code: def test_neural_fingerprint(batch):
      x = torch.randn(3, 7)
      edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])
  
      model = NeuralFingerprint(7, 16, out_channels=5, num_layers=4)
      assert str(model) == "NeuralFingerprint(7, 5, num_layers=4)"
      model.reset_parameters()
  
      out = model(x, edge_index, batch)
      assert out.size() == (1, 5) if batch is None else (2, 5)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(3, 3)
          )
          assert torch.allclose(model(x, adj.t(), batch), out)
  
      if is_full_test():
          jit = torch.jit.export(model)
          assert torch.allclose(jit(x, edge_index, batch), out)
  
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import NeuralFingerprint
  from torch_geometric.testing import is_full_test
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_neural_fingerprint(batch) without return types