code: def set_params(
      self,
      discrete,
      episodic,
      lambd,
      entropy_coef,
      standardize_advantages,
      standardize_obs,
  ):
      self.discrete = discrete
      self.episodic = episodic
      self.lambd = lambd
      self.entropy_coef = entropy_coef
      self.standardize_advantages = standardize_advantages
      self.standardize_obs = standardize_obs
      self.recurrent = True
  
imports: import os
  import tempfile
  import unittest
  import numpy
  import pytest
  import torch
  from torch import nn
  import pfrl
  from pfrl.agents import trpo
  from pfrl.envs.abc import ABC
  from pfrl.experiments import train_agent_batch_with_evaluation, train_agent_with_evaluation
  from pfrl.experiments.evaluator import batch_run_evaluation_episodes, run_evaluation_episodes
  from pfrl.nn import RecurrentSequential
  from pfrl.policies import GaussianHeadWithStateIndependentCovariance, SoftmaxCategoricalHead
  from pfrl.testing import torch_assert_allclose
label_desc: Use image node_id 1 for calling the TestTRPORecurrent obj's underlying member method code with example usage: obj.set_params(discrete, episodic, lambd, entropy_coef, standardize_advantages, standardize_obs) without return types