code: def collate_and_preprocess(
      batch_preprocessor: BatchPreprocessor, use_gpu: bool
  ):
      """Helper for Petastorm's DataLoader to preprocess.
      TODO(kaiwenw): parallelize preprocessing by using transform of Petastorm reader
      Should pin memory and preprocess in reader and convert to gpu in collate_fn.
      """
  
      def collate_fn(batch_list: List[Dict]):
          batch = decimal_friendly_collate(batch_list)
          preprocessed_batch = batch_preprocessor(batch)
          if use_gpu:
              preprocessed_batch = preprocessed_batch.cuda()
          return preprocessed_batch
  
      return collate_fn
  
imports: import logging
  from typing import Dict, List, Optional
  import pytorch_lightning
  import torch
  from petastorm import make_batch_reader
  from petastorm.pytorch import DataLoader, decimal_friendly_collate
  from reagent.core.oss_tensorboard_logger import OssTensorboardLogger
  from reagent.data.spark_utils import get_spark_session
  from reagent.preprocessing.batch_preprocessor import BatchPreprocessor
  from reagent.training import StoppingEpochCallback
  from reagent.training.reagent_lightning_module import has_test_step_override
  from .types import Dataset, ReaderOptions, ResourceOptions
label_desc: Use image node_id 2 for calling a global function with example usage: collate_and_preprocess(batch_preprocessor, use_gpu) and returns: collate_fn, preprocessed_batch