code: def test_compute_td_loss(self):
      inp = DiscreteDqnInput(
          state=FeatureData(
              float_features=torch.rand(self.batch_size, self.state_dim)
          ),
          next_state=FeatureData(
              float_features=torch.rand(self.batch_size, self.state_dim)
          ),
          reward=torch.ones(self.batch_size, 1),
          time_diff=torch.ones(self.batch_size, 1) * 2,
          step=torch.ones(self.batch_size, 1) * 2,
          not_terminal=torch.ones(self.batch_size, 1),
          action=torch.tensor([[0, 1], [1, 0], [0, 1]]),
          next_action=torch.tensor([[1, 0], [0, 1], [1, 0]]),
          possible_actions_mask=torch.ones(
              self.batch_size, self.action_dim
          ),
          possible_next_actions_mask=torch.ones(
              self.batch_size, self.action_dim
          ),
          extras=ExtraData(),
      )
  
      # vanilla mse loss
      trainer = self._construct_trainer()
      discount_tensor = trainer.compute_discount_tensor(
          batch=inp, boosted_rewards=inp.reward
      )
      mse_backward_type = type(
          torch.nn.functional.mse_loss(
              torch.tensor([1.0, 0.0], requires_grad=True),
              torch.zeros(1),
          ).grad_fn
      )
      loss = trainer.compute_td_loss(
          batch=inp,
          boosted_rewards=inp.reward,
          discount_tensor=discount_tensor,
      )
      self.assertEqual(type(loss.grad_fn), mse_backward_type)
  
      # huber loss
      param_copy = DQNTrainerParameters(
          actions=["1", "2"], rl=RLParameters(q_network_loss="huber")
      )
      trainer = self._construct_trainer(new_params=param_copy)
      discount_tensor = trainer.compute_discount_tensor(
          batch=inp, boosted_rewards=inp.reward
      )
      smooth_l1_backward_type = type(
          torch.nn.functional.smooth_l1_loss(
              torch.tensor([1.0], requires_grad=True), torch.zeros(1)
          ).grad_fn
      )
      loss = trainer.compute_td_loss(
          batch=inp,
          boosted_rewards=inp.reward,
          discount_tensor=discount_tensor,
      )
      self.assertEqual(type(loss.grad_fn), smooth_l1_backward_type)
  
imports: import unittest
  import torch
  from reagent.core.parameters import EvaluationParameters, RLParameters
  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 import DQNTrainer
  from reagent.training.parameters import DQNTrainerParameters
  from reagent.workflow.types import RewardOptions
label_desc: Use image node_id 8 for calling the TestDQN obj's underlying member method code with example usage: obj.test_compute_td_loss() without return types