def METHOD_NAME():
    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)