code: def test_filter_edges():
      edge_index = torch.tensor(
          [[0, 1, 1, 2, 2, 3], [1, 0, 1, 3, 2, 2]]
      )
      edge_attr = torch.tensor([1, 2, 3, 4, 5, 6])
      batch = torch.tensor([0, 0, 1, 1])
  
      select_output = SelectOutput(
          node_index=torch.tensor([1, 2]),
          num_nodes=4,
          cluster_index=torch.tensor([0, 1]),
          num_clusters=2,
      )
  
      connect = FilterEdges()
      assert str(connect) == "FilterEdges()"
  
      out1 = connect(select_output, edge_index, edge_attr, batch)
      assert out1.edge_index.tolist() == [[0, 1], [0, 1]]
      assert out1.edge_attr.tolist() == [3, 5]
      assert out1.batch.tolist() == [0, 1]
  
      if torch_geometric.typing.WITH_PT113 and is_full_test():
          jit = torch.jit.script(connect)
          out2 = jit(select_output, edge_index, edge_attr, batch)
          torch.equal(out1.edge_index, out2.edge_index)
          torch.equal(out1.edge_attr, out2.edge_attr)
          torch.equal(out1.batch, out2.batch)
  
imports: import torch
  import torch_geometric.typing
  from torch_geometric.nn.pool.connect import FilterEdges
  from torch_geometric.nn.pool.select import SelectOutput
  from torch_geometric.testing import is_full_test
label_desc: Use image node_id 1 for calling a global function with example usage: test_filter_edges() without return types