code: def __init__(self, graphs, labels):
      for g in graphs:
          g.ndata["feat"] = g.ndata["attr"]
      self.graphs, self.labels = [], []
      for g, l in zip(graphs, labels):
          self.graphs.append(g)
          self.labels.append(l)
      self.gclasses = max(self.labels).item() + 1
      self.graph = self.graphs
  
file_path: AutoGL/test/performance/graph_classification/dgl/trainer.py
imports: import os
  import torch
  import random
  import numpy
  from dgl.data import GINDataset
  from dgl.dataloading import GraphDataLoader
  from autogl.datasets import utils
  from autogl.module.train import GraphClassificationFullTrainer
  from autogl.solver.utils import set_seed
  from helper import get_encoder_decoder_hp
  import logging
label_desc: Use image node_id 1 to create a new DatasetAbstraction object  with example: obj = DatasetAbstraction(graphs, labels)