code: def test_umap_fit_transform_trust(name, target_metric):
      if name == "iris":
          iris = datasets.load_iris()
          data = iris.data
          labels = iris.target
  
      elif name == "digits":
          digits = datasets.load_digits(n_class=5)
          data = digits.data
          labels = digits.target
  
      elif name == "wine":
          wine = datasets.load_wine()
          data = wine.data
          labels = wine.target
      else:
          data, labels = make_blobs(
              n_samples=500, n_features=10, centers=10, random_state=42
          )
  
      model = umap.UMAP(
          n_neighbors=10, min_dist=0.01, target_metric=target_metric
      )
      cuml_model = cuUMAP(
          n_neighbors=10, min_dist=0.01, target_metric=target_metric
      )
      embedding = model.fit_transform(data)
      cuml_embedding = cuml_model.fit_transform(
          data, convert_dtype=True
      )
  
      trust = trustworthiness(data, embedding, n_neighbors=10)
      cuml_trust = trustworthiness(data, cuml_embedding, n_neighbors=10)
  
      assert array_equal(trust, cuml_trust, 1e-1, with_sign=True)
  
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 9 for calling a global function with example usage: test_umap_fit_transform_trust(name, target_metric) without return types