code: def test_dbscan_balltree():
      # Tests the DBSCAN algorithm with balltree for neighbor calculation.
      eps = 0.8
      min_samples = 10
  
      D = pairwise_distances(X)
      core_samples, labels = dbscan(
          D, metric="precomputed", eps=eps, min_samples=min_samples
      )
  
      # number of clusters, ignoring noise if present
      n_clusters_1 = len(set(labels)) - int(-1 in labels)
      assert n_clusters_1 == n_clusters
  
      db = DBSCAN(
          p=2.0, eps=eps, min_samples=min_samples, algorithm="ball_tree"
      )
      labels = db.fit(X).labels_
  
      n_clusters_2 = len(set(labels)) - int(-1 in labels)
      assert n_clusters_2 == n_clusters
  
      db = DBSCAN(
          p=2.0, eps=eps, min_samples=min_samples, algorithm="kd_tree"
      )
      labels = db.fit(X).labels_
  
      n_clusters_3 = len(set(labels)) - int(-1 in labels)
      assert n_clusters_3 == n_clusters
  
      db = DBSCAN(
          p=1.0, eps=eps, min_samples=min_samples, algorithm="ball_tree"
      )
      labels = db.fit(X).labels_
  
      n_clusters_4 = len(set(labels)) - int(-1 in labels)
      assert n_clusters_4 == n_clusters
  
      db = DBSCAN(
          leaf_size=20,
          eps=eps,
          min_samples=min_samples,
          algorithm="ball_tree",
      )
      labels = db.fit(X).labels_
  
      n_clusters_5 = len(set(labels)) - int(-1 in labels)
      assert n_clusters_5 == n_clusters
  
imports: import pickle
  import warnings
  import numpy
  import pytest
  from scipy.spatial import distance
  from sklearn.cluster import DBSCAN, dbscan
  from sklearn.cluster.tests.common import generate_clustered_data
  from sklearn.metrics.pairwise import pairwise_distances
  from sklearn.neighbors import NearestNeighbors
  from sklearn.utils._testing import assert_array_equal
  from sklearn.utils.fixes import CSR_CONTAINERS, LIL_CONTAINERS
label_desc: Use image node_id 11 for calling a global function with example usage: test_dbscan_balltree() without return types