code: def test_umap_transform_on_iris_w_pynndescent(iris, iris_selection):
      data = iris.data[iris_selection]
      fitter = UMAP(
          n_neighbors=10,
          min_dist=0.01,
          n_epochs=100,
          random_state=42,
          force_approximation_algorithm=True,
      ).fit(data)
  
      new_data = iris.data[~iris_selection]
      embedding = fitter.transform(new_data)
  
      trust = trustworthiness(new_data, embedding, n_neighbors=10)
      assert trust >= 0.85, (
          "Insufficiently trustworthy transform for"
          "iris dataset: {}".format(trust)
      )
  
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 5 for calling a global function with example usage: test_umap_transform_on_iris_w_pynndescent(iris, iris_selection) without return types