code: def test_add_random_walk_pe():
      x = torch.randn(6, 4)
      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(x=x, edge_index=edge_index)
  
      transform = AddRandomWalkPE(walk_length=3)
      assert str(transform) == "AddRandomWalkPE()"
      out = transform(data)
      assert out.random_walk_pe.size() == (6, 3)
  
      transform = AddRandomWalkPE(walk_length=3, attr_name=None)
      out = transform(data)
      assert out.x.size() == (6, 4 + 3)
  
      transform = AddRandomWalkPE(walk_length=3, attr_name="x")
      out = transform(data)
      assert out.x.size() == (6, 3)
  
      # Output tests:
      assert out.x.tolist() == [
          [0.0, 0.5, 0.25],
          [0.0, 0.5, 0.25],
          [0.0, 0.5, 0.00],
          [0.0, 1.0, 0.00],
          [0.0, 0.5, 0.25],
          [0.0, 0.5, 0.00],
      ]
  
      edge_index = torch.tensor([[0, 1, 2], [0, 1, 2]])
      data = Data(edge_index=edge_index, num_nodes=4)
      out = transform(data)
  
      assert out.x.tolist() == [
          [1.0, 1.0, 1.0],
          [1.0, 1.0, 1.0],
          [1.0, 1.0, 1.0],
          [0.0, 0.0, 0.0],
      ]
  
imports: import torch
  from torch_geometric.data import Data
  from torch_geometric.transforms import AddLaplacianEigenvectorPE, AddRandomWalkPE
label_desc: Use image node_id 3 for calling a global function with example usage: test_add_random_walk_pe() without return types