code: def test_kmeans_clusters_blobs(
      nrows, ncols, nclusters, random_state, cluster_std
  ):
      X, y = make_blobs(
          int(nrows),
          ncols,
          nclusters,
          cluster_std=cluster_std,
          shuffle=False,
          random_state=0,
      )
  
      cuml_kmeans = cuml.KMeans(
          init="k-means||",
          n_clusters=nclusters,
          random_state=random_state,
          output_type="numpy",
      )
  
      preds = cuml_kmeans.fit_predict(X)
  
      assert (
          adjusted_rand_score(cp.asnumpy(preds), cp.asnumpy(y)) >= 0.99
      )
  
imports: from cuml.internals.safe_imports import gpu_only_import
  from sklearn.preprocessing import StandardScaler
  from sklearn.metrics import adjusted_rand_score
  from sklearn import cluster
  from cuml.testing.utils import get_pattern, unit_param, quality_param, stress_param, array_equal
  from cuml.datasets import make_blobs
  import pytest
  import random
  import cuml
  import cuml.internals.logger
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 6 for calling a global function with example usage: test_kmeans_clusters_blobs(nrows, ncols, nclusters, random_state, cluster_std) without return types