code: def __init__(
      self,
      g,
      in_feats,
      n_classes,
      n_hidden,
      n_layers,
      init_beta,
      learn_beta,
      dropout,
  ):
      super(AGNN, self).__init__()
      self.g = g
      self.layers = nn.ModuleList(
          [AGNNConv(init_beta, learn_beta) for _ in range(n_layers)]
      )
      self.proj = nn.Sequential(
          nn.Dropout(dropout), nn.Linear(in_feats, n_hidden), nn.ReLU()
      )
      self.cls = nn.Sequential(
          nn.Dropout(dropout), nn.Linear(n_hidden, n_classes)
      )
  
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 AGNN object from inherited base classes: nn with example: obj = AGNN(g, in_feats, n_classes, n_hidden, n_layers, init_beta, learn_beta, dropout)