code: def assess_same_labelling(cut1, cut2):
      """Util for comparison with scipy"""
      co_clust = []
      for cut in [cut1, cut2]:
          n = len(cut)
          k = cut.max() + 1
          ecut = np.zeros((n, k))
          ecut[np.arange(n), cut] = 1
          co_clust.append(np.dot(ecut, ecut.T))
      assert (co_clust[0] == co_clust[1]).all()
  
imports: import itertools
  import shutil
  from functools import partial
  from tempfile import mkdtemp
  import numpy
  import pytest
  from scipy.cluster import hierarchy
  from scipy.sparse.csgraph import connected_components
  from sklearn.cluster import AgglomerativeClustering, FeatureAgglomeration, ward_tree
  from sklearn.cluster._agglomerative import _TREE_BUILDERS, _fix_connectivity, _hc_cut, linkage_tree
  from sklearn.cluster._hierarchical_fast import average_merge, max_merge, mst_linkage_core
  from sklearn.datasets import make_circles, make_moons
  from sklearn.feature_extraction.image import grid_to_graph
  from sklearn.metrics import DistanceMetric
  from sklearn.metrics.cluster import adjusted_rand_score, normalized_mutual_info_score
  from sklearn.metrics.pairwise import PAIRED_DISTANCES, cosine_distances, manhattan_distances, pairwise_distances
  from sklearn.metrics.tests.test_dist_metrics import METRICS_DEFAULT_PARAMS
  from sklearn.neighbors import kneighbors_graph
  from sklearn.utils._fast_dict import IntFloatDict
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal, create_memmap_backed_data, ignore_warnings
  from sklearn.utils.fixes import LIL_CONTAINERS
label_desc: Use image node_id 11 for calling a global function with example usage: assess_same_labelling(cut1, cut2) without return types