code: def test_min_span_tree_plot():
      clusterer = HDBSCAN(gen_min_span_tree=True).fit(X)
      if_matplotlib(clusterer.minimum_spanning_tree_.plot)(
          edge_cmap="Reds"
      )
  
      H, y = make_blobs(n_samples=50, random_state=0, n_features=10)
      H = StandardScaler().fit_transform(H)
  
      clusterer = HDBSCAN(gen_min_span_tree=True).fit(H)
      if_matplotlib(clusterer.minimum_spanning_tree_.plot)(
          edge_cmap="Reds", vary_line_width=False, colorbar=False
      )
  
      H, y = make_blobs(n_samples=50, random_state=0, n_features=40)
      H = StandardScaler().fit_transform(H)
  
      clusterer = HDBSCAN(gen_min_span_tree=True).fit(H)
      if_matplotlib(clusterer.minimum_spanning_tree_.plot)(
          edge_cmap="Reds", vary_line_width=False, colorbar=False
      )
  
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 26 for calling a global function with example usage: test_min_span_tree_plot() without return types