class_docstr: Provide data silos for holdout evaluation.
  
  For this, we first combine the
  instances from all the sets or just some of the sets, then allow to create a different data silo
  instance with a train/test split.
  Calling DataSiloForHoldout.make() creates a list of holdout splits
class_docstr_tok: ["Provide","data","silos","for","holdout","evaluation",".","For","this",",","we","first","combine","the","instances","from","all","the","sets","or","just","some","of","the","sets",",","then","allow","to","create","a","different","data","silo","instance","with","a","train\/test","split",".","Calling","DataSiloForHoldout.make","(",")","creates","a","list","of","holdout","splits"]
code: def get_data_loader(self, which):
      return self.loaders[which]
  
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 2 for calling the DataSiloForHoldout obj's underlying member method code with example usage: obj.get_data_loader(which) and returns: self