code: def test_umap_data_formats(
      input_type, should_downcast, nrows, n_feats, name, target_metric
  ):
      dtype = np.float32 if not should_downcast else np.float64
      n_samples = nrows
      n_feats = n_feats
  
      if name == "digits":
          # use the digits dataset for unit test
          digits = datasets.load_digits(n_class=9)
          X = digits["data"].astype(dtype)
  
      else:
          X, y = datasets.make_blobs(
              n_samples=n_samples, n_features=n_feats, random_state=0
          )
  
      umap = cuUMAP(
          n_neighbors=3, n_components=2, target_metric=target_metric
      )
  
      embeds = umap.fit_transform(X)
      assert type(embeds) == np.ndarray
  
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 10 for calling a global function with example usage: test_umap_data_formats(input_type, should_downcast, nrows, n_feats, name, target_metric) without return types