code: def test_blobs_cluster(nrows, n_feats):
      data, labels = datasets.make_blobs(
          n_samples=nrows, n_features=n_feats, centers=5, random_state=0
      )
      embedding = cuUMAP().fit_transform(data, convert_dtype=True)
  
      if nrows < 500000:
          score = adjusted_rand_score(
              labels, KMeans(5).fit_predict(embedding)
          )
          assert score == 1.0
  
imports: import platform
  import pytest
  import copy
  import joblib
  from sklearn.metrics import adjusted_rand_score
  from sklearn.manifold import trustworthiness
  from sklearn.datasets import make_blobs
  from sklearn.cluster import KMeans
  from sklearn.neighbors import NearestNeighbors
  from sklearn import datasets
  from cuml.internals import logger
  from cuml.metrics import pairwise_distances
  from cuml.testing.utils import array_equal, unit_param, quality_param, stress_param
  from cuml.manifold.umap import UMAP
  from cuml.internals.safe_imports import cpu_only_import
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 1 for calling a global function with example usage: test_blobs_cluster(nrows, n_feats) without return types