code: def make_trainer_preprocessor(
      trainer: ReAgentLightningModule,
      device: torch.device,
      env: gym.Env,
      maker_map: Dict,
  ):
      assert isinstance(
          trainer, ReAgentLightningModule
      ), f"{type(trainer)}"
      sig = inspect.signature(trainer.train_step_gen)
      logger.info(
          f"Deriving trainer_preprocessor from {sig.parameters}"
      )
      # Assuming training_batch is in the first position (excluding self)
      assert (
          list(sig.parameters.keys())[0] == "training_batch"
      ), f"{sig.parameters} doesn't have training batch in first position."
      training_batch_type = sig.parameters["training_batch"].annotation
      assert training_batch_type != inspect.Parameter.empty
      try:
          maker = maker_map[training_batch_type].create_for_env(env)
      except KeyError:
          logger.error(f"Unknown type: {training_batch_type}")
          raise
  
      def trainer_preprocessor(batch):
          retval = maker(batch)
          return retval.to(device)
  
      return trainer_preprocessor
  
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 1 for calling a global function with example usage: make_trainer_preprocessor(trainer, device, env, maker_map) and returns: trainer_preprocessor, retval