code: def test_vector_scikit_single_vs_scipy_single(global_random_seed):
      n_samples, n_features, n_clusters = 10, 5, 3
      rng = np.random.RandomState(global_random_seed)
      X = 0.1 * rng.normal(size=(n_samples, n_features))
      X -= 4.0 * np.arange(n_samples)[:, np.newaxis]
      X -= X.mean(axis=1)[:, np.newaxis]
  
      out = hierarchy.linkage(X, method="single")
      children_scipy = out[:, :2].astype(int)
  
      children, _, n_leaves, _ = _TREE_BUILDERS["single"](X)
  
      # Sort the order of child nodes per row for consistency
      children.sort(axis=1)
      assert_array_equal(
          children,
          children_scipy,
          "linkage tree differs from scipy impl for single linkage.",
      )
  
      cut = _hc_cut(n_clusters, children, n_leaves)
      cut_scipy = _hc_cut(n_clusters, children_scipy, n_leaves)
      assess_same_labelling(cut, cut_scipy)
  
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 13 for calling a global function with example usage: test_vector_scikit_single_vs_scipy_single(global_random_seed) without return types