code: def test_sparse_precomputed_distance(sparse_container):
      """Make sure that TSNE works identically for sparse and dense matrix"""
      random_state = check_random_state(0)
      X = random_state.randn(100, 2)
  
      D_sparse = kneighbors_graph(
          X, n_neighbors=100, mode="distance", include_self=True
      )
      D = pairwise_distances(X)
      assert sp.issparse(D_sparse)
      assert_almost_equal(D_sparse.toarray(), D)
  
      tsne = TSNE(
          metric="precomputed",
          random_state=0,
          init="random",
          learning_rate="auto",
      )
      Xt_dense = tsne.fit_transform(D)
  
      Xt_sparse = tsne.fit_transform(sparse_container(D_sparse))
      assert_almost_equal(Xt_dense, Xt_sparse)
  
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 17 for calling a global function with example usage: test_sparse_precomputed_distance(sparse_container) without return types