code: def seed_everything(seed: int) -> None:
      torch.manual_seed(seed)
      torch.cuda.manual_seed_all(seed)
      np.random.seed(seed)
      random.seed(seed)
      torch.backends.cudnn.deterministic = True
  
imports: from __future__ import annotations
  import argparse
  import os
  import random
  import sys
  import warnings
  from pathlib import Path
  from typing import cast, List, Optional
  import numpy
  import pandas
  import torch
  import yaml
  from qlib.backtest import Order
  from qlib.backtest.decision import OrderDir
  from qlib.constant import ONE_MIN
  from qlib.rl.data.native import load_handler_intraday_processed_data
  from qlib.rl.interpreter import ActionInterpreter, StateInterpreter
  from qlib.rl.order_execution import SingleAssetOrderExecutionSimple
  from qlib.rl.reward import Reward
  from qlib.rl.trainer import Checkpoint, backtest, train
  from qlib.rl.trainer.callbacks import Callback, EarlyStopping, MetricsWriter
  from qlib.rl.utils.log import CsvWriter
  from qlib.utils import init_instance_by_config
  from tianshou.policy import BasePolicy
  from torch.utils.data import Dataset
label_desc: Use image node_id 1 for calling a global function with example usage: seed_everything(seed) without return types