code: def test_hdbscan_all_points_membership_vectors():
      clusterer = HDBSCAN(
          prediction_data=True, min_cluster_size=200
      ).fit(X)
      vects = all_points_membership_vectors(clusterer)
      assert_array_equal(
          vects, np.zeros(clusterer.prediction_data_.raw_data.shape[0])
      )
  
imports: import numpy
  from scipy.spatial import distance
  from scipy import sparse
  from scipy import stats
  from sklearn.utils.estimator_checks import check_estimator
  from sklearn.utils._testing import assert_array_equal, assert_array_almost_equal, assert_raises
  from hdbscan import HDBSCAN, hdbscan, validity_index, approximate_predict, approximate_predict_scores, membership_vector, all_points_membership_vectors
  from sklearn.datasets import make_blobs
  from sklearn.utils import shuffle
  from sklearn.preprocessing import StandardScaler
  from scipy.stats import mode
  from tempfile import mkdtemp
  from functools import wraps
  import pytest
  from sklearn import datasets
  import warnings
label_desc: Use image node_id 33 for calling a global function with example usage: test_hdbscan_all_points_membership_vectors() without return types