code: def test_distribution(global_dtype, Estimator, parameters):
      if parameters["kernel"] == "knn":
          pytest.skip(
              "Unstable test for this configuration: changes in k-NN ordering break it."
          )
      samples = np.asarray(
          [[1.0, 0.0], [0.0, 1.0], [1.0, 1.0]], dtype=global_dtype
      )
      labels = [0, 1, -1]
      clf = Estimator(**parameters).fit(samples, labels)
      assert_allclose(
          clf.label_distributions_[2], [0.5, 0.5], atol=1e-2
      )
  
imports: import warnings
  import numpy
  import pytest
  from scipy.sparse import issparse
  from sklearn.datasets import make_classification
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.metrics.pairwise import rbf_kernel
  from sklearn.model_selection import train_test_split
  from sklearn.neighbors import NearestNeighbors
  from sklearn.semi_supervised import _label_propagation
  from sklearn.utils._testing import _convert_container, assert_allclose, assert_array_equal
label_desc: Use image node_id 2 for calling a global function with example usage: test_distribution(global_dtype, Estimator, parameters) without return types