code: def __call__(self, batch):
      n = batch.state.shape[0]
      item_mask = torch.ones(batch.doc.shape[:2])
      next_item_mask = torch.ones(batch.doc.shape[:2])
      # TODO: abs value to make probability?
      item_probability = batch.augmentation_value  # .unsqueeze(2)
      next_item_probability = (
          batch.next_augmentation_value
      )  # .unsqueeze(2)
  
      # concat null action
      null_action = torch.tensor(
          [batch.action.shape[1]] * n, dtype=torch.int64
      ).view(n, 1)
      action = torch.cat([batch.action, null_action], dim=1)
      next_action = torch.cat([batch.next_action, null_action], dim=1)
  
      # concat null reward to position wise reward
      position_reward = getattr(batch, f"response_{self.metric}")
      null_reward = torch.zeros((n, 1))
      position_reward = torch.cat([position_reward, null_reward], dim=1)
  
      # concat null mask when nothing clicked
      reward_mask = batch.response_click
      null_mask = (reward_mask.sum(dim=1) == 0).view(n, 1)
      reward_mask = torch.cat(
          [reward_mask.to(torch.bool), null_mask], dim=1
      )
      dict_batch = {
          "state_features": batch.state,
          "next_state_features": batch.next_state,
          "candidate_features": batch.doc,
          "next_candidate_features": batch.next_doc,
          "item_mask": item_mask,
          "next_item_mask": next_item_mask,
          "item_probability": item_probability,
          "next_item_probability": next_item_probability,
          "action": action,
          "next_action": next_action,
          "position_reward": position_reward,
          "reward_mask": reward_mask,
          "time_diff": None,
          "not_terminal": ~batch.terminal,
      }
      return rlt.SlateQInput.from_dict(dict_batch)
  
imports: import inspect
  import logging
  from typing import Dict, Optional
  import gym
  import numpy
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.parameters import CONTINUOUS_TRAINING_ACTION_RANGE
  from reagent.gym.types import Trajectory
  from reagent.preprocessing.types import InputColumn
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.utils import rescale_actions
label_desc: Use image node_id 3 for calling the SlateQInputMaker obj's underlying member method code with example usage: obj.__call__(batch) and returns: rlt