code: def test_multi_aggr_sage_conv(aggr_kwargs):
      x = torch.randn(4, 8)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
  
      aggr_kwargs["aggrs_kwargs"] = [{}, {}, {}, dict(learn=True, t=1)]
      conv = SAGEConv(
          8,
          32,
          aggr=["mean", "max", "sum", "softmax"],
          aggr_kwargs=aggr_kwargs,
      )
  
      assert_module(conv, x, edge_index, expected_size=(4, 32))
  
file_path: pytorch_geometric/test/nn/conv/test_sage_conv.py
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import MLPAggregation, SAGEConv
  from torch_geometric.testing import assert_module, is_full_test, onlyLinux, withCUDA, withPackage
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 5 for calling a global function with example usage: test_multi_aggr_sage_conv(aggr_kwargs) without return types