code: def __init__(self, encoder, decoder=None):
      super().__init__()
      self.encoder = encoder
      self.decoder = decoder
      if self.decoder is None:
          self.decoder = DotProductLinkPredictionDecoderMaintainer()
          self.decoder.initialize()
          self.decoder = self.decoder.decoder
  
imports: import os
  import time
  import torch
  import torch.nn.functional
  import numpy
  import os.path
  import random
  from torch_geometric.datasets import Planetoid
  from torch_geometric.data import Data
  from autogl.module.model.encoders import GCNEncoderMaintainer, GATEncoderMaintainer, SAGEEncoderMaintainer
  from autogl.module.model.decoders import DotProductLinkPredictionDecoderMaintainer
  import torch_geometric.transforms
  from torch_geometric.utils import train_test_split_edges
  from torch_geometric.utils import negative_sampling
  from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
  from tqdm import tqdm
  from helper import get_encoder_decoder_hp
  from sklearn.metrics import roc_auc_score
label_desc: Use image node_id 1 to create a new DummyModel object from inherited base classes: torch.nn with example: obj = DummyModel(encoder, decoder)