code: def test_quantile_aggregation(
      q, interpolation, dim, dim_size, fill_value
  ):
      x = torch.tensor(
          [
              [0.0, 1.0, 2.0],
              [3.0, 4.0, 5.0],
              [6.0, 7.0, 8.0],
              [9.0, 0.0, 1.0],
              [2.0, 3.0, 4.0],
              [5.0, 6.0, 7.0],
              [8.0, 9.0, 0.0],
              [1.0, 2.0, 3.0],
              [4.0, 5.0, 6.0],
              [7.0, 8.0, 9.0],
          ]
      )
      index = torch.zeros(x.size(dim), dtype=torch.long)
  
      aggr = QuantileAggregation(
          q=q, interpolation=interpolation, fill_value=fill_value
      )
      assert str(aggr) == f"QuantileAggregation(q={q})"
  
      out = aggr(x, index, dim=dim, dim_size=dim_size)
      expected = x.quantile(
          q, dim, interpolation=interpolation, keepdim=True
      )
  
      assert torch.allclose(out.narrow(dim, 0, 1), expected)
  
      if out.size(0) > index.max() + 1:
          padding = out.narrow(dim, 1, out.size(dim) - 1)
          assert torch.allclose(padding, torch.tensor(fill_value))
  
imports: import pytest
  import torch
  from torch_geometric.nn import MedianAggregation, QuantileAggregation
label_desc: Use image node_id 1 for calling a global function with example usage: test_quantile_aggregation(q, interpolation, dim, dim_size, fill_value) without return types