code: def test_supergat_conv(att_type):
      x = torch.randn(4, 8)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
  
      conv = SuperGATConv(
          8,
          32,
          heads=2,
          attention_type=att_type,
          neg_sample_ratio=1.0,
          edge_sample_ratio=1.0,
      )
      assert (
          str(conv) == f"SuperGATConv(8, 32, heads=2, type={att_type})"
      )
  
      out = conv(x, edge_index)
      assert out.size() == (4, 64)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          assert torch.allclose(conv(x, adj.t()), out, atol=1e-6)
  
      # Negative samples are given:
      neg_edge_index = conv.negative_sampling(edge_index, x.size(0))
      assert torch.allclose(conv(x, edge_index, neg_edge_index), out)
      att_loss = conv.get_attention_loss()
      assert isinstance(att_loss, torch.Tensor) and att_loss > 0
  
      # Batch of graphs:
      x = torch.randn(8, 8)
      edge_index = torch.tensor(
          [[0, 1, 2, 3, 4, 5, 6, 7], [0, 0, 1, 1, 4, 4, 5, 5]]
      )
      batch = torch.tensor([0, 0, 0, 0, 1, 1, 1, 1])
      out = conv(x, edge_index, batch=batch)
      assert out.size() == (8, 64)
  
      # Batch of graphs and negative samples are given:
      neg_edge_index = conv.negative_sampling(
          edge_index, x.size(0), batch
      )
      assert torch.allclose(conv(x, edge_index, neg_edge_index), out)
      att_loss = conv.get_attention_loss()
      assert isinstance(att_loss, torch.Tensor) and att_loss > 0
  
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import SuperGATConv
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_supergat_conv(att_type) without return types