code: def test_sparse_precomputed_metric_umap_trustworthiness():
      data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
      dmat = scipy.sparse.csr_matrix(pairwise_distances(data))
      embedding = UMAP(
          n_neighbors=10,
          min_dist=0.01,
          random_state=42,
          n_epochs=100,
          metric="precomputed",
      ).fit_transform(dmat)
      trust = trustworthiness(data, embedding, n_neighbors=10)
      assert trust >= 0.75, (
          "Insufficiently trustworthy embedding for"
          "nn dataset: {}".format(trust)
      )
  
file_path: umap/umap/tests/test_umap_trustworthiness.py
imports: from umap import UMAP
  from sklearn.datasets import make_blobs
  from sklearn.metrics import pairwise_distances
  import numpy
  import scipy.sparse
label_desc: Use image node_id 10 for calling a global function with example usage: test_sparse_precomputed_metric_umap_trustworthiness() without return types