code: def test_boost_rewards(self):
      rewards = torch.ones(3, 1)
      actions = torch.tensor([[0, 1], [1, 0], [0, 1]])
      param_copy = DQNTrainerParameters(
          actions=["1", "2"],
          rl=RLParameters(reward_boost={"1": 1.0, "2": 2.0}),
      )
      trainer = self._construct_trainer(new_params=param_copy)
      boosted_reward = trainer.boost_rewards(rewards, actions)
      self.assertTrue(
          torch.equal(
              boosted_reward, torch.tensor([[3.0], [2.0], [3.0]])
          )
      )
  
imports: import unittest
  from typing import List, Optional, Tuple
  import torch
  from reagent.core.parameters import EvaluationParameters, RLParameters
  from reagent.core.torch_utils import masked_softmax
  from reagent.core.types import DiscreteDqnInput, ExtraData, FeatureData
  from reagent.evaluation.evaluation_data_page import EvaluationDataPage
  from reagent.evaluation.evaluator import get_metrics_to_score
  from reagent.models.dqn import FullyConnectedDQN
  from reagent.training.dqn_trainer_base import DQNTrainerBaseLightning
  from reagent.training.parameters import DQNTrainerParameters
  from reagent.workflow.types import RewardOptions
label_desc: Use image node_id 4 for calling the TestDQNTrainerBaseLightning obj's underlying member method code with example usage: obj.test_boost_rewards() without return types