code: def train_step_gen(
      self, training_batch: rlt.SlateQInput, batch_idx: int
  ):
      assert isinstance(
          training_batch, rlt.SlateQInput
      ), f"learning input is a {type(training_batch)}"
  
      reward = training_batch.reward
      reward_mask = training_batch.reward_mask
  
      discount_tensor = torch.full_like(reward, self.gamma)
  
      # Adjust the discount factor by the time_diff if the discount_time_scale is provided,
      # and the time_diff exists in the training_batch.
      if (
          self.discount_time_scale
          and training_batch.time_diff is not None
      ):
          discount_tensor = discount_tensor ** (
              training_batch.time_diff / self.discount_time_scale
          )
  
      next_action = (
          self._get_maxq_next_action(training_batch.next_state)
          if self.rl_parameters.maxq_learning
          else training_batch.next_action
      )
  
      terminal_mask = (
          training_batch.not_terminal.to(torch.bool) == False
      ).squeeze(1)
      next_action_docs = self._action_docs(
          training_batch.next_state,
          next_action,
          terminal_mask=terminal_mask,
      )
      next_q_values = torch.sum(
          self._get_unmasked_q_values(
              self.q_network_target,
              training_batch.next_state,
              next_action_docs,
          )
          * self._get_docs_value(next_action_docs),
          dim=1,
          keepdim=True,
      )
  
      # If not single selection, divide max-Q by the actual slate size.
      if not self.single_selection:
          next_q_values = next_q_values / self._get_avg_by_slate_size(
              training_batch
          )
  
      filtered_max_q_vals = (
          next_q_values * training_batch.not_terminal.float()
      )
      target_q_values = reward + (discount_tensor * filtered_max_q_vals)
      # Don't mask if not single selection
      if self.single_selection:
          target_q_values = target_q_values[reward_mask]
  
      # Get Q-value of action taken
      action_docs = self._action_docs(
          training_batch.state, training_batch.action
      )
      q_values = self._get_unmasked_q_values(
          self.q_network, training_batch.state, action_docs
      )
      if self.single_selection:
          q_values = q_values[reward_mask]
  
      all_action_scores = q_values.detach()
  
      value_loss = F.mse_loss(q_values, target_q_values)
      yield value_loss
  
      if not self.single_selection:
          all_action_scores = all_action_scores.sum(dim=1, keepdim=True)
  
      # Logging at the end to schedule all the cuda operations first
      self.reporter.log(
          td_loss=value_loss,
          model_values_on_logged_actions=all_action_scores,
      )
  
      # Use the soft update rule to update the target networks
      result = self.soft_update_result()
      self.log(
          "td_loss",
          value_loss,
          prog_bar=True,
          batch_size=training_batch.batch_size(),
      )
      yield result
  
imports: import enum
  import logging
  from typing import Optional
  import reagent.core.parameters
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.dataclasses import field
  from reagent.optimizer import Optimizer__Union, SoftUpdate
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.rl_trainer_pytorch import RLTrainerMixin
label_desc: Use image node_id 11 for calling the SlateQTrainer obj's underlying member method code with example usage: obj.train_step_gen(training_batch, batch_idx) without return types