code: def test_gin(out_dim, dropout, act, norm, jk):
      x = torch.randn(3, 8)
      edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])
      out_channels = 16 if out_dim is None else out_dim
  
      model = GIN(
          8,
          16,
          num_layers=3,
          out_channels=out_dim,
          dropout=dropout,
          act=act,
          norm=norm,
          jk=jk,
      )
      assert str(model) == f"GIN(8, {out_channels}, num_layers=3)"
      assert model(x, edge_index).size() == (3, out_channels)
  
imports: import os
  import os.path
  import random
  import sys
  import warnings
  import pytest
  import torch
  import torch.nn.functional
  import torch_geometric.typing
  from torch_geometric.data import Data
  from torch_geometric.loader import NeighborLoader
  from torch_geometric.nn import SAGEConv
  from torch_geometric.nn.models import GAT, GCN, GIN, PNA, EdgeCNN, GraphSAGE
  from torch_geometric.profile import benchmark
  from torch_geometric.testing import onlyFullTest, onlyLinux, onlyNeighborSampler, onlyOnline, withCUDA, withPackage
label_desc: Use image node_id 3 for calling a global function with example usage: test_gin(out_dim, dropout, act, norm, jk) without return types