code: def test_my_multiple_aggr_conv(multi_aggr_tuple):
      # The 'cat' combine mode will expand the output dimensions by
      # the number of aggregators which is 3 here, while the 'proj'
      # mode keeps output dimensions unchanged.
      aggr_kwargs, expand = multi_aggr_tuple
      x = torch.randn(4, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
  
      conv = MyMultipleAggrConv(aggr_kwargs=aggr_kwargs)
      out = conv(x, edge_index)
      assert out.size() == (4, 16 * expand)
      assert torch.allclose(conv(x, adj1.t()), out)
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          assert torch.allclose(conv(x, adj2.t()), out)
  
imports: import copy
  from typing import Tuple, Union
  import pytest
  import torch
  from torch import Tensor
  from torch.nn import Linear
  import torch_geometric.typing
  from torch_geometric.nn import MessagePassing, aggr
  from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size, SparseTensor
  from torch_geometric.utils import add_self_loops, scatter, spmm, to_torch_csc_tensor
label_desc: Use image node_id 6 for calling a global function with example usage: test_my_multiple_aggr_conv(multi_aggr_tuple) without return types