code: def test_pna():
      # Test for ogbg-mol datasets
      data_info = {"name": "ogbg-molhiv", "delta": 1, "out_size": 1}
      model = PNA(data_info, embed_size=10, num_layers=2)
      num_nodes = 5
      num_edges = 15
      g = dgl.rand_graph(num_nodes, num_edges)
      nfeat = torch.zeros(num_nodes, 9).long()
      model(g, nfeat)
  
      # Test for non-ogbg-mol datasets
      data_info = {
          "name": "a_dataset",
          "node_feat_size": 15,
          "delta": 1,
          "out_size": 1,
      }
      model = PNA(data_info, embed_size=10, num_layers=2)
      nfeat = torch.randn(num_nodes, data_info["node_feat_size"])
      model(g, nfeat)
  
file_path: dgl/tests/go/test_model.py
imports: import dgl
  import pytest
  import torch
  from utils.graph_cases import get_cases
  from dglgo.model import *
label_desc: Use image node_id 12 for calling a global function with example usage: test_pna() without return types