code: def test_dna_conv(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]]
      )
  
      conv = DNAConv(channels, heads=4, groups=8, dropout=0.0)
      assert str(conv) == "DNAConv(32, heads=4, groups=8)"
      out = conv(x, edge_index)
      assert out.size() == (4, channels)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x, edge_index), out, atol=1e-6)
  
      conv = DNAConv(channels, heads=1, groups=1, dropout=0.0)
      assert str(conv) == "DNAConv(32, heads=1, groups=1)"
      out = conv(x, edge_index)
      assert out.size() == (4, channels)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x, edge_index), out, atol=1e-6)
  
      conv = DNAConv(
          channels, heads=1, groups=1, dropout=0.0, cached=True
      )
      out = conv(x, edge_index)
      assert conv._cached_edge_index is not None
      out = conv(x, edge_index)
      assert out.size() == (4, channels)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x, edge_index), out, 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 1 for calling a global function with example usage: test_dna_conv(channels, num_layers) without return types