code: def test_avg_pool_neighbor_x():
      x = torch.tensor(
          [
              [1.0, 2.0],
              [3.0, 4.0],
              [5.0, 6.0],
              [7.0, 8.0],
              [9.0, 10.0],
              [11.0, 12.0],
          ]
      )
      edge_index = torch.tensor(
          [
              [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 5],
              [1, 2, 3, 0, 2, 3, 0, 1, 3, 0, 1, 2, 5, 4],
          ]
      )
      batch = torch.tensor([0, 0, 0, 0, 1, 1])
  
      data = Batch(x=x, edge_index=edge_index, batch=batch)
      data = avg_pool_neighbor_x(data)
  
      assert data.x.tolist() == [
          [4, 5],
          [4, 5],
          [4, 5],
          [4, 5],
          [10, 11],
          [10, 11],
      ]
      assert torch.equal(data.edge_index, edge_index)
  
imports: import torch
  from torch_geometric.data import Batch
  from torch_geometric.nn import avg_pool, avg_pool_neighbor_x, avg_pool_x
  from torch_geometric.testing import is_full_test
label_desc: Use image node_id 3 for calling a global function with example usage: test_avg_pool_neighbor_x() without return types