code: def make_replay_buffer_trainer_preprocessor(
      trainer: ReAgentLightningModule,
      device: torch.device,
      env: gym.Env,
  ):
      return make_trainer_preprocessor(
          trainer, device, env, REPLAY_BUFFER_MAKER_MAP
      )
  
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 a global function with example usage: make_replay_buffer_trainer_preprocessor(trainer, device, env) and returns: make_trainer_preprocessor