code: def test_dbscan_default(name, client):
      from cuml.dask.cluster.dbscan import DBSCAN as cuDBSCAN
  
      eps = 0.5
      default_base = {
          "quantile": 0.3,
          "eps": eps,
          "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_, eps
      )
  
file_path: cuml/python/cuml/tests/dask/test_dask_dbscan.py
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
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 4 for calling a global function with example usage: test_dbscan_default(name, client) without return types