code: def test_degree_scaler_aggregation(train_norm):
      x = torch.randn(6, 16)
      index = torch.tensor([0, 0, 1, 1, 1, 2])
      ptr = torch.tensor([0, 2, 5, 6])
      deg = torch.tensor([0, 3, 0, 1, 1, 0])
  
      aggr = ["mean", "sum", "max"]
      scaler = [
          "identity",
          "amplification",
          "attenuation",
          "linear",
          "inverse_linear",
      ]
      aggr = DegreeScalerAggregation(
          aggr, scaler, deg, train_norm=train_norm
      )
      assert str(aggr) == "DegreeScalerAggregation()"
  
      out = aggr(x, index)
      assert out.size() == (3, 240)
      assert torch.allclose(torch.jit.script(aggr)(x, index), out)
  
      with pytest.raises(NotImplementedError, match="requires 'index'"):
          aggr(x, ptr=ptr)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_degree_scaler_aggregation(train_norm) without return types