code: def main(args):
      dataset = load(args.dataset)
      graph = dataset.graph
      x = graph.node_feat["words"]
      edges = graph.edges
  
      pgl_gnn_model = PGLGCN(
          input_size=x.shape[1],
          num_class=dataset.num_classes,
          hidden_size=16,
      )
  
      pyg_gnn_model = PyGGCN(
          input_size=x.shape[1],
          num_class=dataset.num_classes,
          hidden_size=16,
      )
  
      # Define Model in PGL
      convert_pyg2pgl(pgl_gnn_model, pyg_gnn_model)
  
      pgl_out = pgl_gnn_model(
          paddle.to_tensor(x), paddle.to_tensor(edges)
      )
      pyg_out = pyg_gnn_model(
          torch.FloatTensor(x), torch.LongTensor(edges)
      )
  
      eps = np.mean((pgl_out.numpy() - pyg_out.detach().numpy()) ** 2)
      print(
          "load PGL from PyG has same output get average epsilon", eps
      )
  
file_path: PGL/examples/AITISA/load_model_from_pyg/gcn.py
imports: import pgl
  import paddle
  import torch
  import torch.nn.functional
  from torch_geometric.nn import GCNConv
  import argparse
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: main(args) without return types