code: def test_tsne_distance_metrics(metric):
      data, labels = make_blobs(
          n_samples=1000, n_features=64, centers=5, random_state=42
      )
  
      tsne = TSNE(
          n_components=2,
          random_state=1,
          n_neighbors=DEFAULT_N_NEIGHBORS,
          method="exact",
          learning_rate_method="none",
          min_grad_norm=1e-12,
          perplexity=DEFAULT_PERPLEXITY,
          metric=metric,
      )
  
      sk_tsne = skTSNE(
          n_components=2,
          random_state=1,
          min_grad_norm=1e-12,
          method="exact",
          perplexity=DEFAULT_PERPLEXITY,
          metric=metric,
      )
  
      cuml_embedding = tsne.fit_transform(data)
      sk_embedding = sk_tsne.fit_transform(data)
      nans = np.sum(np.isnan(cuml_embedding))
      cuml_trust = trustworthiness(data, cuml_embedding, metric=metric)
      sk_trust = trustworthiness(data, sk_embedding, metric=metric)
  
      assert cuml_trust > 0.85
      assert nans == 0
      assert array_equal(sk_trust, cuml_trust, 0.05, with_sign=True)
  
imports: import pytest
  from sklearn.manifold import TSNE
  from sklearn import datasets
  from sklearn.manifold import trustworthiness
  from sklearn.datasets import make_blobs
  from sklearn.neighbors import NearestNeighbors
  from cuml.manifold import TSNE
  from cuml.neighbors import NearestNeighbors
  from cuml.metrics import pairwise_distances
  from cuml.testing.utils import array_equal, stress_param
  from cuml.internals.safe_imports import cpu_only_import
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 10 for calling a global function with example usage: test_tsne_distance_metrics(metric) without return types