code: def test_fit_best_piecewise(global_random_seed):
      model = SpectralBiclustering(random_state=global_random_seed)
      vectors = np.array(
          [[0, 0, 0, 1, 1, 1], [2, 2, 2, 3, 3, 3], [0, 1, 2, 3, 4, 5]]
      )
      best = model._fit_best_piecewise(vectors, n_best=2, n_clusters=2)
      assert_array_equal(best, vectors[:2])
  
imports: import numpy
  import pytest
  from scipy.sparse import issparse
  from sklearn.base import BaseEstimator, BiclusterMixin
  from sklearn.cluster import SpectralBiclustering, SpectralCoclustering
  from sklearn.cluster._bicluster import _bistochastic_normalize, _log_normalize, _scale_normalize
  from sklearn.datasets import make_biclusters, make_checkerboard
  from sklearn.metrics import consensus_score, v_measure_score
  from sklearn.model_selection import ParameterGrid
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 10 for calling a global function with example usage: test_fit_best_piecewise(global_random_seed) without return types