code: def test_rgcn_conv_equality(
      aggr, bias, bipartite, max_num_neighbors, num_bases, root_weight
  ):
      in_channels, out_channels, num_relations = (4, 2, 3)
      kwargs = dict(
          aggr=aggr,
          bias=bias,
          num_bases=num_bases,
          root_weight=root_weight,
      )
  
      size = (10, 8) if bipartite else (10, 10)
      x = torch.rand(size[0], in_channels, device="cuda")
      edge_index = torch.tensor(
          [
              [7, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 8, 9],
              [0, 1, 2, 3, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7],
          ],
          device="cuda",
      )
      edge_type = torch.tensor(
          [1, 2, 1, 0, 2, 1, 2, 0, 2, 2, 1, 1, 1, 2, 2]
      ).cuda()
  
      torch.manual_seed(12345)
      conv1 = RGCNConv(
          in_channels, out_channels, num_relations, **kwargs
      ).cuda()
      torch.manual_seed(12345)
      conv2 = CuGraphRGCNConv(
          in_channels, out_channels, num_relations, **kwargs
      ).cuda()
  
      if bipartite:
          out1 = conv1((x, x[: size[1]]), edge_index, edge_type)
      else:
          out1 = conv1(x, edge_index, edge_type)
  
      csc, edge_type = CuGraphRGCNConv.to_csc(
          edge_index, size, edge_type
      )
      out2 = conv2(
          x, csc, edge_type, max_num_neighbors=max_num_neighbors
      )
      assert torch.allclose(out1, out2, atol=1e-3)
  
      grad_out = torch.rand_like(out1)
      out1.backward(grad_out)
      out2.backward(grad_out)
  
      end = -1 if root_weight else None
      assert torch.allclose(
          conv1.weight.grad, conv2.weight.grad[:end], atol=1e-3
      )
  
      if root_weight:
          assert torch.allclose(
              conv1.root.grad, conv2.weight.grad[-1], atol=1e-3
          )
  
      if num_bases is not None:
          assert torch.allclose(
              conv1.comp.grad, conv2.comp.grad, atol=1e-3
          )
  
imports: import pytest
  import torch
  from torch_geometric.nn import CuGraphRGCNConv
  from torch_geometric.nn import FastRGCNConv
  from torch_geometric.testing import onlyCUDA, withPackage
label_desc: Use image node_id 1 for calling a global function with example usage: test_rgcn_conv_equality(aggr, bias, bipartite, max_num_neighbors, num_bases, root_weight) without return types