code: def test_deepgcn(block_tuple, ckpt_grad):
      block, expansion = block_tuple
      x = torch.randn(3, 8)
      edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])
      conv = GENConv(8, 8)
      norm = LayerNorm(8)
      act = ReLU()
      layer = DeepGCNLayer(
          conv, norm, act, block=block, ckpt_grad=ckpt_grad
      )
      assert str(layer) == f"DeepGCNLayer(block={block})"
  
      out = layer(x, edge_index)
      assert out.size() == (3, 8 * expansion)
  
imports: import pytest
  import torch
  from torch.nn import ReLU
  from torch_geometric.nn import DeepGCNLayer, GENConv, LayerNorm
label_desc: Use image node_id 1 for calling a global function with example usage: test_deepgcn(block_tuple, ckpt_grad) without return types