settings."""
      X_train = [
          [-2, -1],
          [-1, -1],
          [-1, -2],
          [1, 1],
          [1, 2],
          [2, 1],
          [6, 3],
          [-4, -7],
      ]
      y_train = [0] * 6 + [1] * 2
      X_test = np.array([[2, 1], [1, 1]])
  
      grid = ParameterGrid(
          {
              "precision_min": [0.0],
              "recall_min": [0.0],
              "n_estimators": [1],
              "max_samples": [0.5, 4],
              "max_samples_features": [0.5, 2],
              "bootstrap": [True, False],
              "bootstrap_features": [True, False],
              "max_depth": [2],
              "max_features": [None, 1, 0.1],
              "min_samples_split": [2, 0.1],
              "n_jobs": [-1, 2],
          }
      )
  
      with suppress_warnings():
          for params in grid:
              SkopeRulesClassifier(random_state=rng, **params).fit(
                  X_train, y_train, feature_names=["a", "b"]
              ).predict(X_test)
  
      # additional parameters:
      SkopeRulesClassifier(
          n_estimators=50,
          max_samples=1.0,
          recall_min=0.0,
          precision_min=0.0,
      ).fit(X_train, y_train).predict(X_test)
  
file_path: imodels/tests/skope_rules_test.py
imports: import numpy
  import pytest
  from numpy.testing import assert_array_equal, assert_no_warnings, assert_raises, suppress_warnings, assert_warns
  from sklearn.datasets import load_iris, make_blobs
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import ParameterGrid
  from sklearn.utils import check_random_state
  from imodels.rule_set.skope_rules import SkopeRulesClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: test_skope_rules() without return types