code: def test_kmeans_sklearn_comparison_default(name, nrows, random_state):
      default_base = {
          "quantile": 0.3,
          "eps": 0.3,
          "damping": 0.9,
          "preference": -200,
          "n_neighbors": 10,
          "n_clusters": 3,
      }
  
      pat = get_pattern(name, nrows)
  
      params = default_base.copy()
      params.update(pat[1])
  
      cuml_kmeans = cuml.KMeans(
          n_clusters=params["n_clusters"],
          random_state=random_state,
          n_init=10,
          output_type="numpy",
      )
  
      X, y = pat[0]
  
      X = StandardScaler().fit_transform(X)
  
      cu_y_pred = cuml_kmeans.fit_predict(X)
      cu_score = adjusted_rand_score(cu_y_pred, y)
      kmeans = cluster.KMeans(
          random_state=random_state, n_clusters=params["n_clusters"]
      )
      sk_y_pred = kmeans.fit_predict(X)
      sk_score = adjusted_rand_score(sk_y_pred, y)
  
      assert sk_score - 1e-2 <= cu_score <= sk_score + 1e-2
  
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 8 for calling a global function with example usage: test_kmeans_sklearn_comparison_default(name, nrows, random_state) without return types