code: def test_compute_class_weight_balanced_unordered():
      # Test compute_class_weight when classes are unordered
      classes = np.array([1, 0, 3])
      y = np.asarray([1, 0, 0, 3, 3, 3])
  
      cw = compute_class_weight("balanced", classes=classes, y=y)
      class_counts = np.bincount(y)[classes]
      assert_almost_equal(np.dot(cw, class_counts), y.shape[0])
      assert_array_almost_equal(cw, [2.0, 1.0, 2.0 / 3])
  
imports: import numpy
  import pytest
  from numpy.testing import assert_allclose
  from sklearn.datasets import make_blobs
  from sklearn.linear_model import LogisticRegression
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal
  from sklearn.utils.class_weight import compute_class_weight, compute_sample_weight
  from sklearn.utils.fixes import CSC_CONTAINERS
label_desc: Use image node_id 6 for calling a global function with example usage: test_compute_class_weight_balanced_unordered() without return types