code: def test_largest_connected_components():
      assert (
          str(LargestConnectedComponents())
          == "LargestConnectedComponents(1)"
      )
  
      edge_index = torch.tensor(
          [
              [0, 0, 1, 1, 2, 2, 2, 3, 3, 4, 5, 6, 8, 9],
              [1, 2, 0, 2, 0, 1, 3, 2, 4, 3, 6, 7, 9, 8],
          ]
      )
      data = Data(edge_index=edge_index, num_nodes=10)
  
      # Testing without `connection` specified:
      transform = LargestConnectedComponents(num_components=2)
      out = transform(data)
      assert out.num_nodes == 8
      assert out.edge_index.tolist() == data.edge_index[:, :12].tolist()
  
      # Testing with `connection = strong`:
      transform = LargestConnectedComponents(
          num_components=2, connection="strong"
      )
      out = transform(data)
      assert out.num_nodes == 7
      assert out.edge_index.tolist() == [
          [0, 0, 1, 1, 2, 2, 2, 3, 3, 4, 5, 6],
          [1, 2, 0, 2, 0, 1, 3, 2, 4, 3, 6, 5],
      ]
  
      edge_index = torch.tensor(
          [
              [0, 1, 2, 3, 3, 4],
              [1, 0, 3, 2, 4, 3],
          ]
      )
      data = Data(edge_index=edge_index, num_nodes=5)
  
      # Testing without `num_components` and `connection` specified:
      transform = LargestConnectedComponents()
      out = transform(data)
      assert out.num_nodes == 3
      assert out.edge_index.tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
  
      # Testing with larger `num_components` than actual number of components:
      transform = LargestConnectedComponents(num_components=3)
      out = transform(data)
      assert out.num_nodes == 5
      assert out.edge_index.tolist() == data.edge_index.tolist()
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_largest_connected_components() without return types