code: def test_train_step_gen(self):
      inp = DiscreteDqnInput(
          state=FeatureData(float_features=torch.rand(3, 10)),
          next_state=FeatureData(float_features=torch.rand(3, 10)),
          reward=torch.ones(3, 1),
          time_diff=torch.ones(3, 1) * 2,
          step=torch.ones(3, 1) * 2,
          not_terminal=torch.ones(
              3, 1
          ),  # todo: check terminal behavior
          action=torch.tensor([[0, 1], [1, 0], [0, 1]]),
          next_action=torch.tensor([[1, 0], [0, 1], [1, 0]]),
          possible_actions_mask=torch.ones(3, 2),
          possible_next_actions_mask=torch.ones(3, 2),
          extras=ExtraData(),
      )
      mse_backward_type = type(
          torch.nn.functional.mse_loss(
              torch.tensor([1.0], 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
      )
      mean_backward_type = type(
          torch.tensor([1.0, 2.0], requires_grad=True).mean().grad_fn
      )
  
      # vanilla
      trainer = self._construct_trainer()
      loss_gen = trainer.train_step_gen(inp, batch_idx=1)
      losses = list(loss_gen)
      self.assertEqual(len(losses), 4)
      self.assertEqual(type(losses[0].grad_fn), mean_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)
      self.assertEqual(len(losses), 2)
  
      # seq_num
      param_copy = QRDQNTrainerParameters(
          actions=["1", "2"],
          num_atoms=11,
          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 = QRDQNTrainerParameters(
          actions=["1", "2"],
          num_atoms=11,
          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 = QRDQNTrainerParameters(
          actions=["1", "2"],
          num_atoms=11,
          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)
  
file_path: ReAgent/reagent/test/training/test_qrdqn.py
imports: import unittest
  import torch
  from reagent.core.parameters import EvaluationParameters, RLParameters
  from reagent.core.types import DiscreteDqnInput, ExtraData, FeatureData
  from reagent.evaluation.evaluator import get_metrics_to_score
  from reagent.models.dqn import FullyConnectedDQN
  from reagent.training.parameters import QRDQNTrainerParameters
  from reagent.training.qrdqn_trainer import QRDQNTrainer
  from reagent.workflow.types import RewardOptions
label_desc: Use image node_id 4 for calling the TestQRDQN obj's underlying member method code with example usage: obj.test_train_step_gen() without return types