code: def test_train_step_gen(self):
      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
      )
      # vanilla
      trainer = self._construct_trainer()
      loss_gen = trainer.train_step_gen(self.inp, batch_idx=1)
      losses = list(loss_gen)
      self.assertEqual(len(losses), 6)
      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), add_backward_type)
      self.assertEqual(type(losses[3].grad_fn), mse_backward_type)
      self.assertEqual(type(losses[4].grad_fn), mse_backward_type)
      self.assertEqual(type(losses[5].grad_fn), add_backward_type)
  
      # no CPE
      trainer = self._construct_trainer(no_cpe=True)
      loss_gen = trainer.train_step_gen(self.inp, batch_idx=1)
      losses = list(loss_gen)
      self.assertEqual(len(losses), 4)
  
      # no q2 net
      trainer = self._construct_trainer(no_q2=True)
      loss_gen = trainer.train_step_gen(self.inp, batch_idx=1)
      losses = list(loss_gen)
      self.assertEqual(len(losses), 5)
  
      # use_target_actor
      params_copy = CRRTrainerParameters(
          actions=self.actions, use_target_actor=True
      )
      trainer = self._construct_trainer(new_params=params_copy)
      loss_gen = trainer.train_step_gen(self.inp, batch_idx=1)
      losses = list(loss_gen)
      self.assertEqual(len(losses), 6)
  
      # delayed policy update
      params_copy = CRRTrainerParameters(
          actions=self.actions, delayed_policy_update=2
      )
      trainer = self._construct_trainer(new_params=params_copy)
      loss_gen = trainer.train_step_gen(self.inp, batch_idx=1)
      losses = list(loss_gen)
      self.assertEqual(len(losses), 6)
      self.assertEqual(losses[2], None)
  
      # entropy
      params_copy = CRRTrainerParameters(
          actions=self.actions, entropy_coeff=1.0
      )
      trainer = self._construct_trainer(new_params=params_copy)
      loss_gen = trainer.train_step_gen(self.inp, batch_idx=1)
      losses = list(loss_gen)
      self.assertEqual(len(losses), 6)
  
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.actor import FullyConnectedActor
  from reagent.models.dqn import FullyConnectedDQN
  from reagent.training.discrete_crr_trainer import DiscreteCRRTrainer
  from reagent.training.parameters import CRRTrainerParameters
  from reagent.workflow.types import RewardOptions
label_desc: Use image node_id 5 for calling the TestCRR obj's underlying member method code with example usage: obj.test_train_step_gen() without return types