code: def shuffle_files(self, files, seed=None):
      if not seed:
          seed = random.randrange(100)
      random.seed(seed)
      random.shuffle(files)
      return files
  
imports: import copy
  import logging
  import torch.multiprocessing
  from contextlib import ExitStack
  from functools import partial
  import random
  from pathlib import Path
  from itertools import chain, groupby
  import numpy
  from sklearn.utils.class_weight import compute_class_weight
  from torch.utils.data import Dataset, Subset, IterableDataset
  from torch.utils.data.distributed import DistributedSampler
  from torch.utils.data.sampler import RandomSampler, SequentialSampler
  import torch
  from sklearn.model_selection import StratifiedKFold, KFold, ShuffleSplit, StratifiedShuffleSplit
  from tqdm import tqdm
  from farm.data_handler.dataloader import NamedDataLoader
  from farm.data_handler.processor import Processor, BertStyleLMProcessor
  from farm.data_handler.utils import grouper
  from farm.data_handler.dataset import ConcatTensorDataset
  from farm.modeling.tokenization import EmbeddingTokenizer
  from farm.utils import MLFlowLogger
  from farm.utils import log_ascii_workers, calc_chunksize
  from farm.utils import get_dict_checksum
  from farm.visual.ascii.images import TRACTOR_SMALL
label_desc: Use image node_id 5 for calling the _StreamingDataSet obj's underlying member method code with example usage: obj.shuffle_files(files, seed) and returns: files