code: def test_tsne_with_different_distance_metrics(
      metric, dist_func, method
  ):
      """Make sure that TSNE works for different distance metrics"""
  
      if method == "barnes_hut" and metric == "manhattan":
          # The distances computed by `manhattan_distances` differ slightly from those
          # computed internally by NearestNeighbors via the PairwiseDistancesReduction
          # Cython code-based. This in turns causes T-SNE to converge to a different
          # solution but this should not impact the qualitative results as both
          # methods.
          # NOTE: it's probably not valid from a mathematical point of view to use the
          # Manhattan distance for T-SNE...
          # TODO: re-enable this test if/when `manhattan_distances` is refactored to
          # reuse the same underlying Cython code NearestNeighbors.
          # For reference, see:
          # https://github.com/scikit-learn/scikit-learn/pull/23865/files#r925721573
          pytest.xfail(
              "Distance computations are different for method == 'barnes_hut' and metric"
              " == 'manhattan', but this is expected."
          )
  
      random_state = check_random_state(0)
      n_components_original = 3
      n_components_embedding = 2
      X = random_state.randn(50, n_components_original).astype(
          np.float32
      )
      X_transformed_tsne = TSNE(
          metric=metric,
          method=method,
          n_components=n_components_embedding,
          random_state=0,
          n_iter=300,
          init="random",
          learning_rate="auto",
      ).fit_transform(X)
      X_transformed_tsne_precomputed = TSNE(
          metric="precomputed",
          method=method,
          n_components=n_components_embedding,
          random_state=0,
          n_iter=300,
          init="random",
          learning_rate="auto",
      ).fit_transform(dist_func(X))
      assert_array_equal(
          X_transformed_tsne, X_transformed_tsne_precomputed
      )
  
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 43 for calling a global function with example usage: test_tsne_with_different_distance_metrics(metric, dist_func, method) without return types