code: def train_model(
      model, model_name, tb_log_name, iter_num, total_timesteps=5000
  ):
      model = model.learn(
          total_timesteps=total_timesteps,
          tb_log_name=tb_log_name,
          callback=TensorboardCallback(),
      )
      model.save(
          f"{config.TRAINED_MODEL_DIR}/{model_name.upper()}_{total_timesteps // 1000}k_{iter_num}"
      )
      return model
  
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 2 for calling the DRLEnsembleAgent obj's underlying member method code with example usage: obj.train_model(model, model_name, tb_log_name, iter_num, total_timesteps) and returns: model