code: def __init__(
      self,
      in_dim,
      num_classes,
      num_layers,
      hidden,
      dropout=0.5,
      pool_type=PoolType.MEAN,
  ):
      super(GIN, self).__init__()
      num_features = in_dim
      self.conv1 = GINConv(
          Sequential(
              Linear(num_features, hidden),
              ReLU(),
              Linear(hidden, hidden),
              ReLU(),
              BN(hidden),
          ),
          train_eps=True,
      )
      self.convs = torch.nn.ModuleList()
      for i in range(num_layers - 1):
          self.convs.append(
              GINConv(
                  Sequential(
                      Linear(hidden, hidden),
                      ReLU(),
                      Linear(hidden, hidden),
                      ReLU(),
                      BN(hidden),
                  ),
                  train_eps=True,
              )
          )
      self.lin1 = Linear(hidden, hidden)
      self.lin2 = Linear(hidden, num_classes)
      self.dropout = dropout
  
      if pool_type == PoolType.MEAN:
          self.pool = global_mean_pool
      elif pool_type == PoolType.SUM:
          self.pool = global_add_pool
      elif pool_type == PoolType.MAX:
          self.pool = global_max_pool
  
file_path: DIG/dig/auggraph/method/GraphAug/model/classifier.py
imports: import torch
  import torch.nn.functional
  from torch.nn import Linear, Sequential, ReLU, BatchNorm1d
  from torch_geometric.nn import GINConv, GCNConv, global_mean_pool, JumpingKnowledge, global_max_pool, global_add_pool
  from dig.auggraph.method.GraphAug.constants import *
label_desc: Use image node_id 1 to create a new GIN object from inherited base classes: torch.nn with example: obj = GIN(in_dim, num_classes, num_layers, hidden, dropout, pool_type)