old_contents: from umap import UMAP
  import pytest
  
  try:
      # works for sklearn>=0.22
      from sklearn.manifold import trustworthiness
  except ImportError:
      # this is to comply with requirements (scikit-learn>=0.20)
      # More recent versions of sklearn have exposed trustworthiness
      # in top level module API
      # see: https://github.com/scikit-learn/scikit-learn/pull/15337
      from sklearn.manifold.t_sne import trustworthiness
  
  
  def test_densmap_trustworthiness(nn_data):
      data = nn_data[:50]
      embedding, rad_h, rad_l = UMAP(
          n_neighbors=10,
          min_dist=0.01,
          random_state=42,
          n_epochs=100,
          densmap=True,
          output_dens=True,
      ).fit_transform(data)
      trust = trustworthiness(data, embedding, 10)
      assert (
          trust >= 0.75
      ), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust)
  
  
  @pytest.mark.skip()
  def test_densmap_trustworthiness_random_init(nn_data):  # pragma: no cover
      data = nn_data[:50]
      embedding = UMAP(
          n_neighbors=10, min_dist=0.01, random_state=42, init="random", densmap=True,
      ).fit_transform(data)
      trust = trustworthiness(data, embedding, 10)
      assert (
          trust >= 0.75
      ), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust)
  
  
  def test_densmap_trustworthiness_on_iris(iris):
      densmap_iris_model = UMAP(
          n_neighbors=10, min_dist=0.01, random_state=42, densmap=True, verbose=True,
      ).fit(iris.data)
      embedding = densmap_iris_model.embedding_
      trust = trustworthiness(iris.data, embedding, 10)
      assert (
          trust >= 0.97
      ), "Insufficiently trustworthy embedding for" "iris dataset: {}".format(trust)
  
      with pytest.raises(NotImplementedError):
          densmap_iris_model.transform(iris.data[:10])
  
      with pytest.raises(ValueError):
          densmap_iris_model.inverse_transform(embedding[:10])
  
      with pytest.raises(NotImplementedError):
          _ = UMAP(
              n_neighbors=10, min_dist=0.01, random_state=42, densmap=True, verbose=True,
          ).fit(iris.data, y=iris.target)
  
new_contents: from umap import UMAP
  import pytest
  
  try:
      # works for sklearn>=0.22
      from sklearn.manifold import trustworthiness
  except ImportError:
      # this is to comply with requirements (scikit-learn>=0.20)
      # More recent versions of sklearn have exposed trustworthiness
      # in top level module API
      # see: https://github.com/scikit-learn/scikit-learn/pull/15337
      from sklearn.manifold.t_sne import trustworthiness
  
  
  def test_densmap_trustworthiness(nn_data):
      data = nn_data[:50]
      embedding, rad_h, rad_l = UMAP(
          n_neighbors=10,
          min_dist=0.01,
          random_state=42,
          n_epochs=100,
          densmap=True,
          output_dens=True,
      ).fit_transform(data)
      trust = trustworthiness(data, embedding, 10)
      assert (
          trust >= 0.75
      ), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust)
  
  
  @pytest.mark.skip()
  def test_densmap_trustworthiness_random_init(nn_data):  # pragma: no cover
      data = nn_data[:50]
      embedding = UMAP(
          n_neighbors=10, min_dist=0.01, random_state=42, init="random", densmap=True,
      ).fit_transform(data)
      trust = trustworthiness(data, embedding, 10)
      assert (
          trust >= 0.75
      ), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust)
  
  
  def test_densmap_trustworthiness_on_iris(iris):
      densmap_iris_model = UMAP(
          n_neighbors=10, min_dist=0.01, random_state=42, densmap=True, verbose=True,
      ).fit(iris.data)
      embedding = densmap_iris_model.embedding_
      trust = trustworthiness(iris.data, embedding, 10)
      assert (
          trust >= 0.97
      ), "Insufficiently trustworthy embedding for" "iris dataset: {}".format(trust)
  
      with pytest.raises(NotImplementedError):
          densmap_iris_model.transform(iris.data[:10])
  
      with pytest.raises(ValueError):
          densmap_iris_model.inverse_transform(embedding[:10])
  
  
  def test_densmap_trustworthiness_on_iris_supervised(iris):
      densmap_iris_model = UMAP(
          n_neighbors=10, min_dist=0.01, random_state=42, densmap=True, verbose=True,
      ).fit(iris.data, y=iris.target)
      embedding = densmap_iris_model.embedding_
      trust = trustworthiness(iris.data, embedding, 10)
      assert (
          trust >= 0.97
      ), "Insufficiently trustworthy embedding for" "iris dataset: {}".format(trust)
  
subject: change test to match