code: def test_sage_conv_equality(
      aggr, bias, bipartite, max_num_neighbors, normalize, root_weight
  ):
      in_channels, out_channels = (8, 16)
      kwargs = dict(
          aggr=aggr,
          bias=bias,
          normalize=normalize,
          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",
      )
  
      conv1 = SAGEConv(in_channels, out_channels, **kwargs).cuda()
      conv2 = CuGraphSAGEConv(
          in_channels, out_channels, **kwargs
      ).cuda()
  
      with torch.no_grad():
          conv2.lin.weight.data[
              :, :in_channels
          ] = conv1.lin_l.weight.data
          if root_weight:
              conv2.lin.weight.data[
                  :, in_channels:
              ] = conv1.lin_r.weight.data
          if bias:
              conv2.lin.bias.data[:] = conv1.lin_l.bias.data
  
      if bipartite:
          out1 = conv1((x, x[: size[1]]), edge_index)
      else:
          out1 = conv1(x, edge_index)
  
      csc = CuGraphSAGEConv.to_csc(edge_index, size)
      out2 = conv2(x, csc, max_num_neighbors=max_num_neighbors)
      assert torch.allclose(out1, out2, atol=1e-6)
  
      grad_out = torch.rand_like(out1)
      out1.backward(grad_out)
      out2.backward(grad_out)
  
      assert torch.allclose(
          conv1.lin_l.weight.grad,
          conv2.lin.weight.grad[:, :in_channels],
          atol=1e-6,
      )
  
      if root_weight:
          assert torch.allclose(
              conv1.lin_r.weight.grad,
              conv2.lin.weight.grad[:, in_channels:],
              atol=1e-6,
          )
  
      if bias:
          assert torch.allclose(
              conv1.lin_l.bias.grad,
              conv2.lin.bias.grad,
              atol=1e-6,
          )
  
imports: import pytest
  import torch
  from torch_geometric.nn import CuGraphSAGEConv, SAGEConv
  from torch_geometric.testing import onlyCUDA, withPackage
label_desc: Use image node_id 1 for calling a global function with example usage: test_sage_conv_equality(aggr, bias, bipartite, max_num_neighbors, normalize, root_weight) without return types