code: def process_pretraining_datasets_for_packing(
      train_dataset, sequence_len
  ):
      drop_long = partial(drop_long_seq, sequence_len=sequence_len)
  
      train_dataset = train_dataset.filter(drop_long)
      train_dataset = train_dataset.map(
          add_position_ids,
      )
      return train_dataset
  
imports: import math
  import os
  from contextlib import contextmanager
  from functools import partial
  from typing import List
  import numpy
  import torch
  import torch.cuda
  from accelerate.logging import get_logger
  from datasets import set_caching_enabled
  from torch.utils.data import DataLoader, RandomSampler
  from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFDPOTrainerBuilder
  from axolotl.utils.distributed import is_main_process, reduce_and_broadcast, zero_first
  from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
label_desc: Use image node_id 9 for calling a global function with example usage: process_pretraining_datasets_for_packing(train_dataset, sequence_len) and returns: train_dataset