code: def test_train_step_gen(self):
      # mock training batch
      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(),
      )
      mse_backward_type = type(
          torch.nn.functional.mse_loss(
              torch.tensor([1.0, 0.0], requires_grad=True),
              torch.zeros(1),
          ).grad_fn
      )
      add_backward_type = type(
          (
              torch.tensor([1.0], requires_grad=True)
              + torch.tensor([1.0], requires_grad=True)
          ).grad_fn
      )
  
      # vanilla DQN with CPE
      trainer = self._construct_trainer()
      loss_gen = trainer.train_step_gen(inp, batch_idx=1)
      losses = list(loss_gen)
      # four outputs of the train_step_gen method call:
      # td_loss, two CPE losses (reward_loss and metric_q_value_loss),
      # and soft_update_result
      self.assertEqual(len(losses), 4)
      self.assertEqual(type(losses[0].grad_fn), mse_backward_type)
      self.assertEqual(type(losses[1].grad_fn), mse_backward_type)
      self.assertEqual(type(losses[2].grad_fn), mse_backward_type)
      self.assertEqual(type(losses[3].grad_fn), add_backward_type)
  
      # no CPE
      trainer = self._construct_trainer(no_cpe=True)
      loss_gen = trainer.train_step_gen(inp, batch_idx=1)
      losses = list(loss_gen)
      # two outputs of the train_step_gen method with no CPE
      self.assertEqual(len(losses), 2)
  
      # seq_num
      param_copy = DQNTrainerParameters(
          actions=["1", "2"],
          rl=RLParameters(use_seq_num_diff_as_time_diff=True),
      )
      trainer = self._construct_trainer(new_params=param_copy)
      loss_gen = trainer.train_step_gen(inp, batch_idx=1)
      losses = list(loss_gen)
      self.assertEqual(len(losses), 4)
  
      # multi_steps
      param_copy = DQNTrainerParameters(
          actions=["1", "2"], rl=RLParameters(multi_steps=2)
      )
      trainer = self._construct_trainer(new_params=param_copy)
      loss_gen = trainer.train_step_gen(inp, batch_idx=1)
      losses = list(loss_gen)
      self.assertEqual(len(losses), 4)
  
      # non_max_q
      param_copy = DQNTrainerParameters(
          actions=["1", "2"], rl=RLParameters(maxq_learning=False)
      )
      trainer = self._construct_trainer(new_params=param_copy)
      loss_gen = trainer.train_step_gen(inp, batch_idx=1)
      losses = list(loss_gen)
      self.assertEqual(len(losses), 4)
  
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 4 for calling the TestDQN obj's underlying member method code with example usage: obj.test_train_step_gen() without return types