code: def test_dna_conv_sparse_tensor(channels, num_layers):
      x = torch.randn((4, num_layers, channels))
      edge_index = torch.tensor(
          [[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]]
      )
      value = torch.rand(edge_index.size(1))
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
      adj2 = to_torch_csc_tensor(edge_index, value, size=(4, 4))
  
      conv = DNAConv(32, heads=4, groups=8, dropout=0.0)
      assert str(conv) == "DNAConv(32, heads=4, groups=8)"
      out1 = conv(x, edge_index)
      assert out1.size() == (4, 32)
      assert torch.allclose(conv(x, adj1.t()), out1, atol=1e-6)
      out2 = conv(x, edge_index, value)
      assert out2.size() == (4, 32)
      assert torch.allclose(conv(x, adj2.t()), out2, atol=1e-6)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj3 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          adj4 = SparseTensor.from_edge_index(edge_index, value, (4, 4))
          assert torch.allclose(conv(x, adj3.t()), out1, atol=1e-6)
          assert torch.allclose(conv(x, adj4.t()), out2, atol=1e-6)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x, edge_index), out1, atol=1e-6)
          assert torch.allclose(
              jit(x, edge_index, value), out2, atol=1e-6
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit(x, adj3.t()), out1, atol=1e-6)
              assert torch.allclose(jit(x, adj4.t()), out2, atol=1e-6)
  
      conv = DNAConv(
          channels, heads=1, groups=1, dropout=0.0, cached=True
      )
  
      out1 = conv(x, adj1.t())
      assert conv._cached_edge_index is not None
      assert torch.allclose(conv(x, adj1.t()), out1, atol=1e-6)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          assert torch.allclose(conv(x, adj3.t()), out1, atol=1e-6)
          assert conv._cached_adj_t is not None
          assert torch.allclose(conv(x, adj3.t()), out1, atol=1e-6)
  
file_path: pytorch_geometric/test/nn/conv/test_dna_conv.py
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import DNAConv
  from torch_geometric.testing import is_full_test
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import to_torch_csc_tensor
label_desc: Use image node_id 2 for calling a global function with example usage: test_dna_conv_sparse_tensor(channels, num_layers) without return types