code: def forward(
      self, batch: Dict[str, torch.Tensor]
  ) -> rlt.ParametricDqnInput:
      batch = batch_to_device(batch, self.device)
      # first preprocess state and action
      preprocessed_state = self.state_preprocessor(
          batch["state_features"], batch["state_features_presence"]
      )
      preprocessed_next_state = self.state_preprocessor(
          batch["next_state_features"],
          batch["next_state_features_presence"],
      )
      preprocessed_action = self.action_preprocessor(
          batch["action"], batch["action_presence"]
      )
      preprocessed_next_action = self.action_preprocessor(
          batch["next_action"], batch["next_action_presence"]
      )
      return rlt.ParametricDqnInput(
          state=rlt.FeatureData(preprocessed_state),
          next_state=rlt.FeatureData(preprocessed_next_state),
          action=rlt.FeatureData(preprocessed_action),
          next_action=rlt.FeatureData(preprocessed_next_action),
          reward=batch["reward"].unsqueeze(1),
          time_diff=batch["time_diff"].unsqueeze(1),
          step=batch["step"].unsqueeze(1),
          not_terminal=batch["not_terminal"].unsqueeze(1),
          possible_actions=batch["possible_actions"],
          possible_actions_mask=batch["possible_actions_mask"],
          possible_next_actions=batch["possible_next_actions"],
          possible_next_actions_mask=batch[
              "possible_next_actions_mask"
          ],
          extras=rlt.ExtraData(
              mdp_id=batch["mdp_id"].unsqueeze(1),
              sequence_number=batch["sequence_number"].unsqueeze(1),
              action_probability=batch["action_probability"].unsqueeze(
                  1
              ),
          ),
      )
  
imports: from typing import Dict
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.core import types
  from reagent.preprocessing.preprocessor import Preprocessor
label_desc: Use image node_id 2 for calling the ParametricDqnBatchPreprocessor obj's underlying member method code with example usage: obj.forward(batch) and returns: rlt