code: def test_umap_clusterability_on_supervised_iris(
      supervised_iris_model, iris
  ):
      embedding = supervised_iris_model.embedding_
      clusters = KMeans(3).fit_predict(embedding)
      assert adjusted_rand_score(clusters, iris.target) >= 0.95
  
imports: from umap import UMAP
  from umap.umap_ import nearest_neighbors
  from scipy import sparse
  import numpy
  from sklearn.cluster import KMeans
  from sklearn.metrics import adjusted_rand_score
  from sklearn.neighbors import KDTree
  from scipy.spatial.distance import cdist, pdist, squareform
  import pytest
  import warnings
label_desc: Use image node_id 10 for calling a global function with example usage: test_umap_clusterability_on_supervised_iris(supervised_iris_model, iris) without return types