code: def test_dense_graph_conv(idtype, g, norm_type, out_dim):
      g = g.astype(idtype).to(F.ctx())
      ctx = F.ctx()
      adj = g.adj_external(transpose=True, ctx=ctx).tostype("default")
      conv = nn.GraphConv(5, out_dim, norm=norm_type, bias=True)
      dense_conv = nn.DenseGraphConv(
          5, out_dim, norm=norm_type, bias=True
      )
      conv.initialize(ctx=ctx)
      dense_conv.initialize(ctx=ctx)
      dense_conv.weight.set_data(conv.weight.data())
      dense_conv.bias.set_data(conv.bias.data())
      feat = F.randn((g.number_of_src_nodes(), 5))
      out_conv = conv(g, feat)
      out_dense_conv = dense_conv(adj, feat)
      assert F.allclose(out_conv, out_dense_conv)
  
imports: import backend
  import dgl
  import dgl.function
  import dgl.nn.mxnet
  import mxnet
  import networkx
  import numpy
  import pytest
  import scipy
  from mxnet import autograd, gluon, nd
  from utils import parametrize_idtype
  from utils.graph_cases import get_cases, random_bipartite, random_dglgraph, random_graph
label_desc: Use image node_id 19 for calling a global function with example usage: test_dense_graph_conv(idtype, g, norm_type, out_dim) without return types