code: def test_distribution(setup, estimator, parameters):
      samples = [[1.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
      labels = [0, 1, -1]
      clf = estimator(**parameters).fit(samples, labels)
      if parameters["kernel"] == "knn":
          return  # unstable test; changes in k-NN ordering break it
      else:
          np.testing.assert_array_almost_equal(
              np.asarray(clf.label_distributions_[2]),
              np.array([0.5, 0.5]),
              2,
          )
  
file_path: mars/mars/learn/semi_supervised/tests/test_label_propagation.py
imports: import numpy
  import pytest
  from sklearn.datasets import make_classification
  from sklearn.model_selection import train_test_split
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.utils._testing import assert_no_warnings
  from ....None import tensor
  from ...metrics.pairwise import rbf_kernel
  from ...neighbors import NearestNeighbors
  from ..None import LabelPropagation
label_desc: Use image node_id 2 for calling a global function with example usage: test_distribution(setup, estimator, parameters) without return types