code: def __init__(
      self, g, in_feats, n_classes, n_hidden, n_layers, k, bias
  ):
      super(ChebNet, self).__init__()
      self.g = g
      self.layers = nn.ModuleList()
      self.layers.append(ChebConv(in_feats, n_hidden, k, bias=bias))
      for _ in range(n_layers - 1):
          self.layers.append(ChebConv(n_hidden, n_hidden, k, bias=bias))
  
      self.layers.append(ChebConv(n_hidden, n_classes, k, bias=bias))
  
imports: import torch
  import torch.nn
  from dgl.nn.pytorch import AGNNConv, APPNPConv, ChebConv, GATConv, GINConv, GraphConv, SAGEConv, SGConv, TAGConv
label_desc: Use image node_id 1 to create a new ChebNet object from inherited base classes: nn with example: obj = ChebNet(g, in_feats, n_classes, n_hidden, n_layers, k, bias)