code: def __call__(self, batch):
      not_terminal = 1.0 - batch.terminal.float()
      assert (
          len(batch.state.shape) == 2
      ), f"{batch.state.shape} is not (batch_size, state_dim)."
      batch_size, _ = batch.state.shape
      action, next_action = one_hot_actions(
          self.num_actions,
          batch.action,
          batch.next_action,
          batch.terminal,
      )
      possible_actions = get_possible_actions_for_gym(
          batch_size, self.num_actions
      )
      possible_next_actions = possible_actions.clone()
      possible_actions_mask = torch.ones((batch_size, self.num_actions))
      possible_next_actions_mask = possible_actions_mask.clone()
      return rlt.ParametricDqnInput(
          state=rlt.FeatureData(float_features=batch.state),
          action=rlt.FeatureData(float_features=action),
          next_state=rlt.FeatureData(float_features=batch.next_state),
          next_action=rlt.FeatureData(float_features=next_action),
          possible_actions=possible_actions,
          possible_actions_mask=possible_actions_mask,
          possible_next_actions=possible_next_actions,
          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 ParametricDqnInputMaker obj's underlying member method code with example usage: obj.__call__(batch) and returns: rlt