code: def __init__(self, dim, conv_type="gin"):
      super(VNAgg, self).__init__()
      self.conv_type = conv_type
      if "gin" in conv_type:
          self.mlp = nn.Sequential(
              MLP(dim, dim), nn.BatchNorm1D(dim), nn.ReLU()
          )
      elif "gcn" in conv_type:
          self.W0 = nn.Linear(dim, dim)
          self.W1 = nn.Linear(dim, dim)
          self.nl_bn = nn.Sequential(nn.BatchNorm1D(dim), nn.ReLU())
      else:
          raise NotImplementedError(
              "Unrecognised model conv : {}".format(conv_type)
          )
  
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 VNAgg object from inherited base classes: nn with example: obj = VNAgg(dim, conv_type)