code: def test_log_normalize(global_random_seed):
      # adding any constant to a log-scaled matrix should make it
      # bistochastic
      generator = np.random.RandomState(global_random_seed)
      mat = generator.rand(100, 100)
      scaled = _log_normalize(mat) + 1
      _do_bistochastic_test(scaled)
  
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 9 for calling a global function with example usage: test_log_normalize(global_random_seed) without return types