code: def test_k_means_new_centers(setup):
      # Explore the part of the code where a new center is reassigned
      X = np.array(
          [
              [0, 0, 1, 1],
              [0, 0, 0, 0],
              [0, 1, 0, 0],
              [0, 0, 0, 0],
              [0, 0, 0, 0],
              [0, 1, 0, 0],
          ]
      )
      labels = [0, 1, 2, 1, 1, 2]
      bad_centers = np.array(
          [[+0, 1, 0, 0], [0.2, 0, 0.2, 0.2], [+0, 0, 0, 0]]
      )
  
      km = KMeans(
          n_clusters=3,
          init=bad_centers,
          n_init=1,
          max_iter=10,
          random_state=1,
          algorithm="elkan",
      )
      for this_X in (X, sp.coo_matrix(X)):
          km.fit(this_X)
          this_labels = km.labels_.fetch()
          # Reorder the labels so that the first instance is in cluster 0,
          # the second in cluster 1, ...
          this_labels = np.unique(this_labels, return_index=True)[1][
              this_labels
          ]
          np.testing.assert_array_equal(this_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 6 for calling a global function with example usage: test_k_means_new_centers(setup) without return types