code: def test_dense_cheb_conv(out_dim):
      for k in range(3, 4):
          ctx = F.ctx()
          g = dgl.DGLGraph(
              sp.sparse.random(100, 100, density=0.1, random_state=42)
          )
          g = g.to(ctx)
  
          adj = tf.sparse.to_dense(
              tf.sparse.reorder(g.adj_external(transpose=True, ctx=ctx))
          )
          cheb = nn.ChebConv(5, out_dim, k, None, bias=True)
          dense_cheb = nn.DenseChebConv(5, out_dim, k, bias=True)
  
          # init cheb modules
          feat = F.ones((100, 5))
          out_cheb = cheb(g, feat, [2.0])
  
          dense_cheb.W = tf.reshape(
              cheb.linear.weights[0], (k, 5, out_dim)
          )
          if cheb.linear.bias is not None:
              dense_cheb.bias = cheb.linear.bias
  
          out_dense_cheb = dense_cheb(adj, feat, 2.0)
          print(out_cheb - out_dense_cheb)
          assert F.allclose(out_cheb, out_dense_cheb)
  
imports: from copy import deepcopy
  import backend
  import dgl
  import dgl.function
  import dgl.nn.tensorflow
  import networkx
  import numpy
  import pytest
  import scipy
  import tensorflow
  from tensorflow.keras import layers
  from utils import parametrize_idtype
  from utils.graph_cases import get_cases, random_bipartite, random_dglgraph, random_graph
label_desc: Use image node_id 21 for calling a global function with example usage: test_dense_cheb_conv(out_dim) without return types