code: def __call__(self, batch):
      not_terminal = 1.0 - batch.terminal.float()
      action, next_action = one_hot_actions(
          self.num_actions,
          batch.action,
          batch.next_action,
          batch.terminal,
      )
      if self.trainer_preprocessor is not None:
          state = self.trainer_preprocessor(batch.state)
          next_state = self.trainer_preprocessor(batch.next_state)
      else:
          state = rlt.FeatureData(float_features=batch.state)
          next_state = rlt.FeatureData(float_features=batch.next_state)
  
      try:
          possible_actions_mask = batch.possible_actions_mask.float()
      except AttributeError:
          possible_actions_mask = torch.ones_like(action).float()
  
      try:
          possible_next_actions_mask = (
              batch.next_possible_actions_mask.float()
          )
      except AttributeError:
          possible_next_actions_mask = torch.ones_like(
              next_action
          ).float()
  
      return rlt.DiscreteDqnInput(
          state=state,
          action=action,
          next_state=next_state,
          next_action=next_action,
          possible_actions_mask=possible_actions_mask,
          possible_next_actions_mask=possible_next_actions_mask,
          reward=batch.reward,
          not_terminal=not_terminal,
          step=None,
          time_diff=None,
          extras=rlt.ExtraData(
              mdp_id=None,
              sequence_number=None,
              action_probability=batch.log_prob.exp(),
              max_num_actions=None,
              metrics=None,
          ),
      )
  
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 DiscreteDqnInputMaker obj's underlying member method code with example usage: obj.__call__(batch) and returns: rlt