code: def test_umap_graph_layout():
      data, labels = make_blobs(n_samples=500, n_features=10, centers=5)
      model = UMAP(n_epochs=100, transform_mode="graph")
      graph = model.fit_transform(data)
      assert scipy.sparse.issparse(graph)
      nc, cl = scipy.sparse.csgraph.connected_components(graph)
      assert nc == 5
  
      new_graph = model.transform(
          data[:10] + np.random.normal(0.0, 0.1, size=(10, 10))
      )
      assert scipy.sparse.issparse(graph)
      assert new_graph.shape[0] == 10
  
imports: from sklearn.datasets import make_blobs
  from sklearn.cluster import KMeans
  from sklearn.metrics import adjusted_rand_score, pairwise_distances
  from sklearn.preprocessing import normalize
  from numpy.testing import assert_array_equal
  from umap import UMAP
  from umap.spectral import component_layout
  import numpy
  import scipy.sparse
  import pytest
  import warnings
  from umap.distances import pairwise_special_metric
  from umap.utils import disconnected_vertices
  from scipy.sparse import csr_matrix
label_desc: Use image node_id 10 for calling a global function with example usage: test_umap_graph_layout() without return types