code: def test_preserve_trustworthiness_approximately_with_precomputed_distances():
      # Nearest neighbors should be preserved approximately.
      random_state = check_random_state(0)
      for i in range(3):
          X = random_state.randn(80, 2)
          D = squareform(pdist(X), "sqeuclidean")
          tsne = TSNE(
              n_components=2,
              perplexity=2,
              learning_rate=100.0,
              early_exaggeration=2.0,
              metric="precomputed",
              random_state=i,
              verbose=0,
              n_iter=500,
              init="random",
          )
          X_embedded = tsne.fit_transform(D)
          t = trustworthiness(
              D, X_embedded, n_neighbors=1, metric="precomputed"
          )
          assert t > 0.95
  
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 12 for calling a global function with example usage: test_preserve_trustworthiness_approximately_with_precomputed_distances() without return types