code: def test_normalized_output(metric_name):
      upper_bound_1 = [0, 0, 0, 1, 1, 1]
      upper_bound_2 = [0, 0, 0, 1, 1, 1]
      metric = SUPERVISED_METRICS[metric_name]
      assert metric([0, 0, 0, 1, 1], [0, 0, 0, 1, 2]) > 0.0
      assert metric([0, 0, 1, 1, 2], [0, 0, 1, 1, 1]) > 0.0
      assert metric([0, 0, 0, 1, 2], [0, 1, 1, 1, 1]) < 1.0
      assert metric([0, 0, 0, 1, 2], [0, 1, 1, 1, 1]) < 1.0
      assert metric(upper_bound_1, upper_bound_2) == pytest.approx(1.0)
  
      lower_bound_1 = [0, 0, 0, 0, 0, 0]
      lower_bound_2 = [0, 1, 2, 3, 4, 5]
      score = np.array(
          [
              metric(lower_bound_1, lower_bound_2),
              metric(lower_bound_2, lower_bound_1),
          ]
      )
      assert not (score < 0).any()
  
file_path: scikit-learn/sklearn/metrics/cluster/tests/test_common.py
imports: from functools import partial
  from itertools import chain
  import numpy
  import pytest
  from sklearn.metrics.cluster import adjusted_mutual_info_score, adjusted_rand_score, calinski_harabasz_score, completeness_score, davies_bouldin_score, fowlkes_mallows_score, homogeneity_score, mutual_info_score, normalized_mutual_info_score, rand_score, silhouette_score, v_measure_score
  from sklearn.utils._testing import assert_allclose
label_desc: Use image node_id 4 for calling a global function with example usage: test_normalized_output(metric_name) without return types