code: def setup_seed(seed):
      torch.manual_seed(seed)
      torch.cuda.manual_seed_all(seed)
      torch.backends.cudnn.deterministic = True
      np.random.seed(seed)
      random.seed(seed)
  
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 2 for calling a global function with example usage: setup_seed(seed) without return types