code: def test_score(setup):
      centers = np.array(
          [
              [0.0, 5.0, 0.0, 0.0, 0.0],
              [1.0, 1.0, 4.0, 0.0, 0.0],
              [1.0, 0.0, 0.0, 5.0, 1.0],
          ]
      )
      n_samples = 100
      n_clusters, n_features = centers.shape
      X = make_blobs(
          n_samples=n_samples,
          centers=centers,
          cluster_std=1.0,
          random_state=42,
      )[0]
  
      for algo in ["full", "elkan"]:
          # Check that fitting k-means with multiple inits gives better score
          km1 = KMeans(
              n_clusters=n_clusters,
              max_iter=1,
              random_state=42,
              n_init=1,
              algorithm=algo,
              init="k-means++",
          )
          s1 = km1.fit(X).score(X).fetch()
          km2 = KMeans(
              n_clusters=n_clusters,
              max_iter=10,
              random_state=42,
              n_init=1,
              algorithm=algo,
              init="k-means++",
          )
          s2 = km2.fit(X).score(X).fetch()
          assert s2 > s1
  
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 15 for calling a global function with example usage: test_score(setup) without return types