docstr: make a prediction based on trained model
docstr_tok: ["make","a","prediction","based","on","trained","model"]
code: def DRL_prediction(
      self,
      model,
      name,
      last_state,
      iter_num,
      turbulence_threshold,
      initial,
  ):
      """make a prediction based on trained model"""
  
      # trading env
      trade_data = data_split(
          self.df,
          start=self.unique_trade_date[
              iter_num - self.rebalance_window
          ],
          end=self.unique_trade_date[iter_num],
      )
      trade_env = DummyVecEnv(
          [
              lambda: StockTradingEnv(
                  df=trade_data,
                  stock_dim=self.stock_dim,
                  hmax=self.hmax,
                  initial_amount=self.initial_amount,
                  num_stock_shares=[0] * self.stock_dim,
                  buy_cost_pct=[self.buy_cost_pct] * self.stock_dim,
                  sell_cost_pct=[self.sell_cost_pct] * self.stock_dim,
                  reward_scaling=self.reward_scaling,
                  state_space=self.state_space,
                  action_space=self.action_space,
                  tech_indicator_list=self.tech_indicator_list,
                  turbulence_threshold=turbulence_threshold,
                  initial=initial,
                  previous_state=last_state,
                  model_name=name,
                  mode="trade",
                  iteration=iter_num,
                  print_verbosity=self.print_verbosity,
              )
          ]
      )
  
      trade_obs = trade_env.reset()
  
      for i in range(len(trade_data.index.unique())):
          action, _states = model.predict(trade_obs)
          trade_obs, rewards, dones, info = trade_env.step(action)
          if i == (len(trade_data.index.unique()) - 2):
              # print(env_test.render())
              last_state = trade_env.envs[0].render()
  
      df_last_state = pd.DataFrame({"last_state": last_state})
      df_last_state.to_csv(
          f"results/last_state_{name}_{i}.csv", index=False
      )
      return last_state
  
imports: from __future__ import annotations
  import time
  import numpy
  import pandas
  from stable_baselines3 import A2C
  from stable_baselines3 import DDPG
  from stable_baselines3 import PPO
  from stable_baselines3 import SAC
  from stable_baselines3 import TD3
  from stable_baselines3.common.callbacks import BaseCallback
  from stable_baselines3.common.noise import NormalActionNoise
  from stable_baselines3.common.noise import OrnsteinUhlenbeckActionNoise
  from stable_baselines3.common.vec_env import DummyVecEnv
  from finrl import config
  from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv
  from finrl.meta.preprocessor.preprocessors import data_split
label_desc: Use image node_id 6 for calling the DRLEnsembleAgent obj's underlying member method code with example usage: obj.DRL_prediction(model, name, last_state, iter_num, turbulence_threshold, initial) and returns: last_state