code: def test_umap_fit_transform_score(nrows, n_feats):
      n_samples = nrows
      n_features = n_feats
  
      data, labels = make_blobs(
          n_samples=n_samples,
          n_features=n_features,
          centers=10,
          random_state=42,
      )
  
      model = umap.UMAP(n_neighbors=10, min_dist=0.1)
      cuml_model = cuUMAP(n_neighbors=10, min_dist=0.01)
  
      embedding = model.fit_transform(data)
      cuml_embedding = cuml_model.fit_transform(
          data, convert_dtype=True
      )
  
      assert not np.isnan(embedding).any()
      assert not np.isnan(cuml_embedding).any()
  
      if nrows < 500000:
          cuml_score = adjusted_rand_score(
              labels, KMeans(10).fit_predict(cuml_embedding)
          )
          score = adjusted_rand_score(
              labels, KMeans(10).fit_predict(embedding)
          )
  
          assert array_equal(score, cuml_score, 1e-2, with_sign=True)
  
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 2 for calling a global function with example usage: test_umap_fit_transform_score(nrows, n_feats) without return types