code: def test_mlp_sage_conv():
      x = torch.randn(4, 8)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
  
      conv = SAGEConv(
          in_channels=8,
          out_channels=32,
          aggr=MLPAggregation(
              in_channels=8,
              out_channels=8,
              max_num_elements=2,
              num_layers=1,
          ),
      )
  
      out = conv(x, edge_index)
      assert out.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 4 for calling a global function with example usage: test_mlp_sage_conv() without return types