code: def test_quadtree_boundary_computation():
      # Introduce a point into a quad tree with boundaries not easy to compute.
      Xs = []
  
      # check a random case
      Xs.append(np.array([[-1, 1], [-4, -1]], dtype=np.float32))
      # check the case where only 0 are inserted
      Xs.append(np.array([[0, 0], [0, 0]], dtype=np.float32))
      # check the case where only negative are inserted
      Xs.append(np.array([[-1, -2], [-4, 0]], dtype=np.float32))
      # check the case where only small numbers are inserted
      Xs.append(
          np.array([[-1e-6, 1e-6], [-4e-6, -1e-6]], dtype=np.float32)
      )
  
      for X in Xs:
          tree = _QuadTree(n_dimensions=2, verbose=0)
          tree.build_tree(X)
          tree._check_coherence()
  
file_path: scikit-learn/sklearn/neighbors/tests/test_quad_tree.py
imports: import pickle
  import numpy
  import pytest
  from sklearn.neighbors._quad_tree import _QuadTree
  from sklearn.utils import check_random_state
label_desc: Use image node_id 1 for calling a global function with example usage: test_quadtree_boundary_computation() without return types