code: def test_k_means_fit_predict(setup, algo, seed, max_iter, tol):
      # check that fit.predict gives same result as fit_predict
      rng = np.random.RandomState(seed)
  
      X = make_blobs(
          n_samples=1000, n_features=10, centers=10, random_state=rng
      )[0]
  
      kmeans = KMeans(
          algorithm=algo,
          n_clusters=10,
          random_state=seed,
          tol=tol,
          max_iter=max_iter,
          init="k-means++",
      )
  
      labels_1 = kmeans.fit(X).predict(X)
      labels_2 = kmeans.fit_predict(X)
  
      # Due to randomness in the order in which chunks of data are processed when
      # using more than one thread, the absolute values of the labels can be
      # different between the 2 strategies but they should correspond to the same
      # clustering.
      assert pytest.approx(v_measure_score(labels_1, labels_2)) == 1
  
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 12 for calling a global function with example usage: test_k_means_fit_predict(setup, algo, seed, max_iter, tol) without return types