code: def __init__(
      self,
      hidden=100,
      out_dim=128,
      layers=3,
      dropout=0.5,
      virtual_node=False,
      k=4,
      conv_type="gin",
      appnp_hop=5,
      alpha=0.2,
  ):
      super(ClassifierNetwork, self).__init__()
      self.k = k
      self.conv_type = conv_type
      convs = [
          ConvBlock(
              hidden,
              dropout=dropout,
              virtual_node=virtual_node,
              k=min(i + 1, k),
              conv_type=conv_type,
              edge_embedding=BondEncoder(emb_dim=hidden),
          )
          for i in range(layers - 1)
      ]
      convs.append(
          ConvBlock(
              hidden,
              dropout=dropout,
              virtual_node=virtual_node,
              virtual_node_agg=False,  # on last layer, use but do not update virtual node
              last_layer=True,
              k=min(layers, k),
              conv_type=conv_type,
              edge_embedding=BondEncoder(emb_dim=hidden),
          )
      )
  
      self.main = nn.Sequential(
          OGBMolEmbedding(
              hidden, embed_edge=False, x_as_list=(conv_type == "gin+")
          ),
          *convs,
      )
  
      self.aggregate = nn.Sequential(
          GlobalPool("mean"), nn.Linear(hidden, out_dim)
      )
  
      self.virtual_node = virtual_node
      if self.virtual_node:
          self.v0 = self.create_parameter(
              shape=[1, hidden],
              dtype="float32",
              default_initializer=nn.initializer.Constant(value=0.0),
          )
  
      self.appnp = gnn.APPNP(
          alpha=alpha, k_hop=appnp_hop, self_loop=True
      )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  import pgl
  import pgl.nn
  from pgl.nn import functional
  from mol_encoder import AtomEncoder, BondEncoder
label_desc: Use image node_id 1 to create a new ClassifierNetwork object from inherited base classes: nn with example: obj = ClassifierNetwork(hidden, out_dim, layers, dropout, virtual_node, k, conv_type, appnp_hop, alpha)