code: def _indexing_list_tuple_of_data(data, i, indexings=None):
      """Data is a list/tuple of data structures (e.g. list of numpy arrays).
  
      ``indexings`` are the indexing functions for each element of
      ``data``. If ``indexings`` are not given, the indexing functions
      for the individual structures have to be determined ad hoc, which
      is slower.
  
      """
      if not indexings:
          return [multi_indexing(x, i) for x in data]
      return [
          multi_indexing(x, i, indexing)
          for x, indexing in zip(data, indexings)
      ]
  
imports: from collections.abc import Mapping, Sequence
  from contextlib import contextmanager
  from enum import Enum
  from functools import partial
  import io
  from itertools import tee
  import pathlib
  import warnings
  import numpy
  from scipy import sparse
  import sklearn
  from sklearn.exceptions import NotFittedError
  from sklearn.utils import _safe_indexing
  from sklearn.utils.validation import check_is_fitted
  import torch
  from torch.nn import BCELoss
  from torch.nn import BCEWithLogitsLoss
  from torch.nn import CrossEntropyLoss
  from torch.nn.utils.rnn import PackedSequence
  from torch.utils.data.dataset import Subset
  from skorch.exceptions import DeviceWarning
  from skorch.exceptions import NotInitializedError
label_desc: Use image node_id 13 for calling a global function with example usage: _indexing_list_tuple_of_data(data, i, indexings) and returns: unknown, unknown