code: def test_pca_initialization_not_compatible_with_sparse_input(
      csr_container,
  ):
      # Sparse input matrices cannot use PCA initialization.
      tsne = TSNE(init="pca", learning_rate=100.0, perplexity=1)
      with pytest.raises(TypeError, match="PCA initialization.*"):
          tsne.fit_transform(csr_container([[0, 5], [5, 0]]))
  
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 22 for calling a global function with example usage: test_pca_initialization_not_compatible_with_sparse_input(csr_container) without return types