code: def test_init(self):
      trainer = self._construct_trainer()
  
      self.assertEqual(
          type(trainer.value_loss_fn),
          type(torch.nn.MSELoss(reduction="mean")),
      )
  
      with self.assertRaises(AssertionError):
          new_params = PPOTrainerParameters(ppo_epsilon=-1)
          self._construct_trainer(new_params)
  
      with self.assertRaises(AssertionError):
          new_params = PPOTrainerParameters(ppo_epsilon=2)
          self._construct_trainer(new_params)
  
      with self.assertRaises(AssertionError):
          params = PPOTrainerParameters(
              actions=["1", "2"], normalize=True
          )
          trainer = self._construct_trainer(new_params=params)
  
imports: import unittest
  from collections import defaultdict
  from unittest import mock
  import torch
  from reagent.core.types import PolicyGradientInput
  from reagent.evaluation.evaluator import get_metrics_to_score
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.samplers.discrete_sampler import SoftmaxActionSampler
  from reagent.models.dueling_q_network import DuelingQNetwork
  from reagent.models.fully_connected_network import FloatFeatureFullyConnected
  from reagent.training.parameters import PPOTrainerParameters
  from reagent.training.ppo_trainer import PPOTrainer
  from reagent.workflow.types import RewardOptions
label_desc: Use image node_id 3 for calling the TestPPO obj's underlying member method code with example usage: obj.test_init() without return types