code: def test_eigenvector_permutation_invariance():
      edge_index = torch.tensor(
          [
              [0, 1, 0, 4, 1, 4, 2, 3, 3, 5],
              [1, 0, 4, 0, 4, 1, 3, 2, 5, 3],
          ]
      )
      data = Data(edge_index=edge_index, num_nodes=6)
  
      perm = torch.randperm(data.num_nodes)
      transform = AddLaplacianEigenvectorPE(
          k=2,
          is_undirected=True,
          attr_name="x",
      )
      out1 = transform(data)
  
      transform = AddLaplacianEigenvectorPE(
          k=2,
          is_undirected=True,
          attr_name="x",
      )
      out2 = transform(data.subgraph(perm))
  
      assert torch.allclose(out1.x[perm].abs(), out2.x.abs(), atol=1e-6)
  
imports: import torch
  from torch_geometric.data import Data
  from torch_geometric.transforms import AddLaplacianEigenvectorPE, AddRandomWalkPE
label_desc: Use image node_id 2 for calling a global function with example usage: test_eigenvector_permutation_invariance() without return types