code: def test_dbscan_default(name):
      default_base = {
          "quantile": 0.3,
          "eps": 0.5,
          "damping": 0.9,
          "preference": -200,
          "n_neighbors": 10,
          "n_clusters": 2,
      }
      n_samples = 500
      pat = get_pattern(name, n_samples)
      params = default_base.copy()
      params.update(pat[1])
      X, y = pat[0]
  
      X = StandardScaler().fit_transform(X)
  
      cuml_dbscan = cuDBSCAN(output_type="numpy")
      cu_labels = cuml_dbscan.fit_predict(X)
  
      sk_dbscan = skDBSCAN(eps=params["eps"], min_samples=5)
      sk_labels = sk_dbscan.fit_predict(X)
  
      # Check the core points are equal
      assert array_equal(
          cuml_dbscan.core_sample_indices_,
          sk_dbscan.core_sample_indices_,
      )
  
      # Check the labels are correct
      assert_dbscan_equal(
          sk_labels,
          cu_labels,
          X,
          cuml_dbscan.core_sample_indices_,
          params["eps"],
      )
  
imports: from sklearn.preprocessing import StandardScaler
  from sklearn.metrics import pairwise_distances
  from sklearn.datasets import make_blobs
  from sklearn.cluster import DBSCAN
  from cuml.testing.utils import get_pattern, unit_param, quality_param, stress_param, array_equal, assert_dbscan_equal
  from cuml import DBSCAN
  from cuml.testing.utils import get_handle
  import pytest
  from cuml.internals.safe_imports import cpu_only_import_from
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 5 for calling a global function with example usage: test_dbscan_default(name) without return types