code: def test_lazy_gmm_conv(separate_gaussians):
      x1 = torch.randn(4, 8)
      x2 = torch.randn(2, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      value = torch.rand(edge_index.size(1), 3)
  
      conv = GMMConv(
          -1,
          32,
          dim=3,
          kernel_size=25,
          separate_gaussians=separate_gaussians,
      )
      assert str(conv) == "GMMConv(-1, 32, dim=3)"
      out = conv(x1, edge_index, value)
      assert out.size() == (4, 32)
  
      conv = GMMConv(
          (-1, -1),
          32,
          dim=3,
          kernel_size=25,
          separate_gaussians=separate_gaussians,
      )
      assert str(conv) == "GMMConv((-1, -1), 32, dim=3)"
      out = conv((x1, x2), edge_index, value)
      assert out.size() == (2, 32)
  
file_path: pytorch_geometric/test/nn/conv/test_gmm_conv.py
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import GMMConv
  from torch_geometric.testing import is_full_test
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import to_torch_coo_tensor
label_desc: Use image node_id 2 for calling a global function with example usage: test_lazy_gmm_conv(separate_gaussians) without return types