class_docstr: Implementations for DRL algorithms
  
  Attributes
  ----------
      env: gym environment class
          user-defined class
      price_array: numpy array
          OHLC data
      tech_array: numpy array
          techical data
      turbulence_array: numpy array
          turbulence/risk data
  Methods
  -------
      get_model()
          setup DRL algorithms
      train_model()
          train DRL algorithms in a train dataset
          and output the trained model
      DRL_prediction()
          make a prediction in a test dataset and get results
class_docstr_tok: ["Implementations","for","DRL","algorithms","Attributes","--","--","--","--","--","env",":","gym","environment","class","user-defined","class","price_array",":","numpy","array","OHLC","data","tech_array",":","numpy","array","techical","data","turbulence_array",":","numpy","array","turbulence\/risk","data","Methods","--","--","--","-","get_model","(",")","setup","DRL","algorithms","train_model","(",")","train","DRL","algorithms","in","a","train","dataset","and","output","the","trained","model","DRL_prediction","(",")","make","a","prediction","in","a","test","dataset","and","get","results"]
code: def get_model(
      self,
      model_name,
      # policy="MlpPolicy",
      # policy_kwargs=None,
      # model_kwargs=None,
  ):
      if model_name not in MODELS:
          raise NotImplementedError("NotImplementedError")
  
      # if model_kwargs is None:
      #    model_kwargs = MODEL_KWARGS[model_name]
  
      model = MODELS[model_name]
      # get algorithm default configration based on algorithm in RLlib
      if model_name == "a2c":
          model_config = model.A2C_DEFAULT_CONFIG.copy()
      elif model_name == "td3":
          model_config = model.TD3_DEFAULT_CONFIG.copy()
      else:
          model_config = model.DEFAULT_CONFIG.copy()
      # pass env, log_level, price_array, tech_array, and turbulence_array to config
      model_config["env"] = self.env
      model_config["log_level"] = "WARN"
      model_config["env_config"] = {
          "price_array": self.price_array,
          "tech_array": self.tech_array,
          "turbulence_array": self.turbulence_array,
          "if_train": True,
      }
  
      return model, model_config
  
file_path: FinRL/finrl/agents/rllib/models.py
imports: from __future__ import annotations
  import ray
  from ray.rllib.algorithms.a2c import a2c
  from ray.rllib.algorithms.ddpg import ddpg
  from ray.rllib.algorithms.ppo import ppo
  from ray.rllib.algorithms.sac import sac
  from ray.rllib.algorithms.td3 import td3
label_desc: Use image node_id 2 for calling the DRLAgent obj's underlying member method code with example usage: obj.get_model(model_name) and returns: model,  model_config