code: def test_dense_cheb_conv(out_dim):
      for k in range(1, 4):
          ctx = F.ctx()
          g = dgl.from_scipy(
              sp.sparse.random(100, 100, density=0.3)
          ).to(F.ctx())
          adj = g.adj_external(transpose=True, ctx=ctx).tostype(
              "default"
          )
          cheb = nn.ChebConv(5, out_dim, k)
          dense_cheb = nn.DenseChebConv(5, out_dim, k)
          cheb.initialize(ctx=ctx)
          dense_cheb.initialize(ctx=ctx)
  
          for i in range(len(cheb.fc)):
              dense_cheb.fc[i].weight.set_data(cheb.fc[i].weight.data())
              if cheb.bias is not None:
                  dense_cheb.bias.set_data(cheb.bias.data())
  
          feat = F.randn((100, 5))
          out_cheb = cheb(g, feat, [2.0])
          out_dense_cheb = dense_cheb(adj, feat, 2.0)
          assert F.allclose(out_cheb, out_dense_cheb)
  
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 18 for calling a global function with example usage: test_dense_cheb_conv(out_dim) without return types