code: def test_trustworthiness():
      # Test trustworthiness score.
      random_state = check_random_state(0)
  
      # Affine transformation
      X = random_state.randn(100, 2)
      assert trustworthiness(X, 5.0 + X / 10.0) == 1.0
  
      # Randomly shuffled
      X = np.arange(100).reshape(-1, 1)
      X_embedded = X.copy()
      random_state.shuffle(X_embedded)
      assert trustworthiness(X, X_embedded) < 0.6
  
      # Completely different
      X = np.arange(5).reshape(-1, 1)
      X_embedded = np.array([[0], [2], [4], [1], [3]])
      assert_almost_equal(
          trustworthiness(X, X_embedded, n_neighbors=1), 0.2
      )
  
imports: import sys
  from io import StringIO
  import numpy
  import pytest
  import scipy.sparse
  from numpy.testing import assert_allclose
  from scipy.optimize import check_grad
  from scipy.spatial.distance import pdist, squareform
  from sklearn import config_context
  from sklearn.datasets import make_blobs
  from sklearn.exceptions import EfficiencyWarning
  from sklearn.manifold import TSNE, _barnes_hut_tsne
  from sklearn.manifold._t_sne import _gradient_descent, _joint_probabilities, _joint_probabilities_nn, _kl_divergence, _kl_divergence_bh, trustworthiness
  from sklearn.manifold._utils import _binary_search_perplexity
  from sklearn.metrics.pairwise import cosine_distances, manhattan_distances, pairwise_distances
  from sklearn.neighbors import NearestNeighbors, kneighbors_graph
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal, ignore_warnings, skip_if_32bit
  from sklearn.utils.fixes import CSR_CONTAINERS, LIL_CONTAINERS
label_desc: Use image node_id 7 for calling a global function with example usage: test_trustworthiness() without return types