code: def test_fit_transform_csr_matrix(method, csr_container):
      # TODO: compare results on dense and sparse data as proposed in:
      # https://github.com/scikit-learn/scikit-learn/pull/23585#discussion_r968388186
      # X can be a sparse matrix.
      rng = check_random_state(0)
      X = rng.randn(50, 2)
      X[(rng.randint(0, 50, 25), rng.randint(0, 2, 25))] = 0.0
      X_csr = csr_container(X)
      tsne = TSNE(
          n_components=2,
          init="random",
          perplexity=10,
          learning_rate=100.0,
          random_state=0,
          method=method,
          n_iter=750,
      )
      X_embedded = tsne.fit_transform(X_csr)
      assert_allclose(
          trustworthiness(X_csr, X_embedded, n_neighbors=1),
          1.0,
          rtol=1.1e-1,
      )
  
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 11 for calling a global function with example usage: test_fit_transform_csr_matrix(method, csr_container) without return types