code: def __init__(
      self,
      hidden_dim: _typ.Optional[int] = 64,
      layer_number: _typ.Optional[int] = 2,
      dropout: _typ.Optional[float] = 0.9,
      input_dim: _typ.Optional[int] = None,
      output_dim: _typ.Optional[int] = None,
      ops_type=0,
  ):
      super().__init__()
      self.layer_number = layer_number
      self.hidden_dim = hidden_dim
      self.input_dim = input_dim
      self.output_dim = output_dim
      self.dropout = dropout
      self.ops_type = ops_type
  
imports: import os
  import logging
  from autogl.backend import DependentBackend
  from autogl.datasets import build_dataset_from_name
  import torch
  from torch import nn
  import torch.nn.functional
  from autogl.module.nas.backend import bk_feat, bk_label
  from autogl.module.nas.algorithm import Darts, RL, GraphNasRL, Enas, RandomSearch, Spos
  from autogl.module.nas.estimator import BaseEstimator
  from autogl.module.train.evaluation import Acc
  import numpy
  from autogl.solver.utils import set_seed
  from autogl.module.nas.space import BaseSpace
  import typing
  from nas_bench_graph import light_read, gnn_list, gnn_list_proteins, Arch
  import pandas
  import argparse
  import os
  import os.path
label_desc: Use image node_id 1 to create a new BenchSpace object from inherited base classes: BaseSpace with example: obj = BenchSpace(hidden_dim, layer_number, dropout, input_dim, output_dim, ops_type)