code: def test_ward_tree_children_order(global_random_seed):
      # Check that children are ordered in the same way for both structured and
      # unstructured versions of ward_tree.
  
      # test on five random datasets
      n, p = 10, 5
      rng = np.random.RandomState(global_random_seed)
  
      connectivity = np.ones((n, n))
      for i in range(5):
          X = 0.1 * rng.normal(size=(n, p))
          X -= 4.0 * np.arange(n)[:, np.newaxis]
          X -= X.mean(axis=1)[:, np.newaxis]
  
          out_unstructured = ward_tree(X)
          out_structured = ward_tree(X, connectivity=connectivity)
  
          assert_array_equal(out_unstructured[0], out_structured[0])
  
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 17 for calling a global function with example usage: test_ward_tree_children_order(global_random_seed) without return types