code: def set_seed(seed=None):
      if seed is None:
          seed = random.randint(0, 5000)
  
      random.seed(seed)
      np.random.seed(seed)
      torch.manual_seed(seed)
      if torch.cuda.is_available():
          torch.cuda.manual_seed_all(seed)
          torch.backends.cudnn.deterministic = True
          torch.backends.cudnn.benchmark = False
  
imports: import time
  import torch
  import torch.nn.functional
  import numpy
  import random
  from torch_geometric.datasets import Planetoid
  from torch_geometric.nn import GCNConv, GATConv, SAGEConv
  import torch_geometric.transforms
  from torch_geometric.utils import train_test_split_edges
  from torch_geometric.utils import negative_sampling
  import os.path
  from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
  from tqdm import tqdm
  from sklearn.metrics import roc_auc_score
label_desc: Use image node_id 2 for calling a global function with example usage: set_seed(seed) without return types