code: def seed_torch(seed=42):
      seed = int(seed)
      random.seed(seed)
      os.environ["PYTHONHASHSEED"] = str(seed)
      np.random.seed(seed)
      torch.manual_seed(seed)
      torch.cuda.manual_seed(seed)
      torch.cuda.manual_seed_all(seed)
      torch.backends.cudnn.deterministic = True
      torch.backends.cudnn.benchmark = False
      torch.backends.cudnn.enabled = False
  
file_path: DeepKE/example/triple/mt5/run_finetune.py
imports: import logging
  import os
  import sys
  import numpy
  from datasets import Dataset
  import random
  import torch
  import json
  import transformers
  from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, HfArgumentParser, Seq2SeqTrainer, Seq2SeqTrainingArguments
  from transformers.trainer_utils import get_last_checkpoint, is_main_process
  from arguments import ModelArguments, DataTrainingArguments
  from utils import get_extract_metrics_f1
label_desc: Use image node_id 1 for calling a global function with example usage: seed_torch(seed) without return types