code: def test_umap_transform_on_digits(target_metric):
      digits = datasets.load_digits()
  
      digits_selection = np.random.RandomState(42).choice(
          [True, False], 1797, replace=True, p=[0.75, 0.25]
      )
      data = digits.data[digits_selection]
  
      fitter = cuUMAP(
          n_neighbors=15,
          verbose=logger.level_debug,
          init="random",
          n_epochs=0,
          min_dist=0.01,
          random_state=42,
          target_metric=target_metric,
      )
      fitter.fit(data, convert_dtype=True)
  
      new_data = digits.data[~digits_selection]
  
      embedding = fitter.transform(new_data, convert_dtype=True)
      trust = trustworthiness(
          digits.data[~digits_selection], embedding, n_neighbors=15
      )
      assert trust >= 0.96
  
imports: import platform
  import pytest
  import copy
  import joblib
  from sklearn.metrics import adjusted_rand_score
  from sklearn.manifold import trustworthiness
  from sklearn.datasets import make_blobs
  from sklearn.cluster import KMeans
  from sklearn.neighbors import NearestNeighbors
  from sklearn import datasets
  from cuml.internals import logger
  from cuml.metrics import pairwise_distances
  from cuml.testing.utils import array_equal, unit_param, quality_param, stress_param
  from cuml.manifold.umap import UMAP
  from cuml.internals.safe_imports import cpu_only_import
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 8 for calling a global function with example usage: test_umap_transform_on_digits(target_metric) without return types