code: def __init__(
      self,
      in_dim,
      num_layers,
      hidden,
      conv_type=ConvType.GEMB,
      pool_type=PoolType.SUM,
      use_gate=True,
      node_update_type=NodeUpdateType.RESIDUAL,
      layer_norm=False,
  ):
      super(GENet, self).__init__()
  
      self.embedding = nn.Sequential(nn.Linear(in_dim, hidden))
  
      self.convs = torch.nn.ModuleList()
      for _ in range(num_layers):
          if conv_type == ConvType.GEMB:
              self.convs.append(
                  GEMBConv(
                      hidden,
                      [hidden * 2, hidden * 2],
                      [hidden * 2],
                      node_update_type,
                      layer_norm,
                  )
              )
          elif conv_type == ConvType.GIN:
              self.convs.append(
                  GINConv(
                      nn.Sequential(
                          nn.Linear(hidden, hidden),
                          nn.ReLU(),
                          nn.Linear(hidden, hidden),
                          nn.ReLU(),
                          nn.BN(hidden),
                      ),
                      train_eps=True,
                  )
              )
  
      self.readout = Readout(
          hidden, [hidden], [hidden], use_gate, pool_type
      )
  
imports: import torch
  import torch.nn
  from torch_scatter import scatter
  from torch_geometric.nn import global_add_pool, global_mean_pool, global_max_pool, GINConv
  from dig.auggraph.method.GraphAug.constants import *
label_desc: Use image node_id 1 to create a new GENet object from inherited base classes: nn with example: obj = GENet(in_dim, num_layers, hidden, conv_type, pool_type, use_gate, node_update_type, layer_norm)