code: def test_hdbscan_badargs():
      assert_raises(ValueError, hdbscan, X="fail")
      assert_raises(ValueError, hdbscan, X=None)
      assert_raises(ValueError, hdbscan, X, min_cluster_size="fail")
      assert_raises(ValueError, hdbscan, X, min_samples="fail")
      assert_raises(ValueError, hdbscan, X, min_samples=-1)
      assert_raises(ValueError, hdbscan, X, metric="imperial")
      assert_raises(ValueError, hdbscan, X, metric=None)
      assert_raises(ValueError, hdbscan, X, metric="minkowski", p=-1)
      assert_raises(
          ValueError,
          hdbscan,
          X,
          metric="minkowski",
          p=-1,
          algorithm="prims_kdtree",
      )
      assert_raises(
          ValueError,
          hdbscan,
          X,
          metric="minkowski",
          p=-1,
          algorithm="prims_balltree",
      )
      assert_raises(
          ValueError,
          hdbscan,
          X,
          metric="minkowski",
          p=-1,
          algorithm="boruvka_balltree",
      )
      assert_raises(
          ValueError,
          hdbscan,
          X,
          metric="precomputed",
          algorithm="boruvka_kdtree",
      )
      assert_raises(
          ValueError,
          hdbscan,
          X,
          metric="precomputed",
          algorithm="prims_kdtree",
      )
      assert_raises(
          ValueError,
          hdbscan,
          X,
          metric="precomputed",
          algorithm="prims_balltree",
      )
      assert_raises(
          ValueError,
          hdbscan,
          X,
          metric="precomputed",
          algorithm="boruvka_balltree",
      )
      assert_raises(ValueError, hdbscan, X, alpha=-1)
      assert_raises(ValueError, hdbscan, X, alpha="fail")
      assert_raises(Exception, hdbscan, X, algorithm="something_else")
      assert_raises(TypeError, hdbscan, X, metric="minkowski", p=None)
      assert_raises(ValueError, hdbscan, X, leaf_size=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 34 for calling a global function with example usage: test_hdbscan_badargs() without return types