code: def __init__(
      self,
      g,
      in_feats,
      n_classes,
      n_hidden,
      n_layers,
      init_eps,
      learn_eps,
  ):
      super(GIN, self).__init__()
      self.g = g
      self.layers = nn.ModuleList()
      self.layers.append(
          GINConv(
              nn.Sequential(
                  nn.Dropout(0.6),
                  nn.Linear(in_feats, n_hidden),
                  nn.ReLU(),
              ),
              "mean",
              init_eps,
              learn_eps,
          )
      )
      for i in range(n_layers - 1):
          self.layers.append(
              GINConv(
                  nn.Sequential(
                      nn.Dropout(0.6),
                      nn.Linear(n_hidden, n_hidden),
                      nn.ReLU(),
                  ),
                  "mean",
                  init_eps,
                  learn_eps,
              )
          )
      self.layers.append(
          GINConv(
              nn.Sequential(
                  nn.Dropout(0.6),
                  nn.Linear(n_hidden, n_classes),
              ),
              "mean",
              init_eps,
              learn_eps,
          )
      )
  
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 GIN object from inherited base classes: nn with example: obj = GIN(g, in_feats, n_classes, n_hidden, n_layers, init_eps, learn_eps)