code: def test_k_means_fortran_aligned_data(setup):
      # Check the KMeans will work well, even if X is a fortran-aligned data.
      X = np.asfortranarray([[0, 0], [0, 1], [0, 1]])
      centers = np.array([[0, 0], [0, 1]])
      labels = np.array([0, 1, 1])
      km = KMeans(
          n_init=1,
          init=centers,
          random_state=42,
          n_clusters=2,
          algorithm="elkan",
      )
      km.fit(X)
      np.testing.assert_array_almost_equal(km.cluster_centers_, centers)
      np.testing.assert_array_equal(km.labels_, labels)
  
imports: import sys
  from io import StringIO
  import numpy
  import pytest
  import scipy.sparse
  from ....None import tensor
  from ....core import TileableGraphBuilder, TileableGraph, ChunkGraphBuilder
  from ....config import options
  from ..None import KMeans, k_means
  from .._kmeans import _init_centroids
label_desc: Use image node_id 11 for calling a global function with example usage: test_k_means_fortran_aligned_data(setup) without return types