code: def get_model(
      model_name,
      env,
      policy="MlpPolicy",
      policy_kwargs=None,
      model_kwargs=None,
      seed=None,
      verbose=1,
  ):
      if model_name not in MODELS:
          raise ValueError(
              f"Model '{model_name}' not found in MODELS."
          )  # this is more informative than NotImplementedError("NotImplementedError")
  
      if model_kwargs is None:
          temp_model_kwargs = MODEL_KWARGS[model_name]
      else:
          temp_model_kwargs = model_kwargs.copy()
  
      if "action_noise" in temp_model_kwargs:
          n_actions = env.action_space.shape[-1]
          temp_model_kwargs["action_noise"] = NOISE[
              temp_model_kwargs["action_noise"]
          ](mean=np.zeros(n_actions), sigma=0.1 * np.ones(n_actions))
      print(temp_model_kwargs)
      return MODELS[model_name](
          policy=policy,
          env=env,
          tensorboard_log=f"{config.TENSORBOARD_LOG_DIR}/{model_name}",
          verbose=verbose,
          policy_kwargs=policy_kwargs,
          seed=seed,
          **temp_model_kwargs,
      )
  
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 1 for calling the DRLEnsembleAgent obj's underlying member method code with example usage: obj.get_model(model_name, env, policy, policy_kwargs, model_kwargs, seed, verbose) and returns: MODELS